- Brian Heseung Kim
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Brian Heseung Kim
Despite decades and hundreds of billions of dollars of federal and state investment in policies to promote postsecondary educational attainment as a key lever for increasing the economic mobility of lower-income populations, research continues to show large and meaningful differences in the mid-career earnings of students from families in the bottom and top income quintiles. Prior research has not disentangled whether these disparities are due to differential sorting into colleges and majors, or due to barriers lower socioeconomic status (SES) graduates encounter during the college-to-career transition. Using linked individual-level higher education and Unemployment Insurance (UI) records for nearly a decade of students from the Virginia Community College System (VCCS), we compare the labor market outcomes of higher- and lower-SES community college graduates within the same college, program, and academic performance level. Our analyses show that, conditional on employment, lower-SES graduates earn nearly $500/quarter less than their higher-SES peers one year after graduation, relative to higher-SES graduate average of $10,846/quarter. The magnitude of this disparity persists through at least three years after graduation. Disparities are concentrated among non-Nursing programs, in which gaps persist seven years from graduation. Our results highlight the importance of greater focus on the college-to-career transition.
Interactive, text message-based advising programs have become an increasingly common strategy to support college access and success for underrepresented student populations. Despite the proliferation of these programs, we know relatively little about how students engage in these text-based advising opportunities and whether that relates to stronger student outcomes – factors that could help explain why we’ve seen relatively mixed evidence about their efficacy to date. In this paper, we use data from a large-scale, two-way text advising experiment focused on improving college completion to explore variation in student engagement using nuanced interaction metrics and automated text analysis techniques (i.e., natural language processing). We then explore whether student engagement patterns are associated with key outcomes including persistence, GPA, credit accumulation, and degree completion. Our results reveal substantial variation in engagement measures across students, indicating the importance of analyzing engagement as a multi-dimensional construct. We moreover find that many of these nuanced engagement measures have strong correlations with student outcomes, even after controlling for student baseline characteristics and academic performance. Especially as virtual advising interventions proliferate across higher education institutions, we show the value of applying a more codified, comprehensive lens for examining student engagement in these programs and chart a path to potentially improving the efficacy of these programs in the future.