Doctoral Dissertation Research in Economics: Merger Analysis of Multisided Platforms: An Application to the Used Heavy Duty Truck Auction Market

University Of Maryland, College Park, College Park MD

Investigators

Abstract

This project is designed to test economic theories about the effects of market competition on firms providing multisided platforms. A multisided platform facilitates interactions between two or more groups of participants. For example, a firm that sells auction services is connecting sellers with buyers. This project will develop new economic theory and will test the theory using data from auction sales of used heavy-duty trucks in the United States. The data analysis will examine the effects of a specific previous merger in this industry to see if and how the merger reduced competition. The methods developed in this project can also be used to examine other markets with competition between between multisided platform firms. Policymakers in the US are asked to evaluate proposed mergers to determine whether the merger might reduce competition. This is a difficult task for these types of merger, since the evaluation has to take into account both how the platforms interact and how the merger might change interactions. This project will develop and demonstrate one way to evaluate these factors. Preserving and strengthening competition strengthens the U.S. economy as a whole. The project will extend the existing theoretical models of multisided platforms by including both horizontal differentiation between platforms and vertical differentiation among goods. According to the model constructed in the project, the higher fees charged by the platforms along with the merger have a heterogeneous impact on sellers with goods at different quality levels. When there are different indirect network effects among the groups on these platforms, it can trigger a change of quality structure across platforms. This structural change is more significant if there is less horizontal differentiation between platforms, which can make the number of participants more elastic to the fees and the indirect network effect. These findings are consistent with transaction data collected from the U.S. used heavy-duty truck auction market. According to the data, the high-quality trucks sold on the offline platform are valued more than the ones sold on the online platform. After the merger of the leading offline and online auction platforms, the offline platform charges a higher fee and uses a cross-platform information policy to reduce the search friction across the two platforms. These policy changes affect the decisions of buyers and sellers, resulting in a quality structure change on these two platforms: low-quality trucks are more likely to be transacted on the online platform, and high-quality trucks tend to be transacted on the offline platform. The difference in transaction prices on these platforms becomes more significant than before. The project will focus on this possible market evolution caused by the merger. After performing a structural estimation based on the model, the project will investigate the welfare implication of the merger on different groups and show several ways to improve the platform's revenue or social welfare. The method and results used in this project are especially useful when the markets have vertically differentiated goods, such as the health insurance market and the peer-to-peer lending market. The project can help us to understand the potential heterogeneous impact of mergers on different groups in those markets. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.