Private information, price discrimination, and collusion - Florian Peiseler; Alexander Rasch; Shiva Shekhar (Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE), 2018)
We analyze firms' ability to sustain collusion in a setting in which horizontally differentiated firms can price-discriminate based on private information regarding consumers' preferences. In particular, firms receive private signals which can be noisy (e.g., big data predictions). We find that there is a non-monotone relationship between signal quality and sustainability of collusion. Starting from a low level, an increase in signal precision first facilitates collusion. However, there is a turning point from which on any further increase renders collusion less sustainable. Our analysis provides important insights for competition policy. In particular, a ban on price discrimination can help to prevent collusive behavior as long as signals are sufficiently noisy.
Keywords: L13; D43; L41; 330; Big Data; Collusion; Loyalty; Private Information; Third-Degree Price Discrimination