Essays on market structure and policy in higher education
Abstract/Contents
- Abstract
- This dissertation contains three essays discussing government policy in higher education, the effects of online social networks during college on students after graduating, and how to use search data as a tool to recover characteristics of both products and customers to estimate demand. In the first chapter, co-authored with Shengmao Cao, we study the equilibrium impact of student aid in the United States market for sub-baccalaureate higher education and consider the implications of alternative aid policies. We estimate a structural model of supply and demand in this market. We then derive an optimal voucher policy that maximizes educational quality, measured as the value-added in earnings generated by each sub-baccalaureate college. Our optimal voucher policy highlights the fact that for-profit colleges, despite being lower quality on average, are more effective at increasing enrollment than public community colleges. Consequently, these schools play an important role in improving the educational outcomes of students. In the second chapter, using quasi-random variation from Facebook's entry to college campuses, I exploit a natural experiment to estimate the effect of online social network access on future earnings. My estimates imply that access to Facebook for an additional year in college causes a .61 percentile increase in a cohort's average earnings, translating to an average wage increase of around \$970 in 2014. My results also suggest that Facebook access decreases income inequality within a cohort. I provide evidence that wage increases comes through the channel of increased social ties to former classmates, which leads to strengthened employment networks between college alumni. In the third chapter of this dissertation, co-authored with Greg Lewis and Giorgos Zervas, we extend a machine learning approach (Bayesian Personalized Ranking) that allows us to jointly learn latent product characteristics and consumer preferences relevant for consumer demand from search data. We show how search data, summarized through these latent parameters, can be combined with existing demand estimation approaches to better predict demand. Our application is to the hotel market, where we combine two datasets: consumers' web browsing histories, and hotel prices and occupancy rates. Using an event study design, we show that closeness in latent characteristic space predicts competition: hotels that are close to new entrants lose the most market share post-entry. We take a more structural approach to the 2016 merger of Marriott and Starwood, demonstrating that by using latent characteristics and consumer preferences learned from search data, we can substantially improve post-merger predictions of demand relative to standard baselines.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2022; ©2022 |
| Publication date | 2022; 2022 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Armona, Luis Christopher |
|---|---|
| Degree supervisor | Gentzkow, Matthew |
| Thesis advisor | Gentzkow, Matthew |
| Thesis advisor | Hoxby, Caroline Minter |
| Thesis advisor | Somaini, Paulo |
| Degree committee member | Hoxby, Caroline Minter |
| Degree committee member | Somaini, Paulo |
| Associated with | Stanford University, Department of Economics |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Luis Armona. |
|---|---|
| Note | Submitted to the Department of Economics. |
| Thesis | Thesis Ph.D. Stanford University 2022. |
| Location | https://purl.stanford.edu/sr936xb9130 |
Access conditions
- Copyright
- © 2022 by Luis Christopher Armona
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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