The glass ceiling a study on annual salaries
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The Glass Ceiling: A Study on Annual Salaries. Group 4 Julie Shan, Brian Abe, Yu-Ting Cheng, Kathinka Tysnes, Huan Zhang, Andrew Booth. Agenda. Introduction Exploratory Analysis Linear Regression & Analysis Conclusion Further Analysis. Introduction. What?

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The glass ceiling a study on annual salaries

The Glass Ceiling: A Study on Annual Salaries

Group 4

Julie Shan, Brian Abe, Yu-Ting Cheng, Kathinka Tysnes, Huan Zhang, Andrew Booth


Agenda

Agenda

  • Introduction

  • Exploratory Analysis

  • Linear Regression & Analysis

  • Conclusion

  • Further Analysis


Introduction

Introduction

  • What?

    • A sample of 1980’s managers salaries

  • Why?

    • To determine factors that affect the salary

  • How?

    • Linear regression


Introduction1

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


Exploratory analysis

Exploratory Analysis


Exploratory analysis1

Exploratory Analysis

  • Affects of the independent variables on the dependent variable SALARY.

  • Independent Variables:

    • Years in job

    • Management level

    • Education level

    • Gender


Exploratory analysis2

Exploratory Analysis

  • Positive Relationship Between Years in Job and Salary


Exploratory analysis3

Exploratory Analysis

  • Upper Management Earns More Than Lower Management


Exploratory analysis4

Exploratory Analysis

  • More Educated Managers Earn More

  • Outliers May Skew Regression Results


Exploratory analysis5

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


Exploratory analysis6

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


Linear regression analysis

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


Linear regression analysis1

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.


Linear regression analysis2

Linear Regression & Analysis

  • Jarque-Bera statistic is greater than 0.05, indicating normality of the residuals


Linear regression analysis3

Linear Regression & Analysis

  • Updated regression excluding variable FEMALE.


Linear regression analysis4

Linear Regression & Analysis

  • R2 = 0.93: still a good fit.

  • The Durbin-Watson statistic is once again less than 2 but greater than 1


Linear regression analysis5

Linear Regression & Analysis

  • Jarque-Bera statistic is greater than 0.05, indicating normality of the residuals


Linear regression analysis6

Linear Regression & Analysis

  • Wald Test for equivalency of intercepts for various education levels

Ho : ED2=ED3

Ho : ED1=ED2


Linear regression analysis7

Linear Regression & Analysis

  • Final Model:

    SALARY = 615.0378*YEARS + 7509.9807*MANAGEMENT +

    7352.3861*ED1 +

    10907.4441*ED23


Linear regression analysis8

Linear Regression & Analysis


Conclusion

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.


Further analysis

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)


The glass ceiling a study on annual salaries

Fin

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