Search and Wholesale Price Discrimination
Corresponding Author
Guillermo Marshall
Sauder School of Business, University of British Columbia
e-mail: guillermo.marshall@sauder.ubc.ca
Search for more papers by this authorCorresponding Author
Guillermo Marshall
Sauder School of Business, University of British Columbia
e-mail: guillermo.marshall@sauder.ubc.ca
Search for more papers by this authorI thank Igal Hendel, Aviv Nevo, Rob Porter, Katja Seim (Editor), and two anonymous referees for detailed feedback as well as Rob Clark, Fernando Luco, Álvaro Parra, Tiago Pires, and numerous seminar participants at various institutions for helpful suggestions. All mistakes are my own.
Abstract
Firms often choose not to post prices in wholesale markets, and buyers must incur costs to discover prices. Inspired by evidence of customized pricing (e.g., some customers pay up to 70% more than others) and search costs, I estimate a search model to study how personalized pricing impacts efficiency in a wholesale market. I find that price discrimination decreases total surplus by 11.6% and increases the sellers' profits by up to 52.1%. These effects are partially explained by price discrimination softening competition through a decrease in search incentives, illustrating how price discrimination may magnify the efficiency costs of search frictions.
Supporting Information
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rand12317-sup-0001-SuppMat.zip185.4 KB |
Figure C1: Number of transactions per customer-product combination Figure C2: Number of days between two transactions by a particular customer: Product-level averages Figure C3: Robustness: Estimates of expected net search benefits Figure C4: Difierences in average paid prices in the price discrimination and uniform price equilibria Table C1: Decomposing the variance of price-cost margins Table C2: Prices and the benefits of paying a low price: OLS regressions Table C3: Breakups and measures of price changes: OLS regressions Table C4: Breakups and measures of price changes: Subsample of customer week combinations with fewer than two breakups. Table C5: Predicted prices and markups on search costs: OLS regressions |
Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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