Inequality in postsecondary education : using new data and methods to understand educational barriers
Keywords: Education.

My dissertation utilizes new data sources and methods to provide evidence on some of the most pernicious barriers to postsecondary educational access and completion for different student groups. In particular, I focus on understanding how financial constraints, informational barriers, and academic support contribute to educational inequity in postsecondary education. I use text-as-data methods to better understand students' postsecondary experiences, and quasi-experimental methods to estimate the causal impacts of policies intended to improve postsecondary access. In my first paper (coauthored with Thomas Dee, Rachel Baker, and Brent Evans), we provide an overview of text-as-data methods for education researchers and apply these methods to examine gender bias in a field experiment in the discussion forums of online classes. We find that respondents provide less assistance to the randomized female posters than they do to the randomized male posters, demonstrating that female students receive less academic support in these MOOCs. This work adds to the literature on how gender bias can manifest itself in online classrooms, and more broadly on how different student groups can receive different levels of academic support in postsecondary education. My second paper examines how remote college counselors can support high school seniors through the college application process. I examine a large-scale remote counseling program in which college counselors initiated interactions with 15,000 low- and middle-income high school seniors via text message, and I use text-as-data methods to measure which interactions lead to productive engagement between counselors and students. I show that interactions about financial aid offers and financial aid applications are much more likely to generate productive engagement than interactions about college lists. My findings reveal the complexity that remote programs face in providing more personalized advice to students, as well as demonstrate how text-as-data methods can combine qualitative and quantitative analysis to generate useful information about how students substantively engage with large-scale educational programs. My third paper, coauthored with Matea Pender, examines the impacts of local promise programs. We use a differences-in-differences design to estimate the impacts of all the local promise programs implemented between 2005 and 2018, across 374 school districts. We find that promise programs increase college enrollment by one percentage point on average, and programs that provide full tuition tend to have larger impacts than programs that only partially subsidize tuition. We find no long-term impacts of programs that only subsidize two-year institutions but find some evidence that programs that subsidize two- and four-year institutions decrease student debt levels and increase earnings after college. These findings are somewhat less positive than impacts found in earlier studies, but still demonstrate that promise programs can impact college enrollment at scale. Taken together, my dissertation shows the challenges and possibilities in improving access to postsecondary institutions, as well as the importance of utilizing new and large-scale datasets in understanding educational problems and solutions