Monday, September 30, 2019

Optimal Law Enforcement with Ordered Leniency

Claudia M. Landeo, University of Alberta - Department of Economics and Kathryn E. Spier, Harvard University - Law School - Faculty; National Bureau of Economic Research (NBER) offer Optimal Law Enforcement with Ordered Leniency.

ABSTRACT: This paper studies the design of optimal enforcement policies with ordered leniency to detect and deter harmful short-term activities committed by groups of injurers. With ordered leniency, the degree of leniency granted to an injurer who self-reports depends on his or her position in the self-reporting queue. We show that the ordered-leniency policy that induces maximal deterrence gives successively larger discounts to injurers who secure higher positions in the reporting queue. This creates a so-called "race to the courthouse" where all injurers self-report promptly and, as a result, social harm is reduced. We show that the expected fine increases with the size of the group, thus discouraging the formation of large illegal enterprises. The first-best outcome is obtained with ordered leniency when the externalities associated with the harmful activities are not too high. Our findings complement Kaplow and Shavell's (JPE 1994) results for single-injurer environments.

https://lawprofessors.typepad.com/antitrustprof_blog/2019/09/optimal-law-enforcement-with-ordered-leniency.html

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