Your Browsing History May Cost You: A Framework for Discovering Differential Pricing in Non-Transparent Markets

In many online markets we "shop alone"- there is no way for us to know the prices other consumers paid for the same goods. Could this lack of price transparency lead to differential pricing? To answer this question, we present a generalized framework to audit online markets for differential pricing using automated agents. Consensus is a key idea in our work: for a successful black-box audit, both the experimenter and seller must agree on the agents' attributes. We audit two competitive online travel markets on kayak.com (flight and hotel markets) and construct queries representative of the demand for goods. Crucially, we assume ignorance of the sellers' pricing mechanisms while conducting these audits. We conservatively implement consensus with nine distinct profiles based on behavior, not demographics. We use a structural causal model for price differences and estimate model parameters using Bayesian inference. We can unambiguously show that many sellers (but not all) demonstrate behavior-driven differential pricing. In the flight market, some profiles are nearly more likely to see a worse price than the best performing profile, and nearly more likely in the hotel market. While the control profile (with no browsing history) was on average offered the best prices in the flight market, surprisingly, other profiles outperformed the control in the hotel market. The price difference between any pair of profiles occurring by chance is $ 0.44 in the flight market and $ 0.09 for hotels. However, the expected loss of welfare for any profile when compared to the best profile can be as much as $ 6.00 for flights and $ 3.00 for hotels (i.e., 15 × and 33 × the price difference by chance respectively). This illustrates the need for new market designs or policies that encourage more transparent market design to overcome differential pricing practices.