Publication

The Impact of Return Policies and Perception Heterogeneity on Sales Platforms for Experience Goods: An Experimental Study

Journal of Economic Behavior and Organization, Vol. 239, 2025, 107287. (With Jinliang Liu) [DOI]

Abstract: On sales platforms for experience goods, consumers only know the distribution of a good’s value before purchase, while its actual value is revealed after the purchase. However, consumers may perceive the distribution’s range differently, and the presence of a return policy can also influence strategic interactions in the market. Utilizing a post-price framework and controlled laboratory experiments, this paper provides the first investigation of experience goods markets that directly examines (1) the presence of a return policy and (2) the level of consumers’ perception heterogeneity about the range of the value’s corresponding distribution. We find that return policies result in lower prices in markets for experience goods. In addition, a larger perception heterogeneity reduces prices and increases return rates. Our results highlight potential downsides of mandated return policies on online sales platforms, as they reduce sellers’ profits without improving consumer welfare.

Working Papers

Tacit Collusion by Pricing Algorithm with Rule-Based Rivals (Job Market Paper)

(Draft coming soon)

Abstract: Pricing algorithms, especially reinforcement learning algorithms, have been widely used over the past decades for firms in competitive markets, helping them capture more information about the market and their rivals. Existing literature has shown that, however, reinforcement learning algorithms could lead to supracompetitive prices even without any communication between firms. In a framework of price competition between two firms both initially using rule-based strategies, we provide theoretical and simulation evidence that the prices of both firms weakly increase when one firm adopts an algorithm. We also find that the firm using a rule can ``free ride” and benefit more from the other firm’s algorithm adoption. Our findings contribute to the literature by highlighting the importance of the order of algorithm adoption and the transition from rule-based strategy to learning-based algorithm, demonstrate how the tacit collusion can occur in a broader circumstances.

Detecting Algorithmic Collusion by Algorithms

with Yonghong An and Yu Zhu (Draft coming soon)

Abstract: Algorithms can facilitate collusive behaviors among competing firms. It is challenging for antitrust authority to monitor and detect algorithmic collusion due to complicated price patterns and frequent price changes. In this paper, we study two important issues assuming that antitrust authority employs algorithms: how firms respond to an algorithmic antitrust authority and what price patterns the algorithmic antitrust authority would detect. In a framework of quantity competition of two firms, we demonstrate numerically that antitrust authority’s algorithms can effectively boost firms’ quantities and reduce the possibility of algorithmic collusion. We also identify several important factors that affect the authority’s effectiveness in auditing collusion: cost uncertainty of firms, penalty on firms due to detected collusion, and the authority’s incentive to audit.

Let Them Eat Pie: Addressing Sample Selection in Multiparty Elections

with Ali Kagalwala, Thiago M. Q. Moreira and Guy D. Whitten, Revised and Resubmitted to Political Analysis. (Draft available upon request)

Abstract: Elections are central to the study of politics. When studying parties’ vote shares across districts, scholars are encouraged to use compositional-outcome models in order to test their theories about what factors shape the dynamics of electoral support. One problem with existing compositional modeling approaches is that they incorrectly deal with scenarios in which not all parties compete in every electoral district. Because unobserved factors that affect a party’s decisions to contest a district are likely correlated with its performance in districts where the party fielded candidates, failing to account for partial contestation is likely to result in sample selection bias. Addressing sample selection in a compositional setting is challenging because the outcomes are in log-ratio form, and thus the errors are not normally distributed. To deal with these issues, we introduce a novel maximum likelihood approach which accounts for this type of sample selection and demonstrate through simulations that our method outperforms commonly used solutions, including the conventional Heckman correction. We illustrate the utility of our approach by analyzing the 2017 and 2019 UK parliamentary elections in English constituencies.

Work In Progress

Historical Simulations of Unified China and Divided Europe: Insights from Reinforcement Learning

with Weiwen Yin and Li Zheng

Long Divided, Must Unite? Historical Simulations of Japan’s Political Evolution Using Reinforcement Learning