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Analysis of Factors Influencing the Salary of Female Employees: A Regression Approach

A statistical study applying regression analysis to examine salary determinants for female employees.

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    Salary of female employees Page 1 of 11 Analysis of Factors Influencing the Salary of Female Employees: A Regression Approach Memorandum To: From: Data Analysts Date: Mar 9 , 20 15 Subject: Checking the dependence of Salary of female employees on the other variables such as job grade , education level etc. Using the provided regression analysis results, discuss how various factors (such as age, education level, years of experience, and others) influence the salary of female employees. How does the inclusion of different explanatory variables impact the gender wage gap, and what does this imply for addressing salary disparities in the workplace? Provide a detailed interpretation of the results and suggest potential strategies for improving pay equity based on the findings. Word count requirement: 750 - 1000 words.

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    Salary of female employees Page 2 of 11 ****************************************************************************** Purpose This memorandum provides an analysis of dependence of salary on other factors such as the gender of employee, education level, job grade, age, experience level and many other related variables . Specifically, we examined the relationship between salary and gender so as to analyze the how the salary of female employees changes if other said facto rs are taken into consideration. Background The gender pay gap (also known as gender wage gap, male – female income difference, gender gap in earnings, gender earnings gap, gender income difference) is the difference between male and female earnings expressed as a percentage of male earnings, according to the OECD . The European Commission defines it as the average difference between men’s and women’s hourly earnings. It is generally suggested that the wage gap is due to a variety of causes, such as discrimination in hiring, d ifferences in education choices, discrimination in salary negotiations, differences in the types of positions held by men and women, differences in the pay of jobs men typically go into as opposed to women (especially highly paid high risk jobs), and differences in amount of work experience, and breaks in employment. Analysis Objectives Our study sought to determine the following: (1) The f actor s or combination of characteristics that influences the salary structure of a female employee.

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    Salary of female employees Page 3 of 11 (2) How addition of one more factor changes the dependence of salary on female factor. That is, how the size of the coefficient for the Female variable changes as we add additional explanatory variables. Methodology The Microsoft Excel 2007 Data Analysis Regression function was used to genera te multiple regressions upon various combinations of variables and to determine for each regression the coefficient of determination , R 2 , to determine the degree to which variation in salary of female employees can be explained by the variables selected. The adjusted R 2 for each regression was also monitored to determine whether additional variables may have been superfluous. Reliability of the coefficients was assessed by examining statistics such as p - values. Variables for which data was available included the following: Variables Employed in Analysis Explanatory Variable Units Abbr eviation Female* 1 or 0 female A ge Years Age Years prior years yrsprior Years experiance Years Yrsexper Education level 1 ,2 or 3 E duclev Education level =i† 1,2 or 3 Index 1,2,3 * Female variable with value 1 means female and 0 means male. † Education level with higher value indicating more education. Results The analysis began with a review of scatter plots of Salary v. variables such as Age, female. While female alone (Figure 1) produced a low coefficient of determination (R 2 less than 13%), Age(Figure 2) produced a slightly higher, though still weak explanatory relationship (17%) to Salary.

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