Research

Abstract: In this paper, I theoretically analyze 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 its filtering policy against suspicious reviews and weights for past reviews. At equilibrium, the number of fake reviews is increasing in the current quality and decreasing in the current reputation. Since fake reviews have a positive relationship with a product’s underlying quality, rational consumers might 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 reviews 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.

Abstract: This article theoretically analyzes the effects of the platform liability for the third-party products on incentives of the third-party vendors to make efforts to produce safe products. For this purpose, I suppose that the platform behaves as a mere marketplace and does not screen vendors or products sold there before the occurrence of a defect. Sellers have incentives to make an effort to build their own reputation. In this setting, the platform liability for third-party products has an unintended effect of reducing incentives for safe products. If the government sets a platform-liability rule for third-party products, the platform increases its monitoring efforts to detect defective products. However, the effectiveness of the liability rule depends on whether the sellers are building their reputation or not.

Abstract: We examined the effects of a rating system on sellers’ entry into and exit from a platform market characterized by monopolistic competition. We focused on the fact that the precision of a rating system affects the distribution of the products’ expected qualities in the marketplace. In the model, we assumed that the platform controls quality distribution in the monopolistic competitive marketplace.

Given the number of sellers (products), a more precise rating system designed by the platform always benefits consumers. By contrast, in a free-entry environment, a more precise rating system pushes out lower-quality sellers. This may decrease the consumer surplus. We provide some necessary and sufficient conditions for such a decrease in the consumer surplus.

In addition, in the absence of additional costs for improving the rating system, solutions of consumer surplus maximization and platform profit maximization coincide. That is, the platform favors consumers rather than sellers to gain profit. However, if a platform faces a convex cost function for improving the rating system, it underprovides the accuracy of the rating system.

Abstract: How do fake reviews alter oligopolistic market outcomes? We model fake reviews in strategic quantity and price competitions, in which a firm writes fake reviews to make its product look more attractive than others. In these markets, each firm has private information of its product quality type, which consumers need to infer before purchasing. The consumers observe a noisy rating score for each firm, which is the combination of authentic reviews and fake reviews, thus the subject of strategic manipulation. A firm’s fake review writing action, though costly, could inflate the review score of its product, boost consumers’ willingness to pay, and upwardly shift the firm’s demand function. Given the inflated demand functions, firms then engage in static quantity or price competition. There are four main findings. First, by focusing on a linear strategy with private information, we establish a linear equilibrium fake review writing strategy in each quantity and price competition models. Second, we report comparative statics of the equilibrium strategies, such as the equilibrium amount of generated fake reviews and quantities (or prices). Third, we analyze surplus implications of our fake review models, notably by making comparisons to the benchmark classical models without fake reviews. Fourth, we report some market competition policy implications. We conclude that, although it is counter-intuitive, ratings inflated by fake reviews could better signal product quality type information to consumers, compared to ratings based only on authentic reviews. Thus, fake reviews improve consumer surplus.

Abstract: Assumptions of competitive structure are often crucial for marginal cost estimation and counterfactual predictions. This paper introduces tests for price competition among multi-product firms. The tests are based on the firm’s revealed preference (revealed profit function). In contrast to other approaches based on estimated demand functions such as conduct parameter estimation, the proposed tests do not require any instrumental variables, even though the models can accommodate structural error terms. In this paper, 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 rationalized by price competition under a discrete/continuous choice model and increasing marginal costs. Adding more assumptions to the demand functions, such as logit, CES, or the coevolving and log-concave property produces some falsifiable restrictions.