The Value of Data Records

Many online platforms intermediate trade between sellers and buyers relying on individual data records of their personal characteristics. Is one buyer's record more valuable than another? How much is each worth and why? After formulating this intermediation as an information-design problem, we use linear-programming duality to characterize the unit value that the platform derives from each buyer's specific record. We find that this value differs from the payoff that the platform directly earns from the trade between a seller and a buyer. This gap reflects unaccounted externalities between records, which arise because the platform pools records to withhold information from the sellers. We then characterize the platform's willingness to pay for more records---e.g., more buyers joining the platform---and for better records---e.g., more information about existing buyers. Our analysis establishes basic properties of the demand side of data markets. Our methods apply generally to a large class of principal-agents problems