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 document that the current federal aid system, by subsidizing marginal price increases, incentivizes private for-profit colleges to charge high tuition prices. We also present new descriptive evidence on the importance of advertising in the demand for higher education. Using these facts, we estimate a structural model of supply and demand in this market. We then derive an optimal voucher policy that maximizes educational quality, holding the quality of schools fixed. We measure quality by estimating the value-added in earnings generated by each sub-baccalaureate college. Counterfactual results show that the optimal voucher system improves the overall quality provided by 8.8%. 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 0.61 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. A building block of many models in empirical industrial organization is a characteristic space, where products are modeled as a bundle of characteristics over which consumers have preferences. The ability of such models to predict counterfactual outcomes depends on how well this characteristic space representation can capture substitution patterns. A limitation of existing methods is that product characteristics must be observable. In this chapter, we show how search data 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. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com.bibliotheek.ehb.be/en-US/products/dissertations/individuals.shtml.]