Causations of Income

Jarred D. RiccadonnaMBA 506 Project

Dr. Hajiran

Spring, 2014

Table of Contents

Executive Summary...................................................................................................................................3

Introduction................................................................................................................................................4

Regression Analysis and Information........................................................................................................4

Equation:....................................................................................................................................................4

Interpretation of the Data...........................................................................................................................5

Forecasting / Predicting.............................................................................................................................5

Supporting Studies.....................................................................................................................................6

Conclusion.................................................................................................................................................7

Works Cited...............................................................................................................................................8

Executive Summary

Problem:

Which independent variables influence income (the dependent variable) the most? The

independent variables are education, length of employment, and gender.

Results:

The independent variable I anticipated to play the most statistically relevant factor in

determining income was education. I did find that to be true. Out of the three independent variables,

education seemed to play the biggest factor of all the three in relation to the regression analysis ran.

Gender played the least significant impact, which shows that there is little to no discrimination toward

salary.

Equation:

Y = 0.65X1 + 0.23X2 + -0.20X3, upon multiple regression ran

T-values: (5.77) (3.12) (-0.81)

Where:

Y = Annual Salary

X1 = Education

X2 = Length of Employment

X3 = (Gender)

Regression Ran R2 SeData: 0.56 0.75

Observations: 40 Type: Time Series

Benefits:

By reading and understanding the relevance behind the statistics posted and study ran, one will

be able to conclude the importance behind a higher income and what employers are typically looking

for in determining salary. Everybody always preaches and describes education and experience;

however, being able to actually infer data and have the empirical evidence shown to back up these

hypotheses will enable individuals to see which independent variables yield the best income in the

workforce.

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Jarred D. RiccadonnaMBA 506 Project

Dr. Hajiran

Introduction

The purpose of this research is to find a direct cause and effect regarding income in the form of

a regression analysis. Data will be obtained by using a random number generator in Excel and having

the regression ran on 40 observations to interpret the output the regression shows. The dependent

variable is income (herein stated as “dependent variable”). The remaining data obtained (herein stated

as “independent variables”) will help determine the biggest influence for a higher level of income. By

doing so, the reader will be able to deduce which independent variable(s) play the largest role in the

level of annual income acquired. The data in this study assumes Ceteris Paribus, accurate data, and

contains no regression analysis pitfalls.

Regression Analysis and Information

Each independent variable would have a positive influence on income. Because of this, the

equation derived from the independent variables are added respectively within the equation and hold

the value based on the multiple regression analysis ran using the data from the Excel spreadsheet

(attached hereto as Annex A).

Equation:

X = 0.65X1 + 0.23X2 + -0.20X3, upon multiple regression ran

T-values: (5.77) (3.12) (-0.81)

Where:

X = Annual Salary

X1 = Education

X2 = Length of Employment

X3 = (Gender)

Regression Ran R2 SeData: 0.56 0.75

Observations: 40 Type: Time Series

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Jarred D. RiccadonnaMBA 506 Project

Dr. Hajiran

Interpretation of the Data

By running the multiple regression, I came to find positive findings within the data relating to

the coefficient of determination (R2), t-values, and the standard error of the estimate (SE). The

coefficient of determination explains the percentage of variation in the dependent variable (income) by

the variation in the independent variables. After running the multiple regression, the coefficient of

determination showed a 0.56. This means 56% of the independent variables explain the effect on

income, and the remaining 44% is explained by other factors not listed in the analysis.

Next, t-values are a determining factor as to whether or not a variable is statistically significant

to the dependent variable. In order for a variable to be considered statistically significant, the t-value

must be a two or higher. Income had a t-value of 2.34, education 5.77, length of employment 2.95, and

gender -0.91. As predicted, education appears to be the most significant factor relating to income. On

the contrary, gender has a negative t-value, which in this case would be classified as a good thing

because the statistical insignificance shows that income-based gender discrimination is not happening

in this study.

The standard error of the estimate (SE) shows the deviation around the best linear unbiased

estimate line that best fits. In other words, the deviation shown helps find the best linear unbiased

estimate equation to be graphed around the standard error estimate. In this analysis, the SE is 0.75,

which tells the deviation in the data resulting from the multiple regression ran.

Forecasting / Predicting

I forecasted education would continue to be the largest influence affecting income the most for

my next two data points regarding the study. The two points did not directly show this. Instead, the

numbers had shown that length of employment played the greatest factor out of the independent

variables. Two other independent variables that may have made this study more precise and make the

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Jarred D. RiccadonnaMBA 506 Project

Dr. Hajiran

coefficient of variation closer to one could be age range and work experience in the industry.

Supporting Studies

I collected information from other studies relating to income and my independent variables and

the research shows both similar and different results to my hypothesis. Reference for this data is under

the works cited page of the materials.

According to a study from the United States government, education had been set up as a range

between “less than high school” up to “doctoral degree”. Much as my hypothesis suggested, the

professional and doctoral degrees were most significant in reflecting median weekly earnings for

persons age 25 or over in 2013. The lower the degree, the less median weekly income would be

acquired (www.bls.gov).

Next, we look at how length of employment can be an independent variable for income. An

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Jarred D. RiccadonnaMBA 506 Project

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explanation of payment and salary by Susan Heathfield informs that there are multiple ways pay can be

incorporated with length of employment. Initially, there is the salary negotiation upon being hired

regarding fairness and contribution. Credentials and experience play a pivotal role in this process.

Next, a pay grade is determined. Pay grade is a range of annual income, much like the numbers ran in

the regression within the study. Once an employee reaches the top of their pay grade, they can receive

a raise called a cost of living allowance or obtain a promotion based on employer discretion. Years of

employment and job performance are the top two key components for receiving a raise or promotion

within a firm (humanresources.about.com).

Last, we look how gender discrimination can play a factor on income from other studies in

comparison to this study. My data shows gender does not play an important role in determining

education. However, studies show from multiple women’s organizations that gender discrimination is

problematic in today’s business world. Multiple studies suggests that “women on average earn 82% of

what their male peers earn” and that women typically earn 7% less than males do in a similar

occupation. Also, 53% of women graduates were paying a greater portion of their annual income

toward their student loans in comparison to 39% of men. These statistics challenge my data, which

shows that gender discrimination can still play a role in income. The unfortunate side is that most

private sector jobs do not disclose salaries. This likely prevents anyone from being able to prove that

gender discrimination exists in the workplace.

Conclusion

There are difficulties determining what factors can play the largest role relating to income.

Many firms look for many different things in the hiring process when determining how much a person

can get paid. However, most job qualifications and core competencies require a college degree,

typically a Bachelor’s at the very least. As my study and hypothesis suggests, education is still the

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initial influence on income for employers hiring in the workplace.

Works Cited

http://humanresources.about.com/od/glossaryp/g/pay-grade.htm

http://www.bls.gov/emp/ep_chart_001.htm

http://www.usatoday.com/story/money/personalfinance/2012/10/24/gender-pay-gap/1652511/

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Annex AMultiple Regression Data

(attached)