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Undergraduate Education and the Gender Wage Gap: An Analysis of the Effects of College Experience and Gender on Income

By Kelsey Siman

Labor and education economists have long been interested in the link between undergraduate education and earnings. In addition, studies have addressed the connections between gender and college major and GPA, as well as between gender and income. This paper brings all of these together in order to show that college major choice does have a significant effect on earnings, and that this effect differs with gender and across majors. The results show that controlling for college major, ability measures, graduation year, and GPA can help to explain a majority of the gender pay gap. Finally, the thesis then utilizes the Oaxaca-Blinder Decomposition to break down the price and composition effect of undergraduate education on the gender pay gap.

Honor’s Thesis

Data Set

Advisor: Arnaud Maurel, Kent Kimbrough | JEL Codes: A22, J16 | Tagged: College, Gender, Income

Capturing a College Education’s Impact on Industry Wages Across Time: An Analysis of Academic Factors that Affect Earnings

By Ian Low

Studying how a college education can impact one’s wages has always been an area of interest amongst labor and education economists. While previous studies have stressed using single academic factors (i.e. college major choice, performance, or college prestige) to determine the effect on wages, there has not been a focus on predicting wages given industries and a combination of these academic factors across time. Therefore, the crux of my thesis seeks to provide a new model which incorporates college major choice, GPA, industry selection across time, college type (private or public), natural ability (standardized test scores), and several demographic variables in order to predict percent increase/decrease in wages. My results show that college major choice, academic performance, natural ability, and industry selection (together) do have a significant impact on earnings, and they are appropriate measures to predict post-graduation wages.

View Thesis

Advisor: Peter Arcidiacon | JEL Codes: A2, A22, J3, J31 | Tagged: College, Industry, Wages

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