Luis Armona
Job Market Candidate

Stanford University
Department of Economics
579 Serra Mall
Stanford, CA 94305
562-754-0870
larmona@stanford.edu

I am on the academic job market for the 2021-2022 academic year and will be available for interviews during the ASSA annual meeting.

Curriculum Vitae

Fields:
Industrial Organization
Economics of Education

Expected Graduation Date:
June, 2022

Thesis Committee:
Matthew Gentzkow (Primary):
gentzkow@stanford.edu

Caroline Hoxby:
choxby@stanford.edu

Paulo Somaini:
soma@stanford.edu

Job Market Paper

Redesigning Federal Student Aid in Higher Education (with Shengmao Cao)
In this paper, we study the equilibrium impact of student aid in the 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 12%. 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 an important factor for improving the educational outcomes of students.

Publications

Home Price Expectations and Behaviour: Evidence from a Randomized Information Experiment (with Andreas Fuster, Basit Zafar) Review of Economic Studies, 2019
Home price expectations are believed to play an important role in housing dynamics, yet we have limited understanding of how they are formed and how they affect behaviour. Using a unique information experiment embedded in an online survey, this article investigates how consumers' home price expectations respond to past home price growth, and how they impact investment decisions. After eliciting respondents' priors about past and future local home price changes, we present a random subset of them with factual information about past (one- or five-year) changes, and then re-elicit expectations. This unique panel data allows us to identify causal effects of the information, and provides insights on the expectation formation process. We find that, on average, year-ahead home price expectations are revised in a way consistent with short-term momentum in home price growth, though respondents tend to underpredict the strength of momentum. Revisions of longer-term expectations show that respondents do not expect the empirically-occurring mean reversion in home price growth. These patterns are in line with recent behavioural models of housing cycles. Finally, we show that home price expectations causally affect investment decisions in a portfolio choice experiment embedded in the survey.

Beyond Word Embeddings: Dense Representations for Multi-Modal Data (with Jose P. Gonzalez-Brenes, Ralph Edezath) Proceedings of the 32nd International FLAIRS Conference, 2019
Methods that calculate dense vector representations for text have proven to be very successful for knowledge representation. We study how to estimate dense representations for multi-modal data (e.g., text, continuous, categorical). We propose Feat2Vec as a novel model that supports supervised learning when explicit labels are available, and self-supervised learning when there are no labels. Feat2Vec calculates embeddings for data with multiple feature types, enforcing that all embeddings exist in a common space. We believe that we are the first to propose a method for learning self-supervised embeddings that leverage the structure of multiple feature types. Our experiments suggest that Feat2Vec outperforms previously published methods, and that it may be useful for avoiding the cold-start problem.

Working Papers

Learning Product Characteristics and Consumer Preferences from Search Data (with Greg Lewis, Georgios Zervas)
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 paper, we extend a machine learning approach (Bayesian Personalized Ranking) that allows us to jointly learn latent product characteristics and consumer preferences from search data. We then show how this can be combined with existing demand estimation approaches to 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.

Information Inequality in Online Education (with Mohammad Rasouli)
In this paper, we study platform solutions for improving customer engagement in online higher education by reducing informational inequality for historically under-represented groups in education such as females and workers seeking to improve their skill set. Using novel search and enrollment data from the largest online education platform in Iran, we estimate a structural model of course search and enrollment for paid courses, allowing us to recover learner belief's about courses, as well as their true preference over the characteristic space of online courses. We use machine learning methods to recover the latent characteristic space of courses, identifying which courses are substitutes via a data-driven approach. We document significant heterogeneity in how learners differing by gender and working status perceive course value, due to biased beliefs, relative to the true value. Counterfactual policy exercises suggest that the platform can increase revenue, improve consumer surplus, and reduce the gender gap in quantitative courses. Finally, we also present the problem faced by the platform from an information design perspective, and characterize the optimal signal the platform can send to learners with heterogenous priors to maximize an arbitrary objective function.

Online Social Network Effects in Labor Markets: Evidence From Facebook's Entry into College Campuses
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.

How Does For-profit College Attendance Affect Student Loans, Defaults and Labor Market Outcomes (with Rajashri Chakrabarti and Michael Lovenheim)
For-profit providers are becoming an increasingly important fixture of US higher education markets. Students who attend for-profit institutions take on more educational debt, have worse labor market outcomes, and are more likely to default than students attending similarly-selective public schools. Because for-profits tend to serve students from more disadvantaged backgrounds, it is important to isolate the causal effect of for-profit enrollment on educational and labor market outcomes. We approach this problem using a novel instrument combined with more comprehensive data on student outcomes than has been employed in prior research. Our instrument leverages the interaction between changes in the demand for college due to labor demand shocks and the local supply of for-profit schools. We compare enrollment and postsecondary outcome changes across areas that experience similar labor demand shocks but that have different latent supply of for-profit institutions. The first-stage estimates show that students are much more likely to enroll in a for-profit institution for a given labor demand change when there is a higher supply of such schools in the base period. Among four-year students, forprofit enrollment leads to more loans, higher loan amounts, an increased likelihood of borrowing, an increased risk of default and worse labor market outcomes. Two-year for-profit students also take out more loans, have higher default rates and lower earnings. But, they are more likely to graduate and to earn over $25,000 per year (the median earnings of high school graduates). Finally, we show that for-profit entry and exit decisions are at most weakly responsive to labor demand shocks. Our results point to low returns to for-profit enrollment that have important implications for public investments in higher education as well as how students make postsecondary choices.

Work in Progress

What is Newsworthy? Information and Bias in the Filtering of News (with Matthew Gentzkow, Emir Kamenica, Jesse Shapiro)
We study the determinants of newsworthiness in theory and in practice. We consider a setting in which an information provider chooses a filtering rule to maximize the utility of a receiver. We show that differences across stories or topics in the likelihood of reporting can be broadly divided into differences in the information content of the reports and differences in their weight in receiver preferences. We analyze the extent to which these are separately identified in reporting data. We then apply this framework to study the determinants of television news reporting in three domains: economic news, weather, and military casualties. We find that information content explains much of the systematic variation in reporting we observe, and we highlight specific cases where preference differences appear to play an important role.