The Impact of Privacy Policy on the Auction Market for Online Display Advertising

Abstract

The advent of online advertising has simultaneously created unprecedented opportunities for advertisers to target consumers and prompted privacy concerns among consumers and regulators. This paper estimates the financial impact of privacy policies on the online display ad industry by applying an empirical model to a proprietary auction dataset. Two challenges complicate the analysis. First, while the advertisers are assumed to publicly observe tracking profiles, the econometrician does not see this data. My model overcomes this challenge by disentangling the unobserved premium paid for certain users from the observed bids. In order to simulate a market in which advertisers can no longer track users, I set the unobserved bid premium’s variance to zero. Second, the data provider uses a novel auction mechanism in which first-price bidders and second-price bidders operate concurrently. I develop new techniques to analyze these hybrid auctions. I consider three privacy policies that vary by the degree of user choice. My results suggest that online publisher revenues drop by 3.9% under an opt-out policy, 34.6% under an opt-in policy, and 38.5% under a tracking ban. Total advertiser surplus drops by 4.6%, 4'39%, and 45.5% respectively.