Accuracy and Preferences for Legal Error

31 Pages Posted: 14 Dec 2020 Last revised: 22 Dec 2023

See all articles by Murat C. Mungan

Murat C. Mungan

Texas A&M University School of Law

Marie Obidzinski

Université Paris II - Panthéon-Assas

Yves Oytana

Université de Bourgogne

Date Written: December 11, 2020

Abstract

We study the interactions between accuracy and standards used in the determination of legal liability. First, we show that accuracy and type-1 errors (wrongful findings of liability) must reduce each other's effectiveness in mitigating optimal type-2 errors (wrongful failures to assign liability) for previous results in the literature to hold. When this condition holds, for major crimes the median voter's tolerance for type-1 errors is reduced as the legal system's accuracy increases. However, this relationship need not hold for minor offenses. Our analysis also reveals that legal processes that emerge under electoral pressures convict more often than is optimal but less often than necessary to maximize deterrence. Moreover, when the median voter's preferences are implemented, an increase in accuracy can counter-intuitively reduce welfare.

Keywords: Crime, deterrence, legal errors, accuracy, standard of proof, election

JEL Classification: K4

Suggested Citation

Mungan, Murat C. and Obidzinski, Marie and Oytana, Yves, Accuracy and Preferences for Legal Error (December 11, 2020). American Law and Economics Review, Forthcoming, George Mason Law & Economics Research Paper No. 20-33, Texas A&M University School of Law Legal Studies Research Paper No. 24-02, Available at SSRN: https://ssrn.com/abstract=3747287 or http://dx.doi.org/10.2139/ssrn.3747287

Murat C. Mungan (Contact Author)

Texas A&M University School of Law

1515 Commerce St.
Fort Worth, TX Tarrant County 76102
United States

Marie Obidzinski

Université Paris II - Panthéon-Assas ( email )

12 place du Pantheon
Paris cedex 06, 75231
France

Yves Oytana

Université de Bourgogne ( email )

Boulevard Gabriel
21066 Dijon Cedex, Bourgogne 21000
United States

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