Tests on Competitive Structures and Theories on Firm's Reputation Maintenance
Skip to main content
eScholarship
Open Access Publications from the University of California

UCLA

UCLA Electronic Theses and Dissertations bannerUCLA

Tests on Competitive Structures and Theories on Firm's Reputation Maintenance

Abstract

This dissertation theoretically analyzes three topics in industrial organization. The first chapter introduces tests for price competition among multi-product firms. The tests are based on the firm's revealed preference (revealed profit function). I employ a demand structure introduced by Nocke and Schutz (2018), the discrete/continuous choice model, which nests the multinomial logit demand and CES demand functions. Any price and quantity data can be rationalizedby price competition under a discrete/continuous choice model and increasing marginal costs. Adding more assumptions on the demand function, such as logit, CES, or the co-evolving and log-concave property produces some falsifiable restrictions.

The second and third chapters analyze seller's reputation maintenance behavior in dynamic models. Chapter 2 theoretically analyzes fake reviews on a platform market using models where a seller creates fake reviews through incentivized transactions, and its sales depend on its rating based on a review history. The platform can control the incentive for fake reviews by changing the parameters of the rating system, such as weights placed on old and new reviews and its filtering policy. At equilibrium, the number of fake reviews increases as quality increases but decreases as reputation improves. Since fake reviews have a positive relationship with a product’s underlying quality, rational consumers find a rating more informative when fake reviews exist, while credulous consumers suffer from a bias caused by boosted reputation. A stringent filtering policy can decrease the expected amount of fake reviews and the bias of credulous consumers, but at the same time, it can decrease the informativeness of a rating system for rational consumers. In terms of the weight placed on the review history, rational consumers benefit from higher weights on past reviews than from optimal weights without fake reviews.

In Chapter 3, sellers build their reputation through their investment in product safety. I show that the platform's liability for the third-party products distorts the sellers' incentive to invest in product safety.

Main Content
For improved accessibility of PDF content, download the file to your device.
Current View