Ordered Leniency: An Experimental Study of Law Enforcement with Self-Reporting

55 Pages Posted: 1 Oct 2018

See all articles by Claudia M. Landeo

Claudia M. Landeo

University of Alberta - Department of Economics

Kathryn E. Spier

Harvard University - Law School - Faculty; National Bureau of Economic Research (NBER)

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Date Written: September 2018

Abstract

This paper reports the results of an experiment designed to assess the ability of an enforcement agency to detect and deter harmful short-term activities committed by groups of injurers. With ordered-leniency policies, early cooperators receive reduced sanctions. We replicate the strategic environment described by Landeo and Spier (2018). In theory, the optimal ordered-leniency policy depends on the refinement criterion applied in case of multiplicity of equilibria. Our findings are as follows. First, we provide empirical evidence of a "race-to-the-courthouse" effect of ordered leniency: Mild and Strong Leniency induce the injurers to self-report promptly. These findings suggest that the injurers' behaviors are aligned with the risk-dominance refinement. Second, Mild and Strong Leniency significantly increase the likelihood of detection of harmful activities. This fundamental finding is explained by the high self-reporting rates under ordered-leniency policies. Third, as a result of the increase in the detection rates, the averages fines are significantly higher under Mild and Strong Leniency. As expected when the risk-dominance refinement is applied, Mild Leniency exhibits the highest average fine.

Suggested Citation

Landeo, Claudia M. and Spier, Kathryn E., Ordered Leniency: An Experimental Study of Law Enforcement with Self-Reporting (September 2018). NBER Working Paper No. w25094, Available at SSRN: https://ssrn.com/abstract=3258191

Claudia M. Landeo (Contact Author)

University of Alberta - Department of Economics ( email )

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Kathryn E. Spier

Harvard University - Law School - Faculty ( email )

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United States
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National Bureau of Economic Research (NBER)

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