JOURNAL ARTICLE

Law Matters—Less Than We Thought.

  • Published In: Journal of Law, Economics & Organization, 2024, v. 40, n. 1. P. 108 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Klerman, Daniel; Spamann, Holger 3 of 3

Abstract

This article reports on a pre-registered 2×2×2 factorial randomized lab experiment involving 61 federal judges to assess whether legal rules influence judicial decisions, whether rules constrain more than standards, and whether legally irrelevant sympathies affect outcomes. Judges decided a realistic fictitious auto accident case with varying forum (Wyoming or South Dakota), accident location/common domicile (Kansas or Nebraska), and sympathy toward plaintiff or defendant. The study found only weak evidence that law influences decisions, with judges following clear legal rules (lex loci delicti) about 77% of the time under a rule-based forum (Wyoming) and less than 50% under a standard-based forum (South Dakota), and no evidence that sympathy affected rulings. The experiment’s design manipulated intra-U.S. geographic elements to induce legal variation realistically, but results suggest judges often deviated from legal directives, sometimes avoiding damage caps or favoring traditional choice-of-law rules regardless of the applicable standard. These findings align with prior research indicating limited internal force of law in judicial decision-making absent external enforcement mechanisms like appeals or public scrutiny.

Additional Information

  • Source:Journal of Law, Economics & Organization. 2024/03, Vol. 40, Issue 1, p108
  • Document Type:Article
  • Subject Area:Law
  • Publication Date:2024
  • ISSN:8756-6222
  • DOI:10.1093/jleo/ewac008
  • Accession Number:175522607
  • Copyright Statement:Copyright of Journal of Law, Economics & Organization is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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