Professor Claudia Landeo discusses her theoretical work on the design of optimal law enforcement policies with ordered leniency at the Annual Meeting of the American Law and Economics Association. Her study will be published in the University of Chicago's Journal of Law and Economics, the top journal in the field of Law and Economics
On May 18, 2019, Professor Claudia Landeo presented her paper "Optimal Law Enforcement with Ordered Leniency'' (co-authored with Professor Kathryn Spier) at the Annual Meeting of the American Law and Economics Association held at New York University. Her paper will be published in the University of Chicago's Journal of Law and Economics, the top journal in the field of Law and Economics. An extended abstract is presented below:
Extended Abstract
Illegal activities are often committed by groups of people working together rather than by individuals working alone. Common examples in the corporate setting include insider trading and market manipulation schemes. In 2011, the FBI reported 726 corporate fraud cases, several of which involved losses to public investors that individually exceeded $1 billion, and 343 securities fraud cases involving more than 120,000 victims and approximately $8 billion in losses (FBI, 2012). More generally, illegal activities committed by groups of wrongdoers impose considerable costs on society. To combat illegal group activities, law enforcement agencies often grant leniency to wrongdoers who come forward and self-report. In a typical leniency program, wrongdoers who self-report early face lower sanctions than those who self-report later.
This paper theoretically studies the design of optimal enforcement policies with ordered leniency to detect and deter illegal short-term activities committed by groups of injurers. With an ordered-leniency policy, the degree of leniency granted to an injurer depends on his or her position in a self-reporting queue. The earlier an injurer reports the act, the higher his or her position in the self-reporting queue. Our analysis demonstrates that the ordered-leniency policy that induces maximal deterrence gives successively larger discounts to injurers who secure higher positions in the self-reporting queue, creating a so-called "race to the courthouse'' where all injurers self-report promptly. Prompt self-reporting also allows the enforcement agency to detect illegal activities sooner and, consequently, to mitigate the harms inflicted on others. We also 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 study provides important contributions to the literature on the control of harmful externalities. To the best of our knowledge, there are no previous theoretical studies of optimal enforcement policies with ordered leniency for harmful short-term group activities.
Strong policy implications are derived from our study. Although our paper is motivated by insider trading and securities fraud, our analysis applies to any kind of harmful short-term activity committed by a group of wrongdoers. For instance, our work is relevant for the design of plea-bargaining agreements. We demonstrate that the proverbial prisoners' dilemma is not the only way to conduct plea bargaining or to detect and punish socially harmful activities. When the wrongdoers are sufficiently distrustful of each other, the prosecutor could forego the prisoners' dilemma and employ a coordination mechanism instead. Our study also provides important insights for the design of law enforcement policies involving corporate and individual criminal liability, and the design of whistleblower and environmental policies, among others.