Behavior-Based Price Discrimination and Data Protection in the Age of Algorithms
Haggai Porat
What the paper says
The literature on price discrimination typically focuses on immutable features, such as higher interest rates offered to black borrowers or higher prices offered to women at car dealerships. This paper studies behavior-based pricing (BBP), which is a practice of setting prices according to consumers’ past behavior, such as prior purchases. This is becoming the predominant form of price discrimination with the rise of artificial intelligence. Unlike race-based and sex-based discrimination, with BBP consumers can strategically adjust their behavior to impact the prices they will be offered in the future, and sellers can adjust prices to increase the informational value gleaned from consumers’ behavior. This paper further identifies two key legal insights: Mandating BBP disclosure may reduce overall welfare despite decreasing informational asymmetry; and data protection laws, such as the right to erase data and the right to opt out of data collection, could increase welfare despite increasing informational asymmetry.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.