An online platform makes a profit by auctioning an advertising slot that appears whenever a consumer visits its website. Several firms compete in the auction, and consumers differ in their preferences. Prior to the auction, the platform gathers data which is statistically correlated with consumers' tastes for products. We study the implications of the platform's decision to allow potential advertisers to access the data about consumers' characteristics before they bid. On top of the familiar trade-off between rent extraction and efficiency, we identify a new trade- off: the disclosure of information leads to a better matching between firms and consumers, but results in a higher equilibrium price on the product market. We find that the equilibrium price is an increasing function of the number of firms. As the number of firms becomes large, it is always profitable for the platform to disclose the information, but this need not be efficient, because of the distortion caused by the higher prices. When the quality of the match represents vertical shifts in the demand function, we provide conditions under which disclosure is optimal.
online advertising; privacy; information disclosure; auctions;
Alexandre Cornière (de), and Romain De Nijs, “Online Advertising and Privacy”, The RAND Journal of Economics, vol. 47, n. 1, Spring 2016, pp. 48–72.
The RAND Journal of Economics, vol. 47, n. 1, Spring 2016, pp. 48–72