Working Paper: NBER ID: w30219
Authors: Juan M. Ortner; Sylvain Chassang; Kei Kawai; Jun Nakabayashi
Abstract: We propose an equilibrium theory of data-driven antitrust oversight in which regulators launch investigations on the basis of suspicious bidding patterns and cartels can adapt to the statistical screens used by regulators. We emphasize the use of asymptotically safe tests, i.e. tests that are passed with probability approaching one by competitive firms, regardless of the underlying economic environment. Our main result establishes that screening for collusion with safe tests is a robust improvement over laissez-faire. Safe tests do not create new collusive equilibria, and do not hurt competitive industries. In addition, safe tests can have strict bite, including unraveling all collusive equilibria in some settings. We provide evidence that cartel adaptation to regulatory oversight is a real concern.
Keywords: Antitrust; Collusion; Statistical Screening; Cartels
JEL Codes: C57; C72; D44; H57; L4
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| screening for collusion using asymptotically safe tests (C52) | robust improvement over laissez-faire approaches (D78) |
| screening for collusion using asymptotically safe tests (C52) | does not expand the set of enforceable collusive schemes available to cartels (K21) |
| screening for collusion using asymptotically safe tests (C52) | does not increase incentives to form collusion (D43) |
| safe tests (C52) | unraveling of collusive equilibria (C72) |
| introduction of tests that flag close bids (D44) | disrupts collusive strategies (C72) |
| regulatory oversight (screening tests) (G18) | influences cartel behavior (L12) |
| cartel behavior (L12) | changes in the nature of collusion (D43) |