UC Irvine

With the proliferation of third-party sellers, counterfeiting has become a serious source of friction in online marketplaces. Different from traditional counterfeiters who target consumers who consciously seek for cheap knockoffs, sellers of deceptive counterfeit products target the whole population of online shoppers and make their products indistinguishable from genuine products. I develop two identification approaches to identify deceptive counterfeit products in online marketplace. The first applies natural language processing techniques to Amazon product reviews to generate a listing-level counterfeit probability, which in turn is used to classify ASIN (Amazon Standard Identification Number) listings as likely counterfeit or likely authentic. The second leverages an AI-based anti-counterfeit project launched by Amazon as an exogenous shock to directly measure the likelihood of a listing being counterfeit. I focus on two product categories, one a taste-based experience good (men’s fragrances) and the other being a utilitarian product (wireless cell phone chargers). I embed the estimated counterfeit probability into a BLP-type choice model to model consumers’ decision-making process and investigate how counterfeiting intensity affects user demand and platform revenues. I confirm that consumer disutility is increasing in the counterfeit probability, more so for high-end or popular products. I further find a substitution effect between likely counterfeit and likely authentic products: a 10% decrease in the price of a likely counterfeit product is associated with an average 0.0011% decrease in the market share of a likely authentic product. I leverage the structural parameter estimates to run a number of counterfactual experiments. These experiments suggest that protecting authentic sellers by simply banning all likely counterfeit listings would drastically reduce platform revenues. Instead, the deployment of counterfeit detection algorithms, and reporting the results to users, would align the interests of authentic sellers with the welfare of the platform. Overall, my analysis provides a robust empirical examination for identifying deceptive online counterfeiting and understanding its impact on the various stakeholders of an online retail platform.