Information Inequality in Online Education

63 Pages Posted: 8 Jan 2021 Last revised: 7 Sep 2021

See all articles by Luis Armona

Luis Armona

Stanford University

Mohammad Rasouli

Stanford University

Date Written: November 13, 2020


In this paper, we study platform solutions for improving customer engagement in online higher education by reducing 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 increase overall achievement in courses by reducing the information inequality via redesign of search and recommendation engine. Finally, we map the optimal search engine problem of a platform, subject to public non-discriminatory signals, into an information design problem, and characterize the optimal public signal the platform can send to learners with heterogeneous priors.

Suggested Citation

Armona, Luis and Rasouli, Mohammad, Information Inequality in Online Education (November 13, 2020). Available at SSRN: or

Luis Armona (Contact Author)

Stanford University ( email )

Mohammad Rasouli

Stanford University ( email )

Stanford, CA 94305
United States

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