Essays on the Industrial Organization of Retail Markets
I study the industrial organization of both online and offline retail markets, trying to uncover the impact of recent trends in this industry, namely store brand and big data. Chapter 2 gives a detailed discussion about the data collection and analysis process in both online and offline retail markets. I first describe two offline retailer scanner datasets provided by Nielsen, the Homescan dataset and the Retail Scan dataset. The former documents household shopping trips, and the latter provides detailed information of how many of each product are sold in every retailer. Then I move on to describe the datasets collected at a big Chinese e-commerce platform. I will talk about what the datasets look like, how the datasets are collected and organized, and finally how they are analyzed.
Chapter 3 studies the welfare consequences of store brands in the offline retail market. I model the vertical relationship between retailers and manufacturers with a Nash-in-Nash bargaining model. I show that stronger preference for store brand has an ambiguous effect on the non-store brand prices, and that this effect is nonlinear: it is more negative when store brand has a smaller market share. Using Nelson Homescan data. I find a 1% increase in store brand's market share leads to a more than 0.5% decrease in non-store brand prices. This shows that in reality, the main impact of store brand is to help retailers gain better bargaining positions vis-a-vis suppliers. Furthermore, I also find the negative effect is larger in magnitude when the market share is smaller.
Chapter 4 studies the impact of market intelligence data on online retailer performance, product choice, and market outcomes. To do so, I exploit a unique setting in which an e-commerce platform provides its sellers with a market intelligence tool called “Market Insight.” First I find Market Insight helps online sellers choose better products and increases their sales. Second I show the current design of Market Insight benefits consumers and the platform, though providing ``too much" information through Market Insight could be harmful. Finally I solve for the platform-optimal design of Market Insight and show that the total sales revenue on the platform would increase 8% under this design, and consumer welfare will increase 0.8%. I also compare the platform-optimal design to the socially optimal design. They are very close to each other, showing in this case, the platform acts like a benevolent social planner.
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