Date of Completion

Spring 5-1-2025

Thesis Advisor(s)

Elizabeth Jacobs; HaiYing Wang; Haim Bar

Honors Major

Statistics

Abstract

As the job market experiences turbulence, understanding how different people progress in their career trajectories can aid in understanding why certain professional disparities exist between demographics. Studying job mobility in the United States is critical to understanding patterns that could be indicative of systemic behaviors. Prior literature has focused on job mobility based on income, industry, and/or older data. We focus on encoding job mobility patterns of Indian immigrants in the U.S. labor force using webscraped LinkedIn data from 2019 and measuring various job changes through mapping job titles to a five-point scale and comparing string values. We also utilize logistic regression and variable subset selection methods to optimize model performance and improve interpretation by considering model outputs holistically.

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