Haoran Wang

I am a Ph.D. candidate in Economics at The Ohio State University. My research interests are in labor economics, health economics, and public policy.

I study how labor market regulations and social insurance programs affect employment, earnings, and well-being. My research focuses on labor market information and wage determination, labor supply, and health and social insurance programs, with particular interests in information frictions, labor market concentration and competition, workers’ bargaining power, and public health insurance.

Research Fields

Labor Economics, Health Economics, Public Policy, Applied Econometrics

Job Market Paper

The Effects of Colorado’s Pay Transparency Law on Older Workers

This paper studies how cross-firm pay transparency affects labor market outcomes of mid- to late-career workers. Using Colorado’s Equal Pay for Equal Work Act (EPEWA) and a difference-in-differences design, I find that pay transparency increases weekly earnings and hourly wages. The gains are larger for women, more-educated workers, and workers closer to retirement; the law also narrows the gender wage gap. Wage gains among both job changers and stayers, together with larger effects where occupational wage dispersion and employer concentration are greater, are potentially consistent with pay transparency improving mid- to late-career workers’ bargaining position in wage setting. The policy also increases employment and hours worked. By contrast, its effects among younger workers are limited. Survey evidence on workers’ beliefs further shows that pay transparency leads mid- to late-career workers to revise their expectations about outside options and their reservation wages. Overall, the findings suggest that cross-firm wage information is particularly valuable for mid- to late-career workers, who may face greater search frictions and rely on outdated wage information.

Publication

Balancing Efficiency and Equity in Classroom Assignment under Endogenous Peer Effects

with Lei Bill Wang, Zhenbang Jiao, and Om Prakash Bedant

Published in Journal of Applied Economics

This paper develops a three-step empirical framework to optimize classroom assignments under endogenous peer effects. First, we design PeerNN, a neural network modeling friendship formation, generating a linkage-intensity matrix (Ω) that captures student popularity. Second, using quasi-random classroom assignments as an instrument, we estimate peer effects: a 10% increase in friends’ 6th-grade class rank (weighted by Ω) increases 8th-grade test scores by 0.13 SD. Third, we simulate assignment policies. A genetic algorithm (GA) maximizing average peer effect yields a 1.9% gain but exacerbates inequality, disproportionately harming students with lower 6th-grade class ranks. We propose a variance-penalizing approach named Algorithmically Fair GA (AFGA), which achieves a 1.2% improvement while ensuring equitable outcomes. Our findings highlight the trade-off between efficiency and equity in classroom assignment policies and underscore the need for fairness-aware algorithms when designing group assignment policies.

First place, 2024 Hayes Forum Poster Competition.

Published Paper