Research Teaching Asymptotica
Mike Vo

Mike Vo

Postdoctoral Research Associate in Applied Economics & Data Science
University of Connecticut
Email: [email protected]

Economic Research

Interests: Industrial Organization, Applied Econometrics, Microeconomic Theory

Publications

When to degrade a product: The utility-to-cost ratio rule Economics Letters, September 2025
Harnessing Human Mobility Data for Applied Economic Research: Current Knowledge, Challenges, and Emerging Opportunities Joint with Cristina Connolly, Sandro Steinbach, and Xibo Wan
Journal of Economic Surveys, February 2026

Working Papers

Endogenous Product Return Policies and Consumer Welfare Second-round R&R at International Journal of Industrial Organization
Price-Directed Search and Collusion: How Easier Search for Product Information Can Stabilize Cartels
Directed Search, Frictions, and Tacit Collusion: A Computational Experiment Joint with Joe Nguyen
The Impact of Texas Senate Bill 8 on Family Care Utilization: Evidence from Large-Scale Mobility Data Joint with Cristina Connolly, Sandro Steinbach, Debarchana Ghosh, and Xibo Wan

Selected Work in Progress

Estimating Structural Models with Privacy-Protected Data: A Mean Bayesian Correction for Large-Scale Mobility Data Joint with Cristina Connolly, Sandro Steinbach, and Xibo Wan
Beyond Supermarkets: Measuring the Impact of Food Environment on Healthy Store Utilization Joint with Cristina Connolly, Sandro Steinbach, and Xibo Wan

Teaching

University of California, Irvine

Instructor

Principles of Microeconomics

Teaching Assistant

Graduate Microeconomic Theory II (PhD Core), Industrial Organization III, Money and Banking, Managerial Economics, Intermediate Microeconomics

Outstanding Teaching Assistant Award

Truman State University

Teaching Assistant

Econometrics, International Trade

Featured Project

Asymptotica

I am building Asymptotica to connect the mathematical foundations of asymptotic theory and its practical application. It is designed to help PhD students, advanced undergraduates, and practitioners who want to strengthen knowledge in asymptotic inference to facilitate both the execution and interpretation of empirical research.

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