Community colleges are a pivotal gateway for historically underserved students to access higher education, accounting for over 40% of undergraduate student enrollment in the United States (Ma and Baum, 2015). Increasingly, community colleges are under pressure to align their program offerings and enrollments with the needs of local labor markets. Increased alignment could benefit students by promoting transferable skill-building to the labor market, granting improved employment opportunities. However, students often select their field of study prior to entering college and may not select programs with superior returns. Previous findings suggest that students may prioritize factors other than labor market outcomes, such as course enjoyment (Baker et al., 2018). Notwithstanding, institutions serve as a significant source of guidance for students from low-income and first-generation backgrounds in refining and affirming their choice of study. In this descriptive study, I build on previous research that examines the dynamic selection process of community college students' fields of study. My key contribution is the ability to examine real-life decision-making from the time of application and assess how these choices shift throughout students' first two years. 

At this point in the study, we are examining the impact of pre-college factors on community college students' initial program of study, accounting for various factors such as age, high school GPA, race, and gender that may potentially have a significant impact on not only students' program of study but subsequent labor outcomes too. Understanding the factors influencing students' program of study is crucial as it directly affects educational satisfaction and future economic prospects. This understanding then enables institutions and policymakers to tailor programs to reflect the labor market demands while accommodating the diverse aspirations and circumstances of the student body, ultimately enhancing their success and social mobility. Through statistical testing, graphical representation, and multinomial logistic regression, I've found two demographic and/or academic factors that most notably impact students' initial enrollment in "STEM" and "Professional Studies" programs: female and Latino status. By examining these variables, we understand that the complex framing of educational institutions' programs of study requires a multifaceted approach, considering labor trends and equity issues.  I advocate for the development of tailored intervention strategies that accommodate the diverse needs of the female and Latino student populations to have greater representation in the STEM and Professional Studies fields to enhance academic outcomes and reduce educational disparities.