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Network Analysis and the Law: Measuring the Legal Importance of Precedents at the U.S. Supreme Court

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  • Fowler, James H.
  • Johnson, Timothy R.
  • Spriggs, James F.
  • Jeon, Sangick
  • Wahlbeck, Paul J.

Abstract

We construct the complete network of 26,681 majority opinions written by the U.S. Supreme Court and the cases that cite them from 1791 to 2005. We describe a method for using the patterns in citations within and across cases to create importance scores that identify the most legally relevant precedents in the network of Supreme Court law at any given point in time. Our measures are superior to existing network-based alternatives and, for example, offer information regarding case importance not evident in simple citation counts. We also demonstrate the validity of our measures by showing that they are strongly correlated with the future citation behavior of state courts, the U.S. Courts of Appeals, and the U.S. Supreme Court. In so doing, we show that network analysis is a viable way of measuring how central a case is to law at the Court and suggest that it can be used to measure other legal concepts.

Suggested Citation

  • Fowler, James H. & Johnson, Timothy R. & Spriggs, James F. & Jeon, Sangick & Wahlbeck, Paul J., 2007. "Network Analysis and the Law: Measuring the Legal Importance of Precedents at the U.S. Supreme Court," Political Analysis, Cambridge University Press, vol. 15(3), pages 324-346, July.
    • Handle: RePEc:cup:polals:v:15:y:2007:i:03:p:324-346_00
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    Citations

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    Cited by:

    1. Bokwon Lee & Kyu-Min Lee & Jae-Suk Yang, 2019. "Network structure reveals patterns of legal complexity in human society: The case of the Constitutional legal network," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-15, January.
    2. Amadxarif, Zahid & Brookes, James & Garbarino, Nicola & Patel, Rajan & Walczak, Eryk, 2019. "The language of rules: textual complexity in banking reforms," Bank of England working papers 834, Bank of England.
    3. Monika Stachowiak-Kudła & Janusz Kudła, 2023. "Measuring the prestige of administrative courts," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(4), pages 3637-3662, August.
    4. Squintani, Francesco, 2024. "Persuasion in Networks," The Warwick Economics Research Paper Series (TWERPS) 1520, University of Warwick, Department of Economics.
    5. Ryan C. Black & James F. Spriggs, 2013. "The Citation and Depreciation of U.S. Supreme Court Precedent," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 10(2), pages 325-358, June.
    6. Kazutaka Takechi, 2023. "How are the precedents of trade policy rules made under the World Trade Organization?," Economics and Politics, Wiley Blackwell, vol. 35(3), pages 806-821, November.
    7. Squintani, Francesco, 2024. "Persuasion in Networks," CRETA Online Discussion Paper Series 88, Centre for Research in Economic Theory and its Applications CRETA.
    8. Grajzl, Peter & Murrell, Peter, 2024. "Caselaw and England's economic performance during the Industrial Revolution: Data and evidence," Journal of Comparative Economics, Elsevier, vol. 52(1), pages 145-165.
    9. John Szmer & Robert K. Christensen & Samuel Grubbs, 2020. "What influences the influence of U.S. Courts of Appeals decisions?," European Journal of Law and Economics, Springer, vol. 49(1), pages 55-81, February.
    10. Sandeep Soni & Kristina Lerman & Jacob Eisenstein, 2021. "Follow the leader: Documents on the leading edge of semantic change get more citations," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(4), pages 478-492, April.
    11. Youjin Lee & Ashley L. Buchanan & Elizabeth L. Ogburn & Samuel R. Friedman & M. Elizabeth Halloran & Natallia V. Katenka & Jing Wu & Georgios K. Nikolopoulos, 2023. "Finding influential subjects in a network using a causal framework," Biometrics, The International Biometric Society, vol. 79(4), pages 3715-3727, December.
    12. Bommarito, Michael J. & Katz, Daniel Martin & Zelner, Jonathan L. & Fowler, James H., 2010. "Distance measures for dynamic citation networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(19), pages 4201-4208.

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