IDEAS home Printed from https://ideas.repec.org/a/eee/irlaec/v62y2020ics0144818819303667.html
   My bibliography  Save this article

Text classification of ideological direction in judicial opinions

Author

Listed:
  • Hausladen, Carina I.
  • Schubert, Marcel H.
  • Ash, Elliott

Abstract

This paper draws on machine learning methods for text classification to predict the ideological direction of decisions from the associated text. Using a 5% hand-coded sample of cases from U.S. Circuit Courts, we explore and evaluate a variety of machine classifiers to predict “conservative decision” or “liberal decision” in held-out data. Our best classifier is highly predictive (F1 = .65) and allows us to extrapolate ideological direction to the full sample. We then use these predictions to replicate and extend Landes and Posner’s (2009) analysis of how the party of the nominating president influences circuit judge's votes.

Suggested Citation

  • Hausladen, Carina I. & Schubert, Marcel H. & Ash, Elliott, 2020. "Text classification of ideological direction in judicial opinions," International Review of Law and Economics, Elsevier, vol. 62(C).
    • Handle: RePEc:eee:irlaec:v:62:y:2020:i:c:s0144818819303667
    DOI: 10.1016/j.irle.2020.105903
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0144818819303667
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.irle.2020.105903?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Alberto Abadie, 2020. "Statistical Nonsignificance in Empirical Economics," American Economic Review: Insights, American Economic Association, vol. 2(2), pages 193-208, June.
    2. A. Colin Cameron & Jonah B. Gelbach & Douglas L. Miller, 2011. "Robust Inference With Multiway Clustering," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(2), pages 238-249, April.
    3. Benjamin E. Lauderdale & Tom S. Clark, 2014. "Scaling Politically Meaningful Dimensions Using Texts and Votes," American Journal of Political Science, John Wiley & Sons, vol. 58(3), pages 754-771, July.
    4. Ash, Elliott & Chen, Daniel L. & Lu, Wei, 2018. "Motivated Reasoning in the Field: Partisanship in Precedent, Prose, Vote, and Retirement in U.S. Circuit Courts, 1800-2013," TSE Working Papers 18-976, Toulouse School of Economics (TSE).
    5. Ash, Elliott & Chen, Daniel L., 2018. "Mapping the Geometry of Law using Document Embeddings," TSE Working Papers 18-935, Toulouse School of Economics (TSE).
    6. Hlavac, Marek, 2016. "ExtremeBounds: Extreme Bounds Analysis in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 72(i09).
    7. Xavier X. Sala-i-Martin, 1997. "I Just Ran Four Million Regressions," NBER Working Papers 6252, National Bureau of Economic Research, Inc.
    8. Sala-i-Martin, Xavier, 1997. "I Just Ran Two Million Regressions," American Economic Review, American Economic Association, vol. 87(2), pages 178-183, May.
    9. Jonathan B. Slapin & Sven‐Oliver Proksch, 2008. "A Scaling Model for Estimating Time‐Series Party Positions from Texts," American Journal of Political Science, John Wiley & Sons, vol. 52(3), pages 705-722, July.
    10. Laver, Michael & Benoit, Kenneth & Garry, John, 2003. "Extracting Policy Positions from Political Texts Using Words as Data," American Political Science Review, Cambridge University Press, vol. 97(2), pages 311-331, May.
    11. Segal, Jeffrey A. & Cover, Albert D., 1989. "Ideological Values and the Votes of U.S. Supreme Court Justices," American Political Science Review, Cambridge University Press, vol. 83(2), pages 557-565, June.
    12. Lauderdale, Benjamin E. & Herzog, Alexander, 2016. "Measuring Political Positions from Legislative Speech," Political Analysis, Cambridge University Press, vol. 24(3), pages 374-394, July.
    13. Martin, Andrew D. & Quinn, Kevin M., 2002. "Dynamic Ideal Point Estimation via Markov Chain Monte Carlo for the U.S. Supreme Court, 1953–1999," Political Analysis, Cambridge University Press, vol. 10(2), pages 134-153, April.
    14. Leamer, Edward E, 1985. "Sensitivity Analyses Would Help," American Economic Review, American Economic Association, vol. 75(3), pages 308-313, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Renáta Németh, 2023. "A scoping review on the use of natural language processing in research on political polarization: trends and research prospects," Journal of Computational Social Science, Springer, vol. 6(1), pages 289-313, April.
    2. Henrika Langen, 2022. "The Impact of the #MeToo Movement on Language at Court -- A text-based causal inference approach," Papers 2209.00409, arXiv.org, revised Sep 2023.
    3. Baumann, Florian & Fagan, Frank, 2023. "When more isn’t always better: The ambiguity of fully transparent judicial action and unrestricted publication rules," International Review of Law and Economics, Elsevier, vol. 75(C).
    4. Yıldırım, Engin & Sert, Mehmet Fatih & Kartal, Burcu & Çalış, Şuayyip, 2023. "Non-compliance of the European Court of Human Rights decisions: A machine learning analysis," International Review of Law and Economics, Elsevier, vol. 76(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Carmen Fernandez & Eduardo Ley & Mark F. J. Steel, 2001. "Model uncertainty in cross-country growth regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(5), pages 563-576.
    2. C. Alper & S. Cakici, 2009. "Financial Liberalization, Fiscal Prudence and Growth: Panel Evidence from 1980–2003," Open Economies Review, Springer, vol. 20(4), pages 509-524, September.
    3. Heijungs, Reinout & Groot, Henri L. F. de & Florax, Raymond J.G.M., 2001. "Metagrowth 1.0, a computer program for robustness analysis," Serie Research Memoranda 0031, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    4. Mónica D. Oliveira & Inês Mataloto & Panos Kanavos, 2019. "Multi-criteria decision analysis for health technology assessment: addressing methodological challenges to improve the state of the art," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 20(6), pages 891-918, August.
    5. Wang, Xinya & Lucey, Brian & Huang, Shupei, 2022. "Can gold hedge against oil price movements: Evidence from GARCH-EVT wavelet modeling," Journal of Commodity Markets, Elsevier, vol. 27(C).
    6. Diaf, Sami & Döpke, Jörg & Fritsche, Ulrich & Rockenbach, Ida, 2022. "Sharks and minnows in a shoal of words: Measuring latent ideological positions based on text mining techniques," European Journal of Political Economy, Elsevier, vol. 75(C).
    7. Schneider Ulrike & Wagner Martin, 2012. "Catching Growth Determinants with the Adaptive Lasso," German Economic Review, De Gruyter, vol. 13(1), pages 71-85, February.
    8. Danila Serra, 2006. "Empirical determinants of corruption: A sensitivity analysis," Public Choice, Springer, vol. 126(1), pages 225-256, January.
    9. Mallick, Debdulal, 2012. "The role of the elasticity of substitution in economic growth: A cross-country investigation," Labour Economics, Elsevier, vol. 19(5), pages 682-694.
    10. Christopher Wratil & Sara B Hobolt, 2019. "Public deliberations in the Council of the European Union: Introducing and validating DICEU," European Union Politics, , vol. 20(3), pages 511-531, September.
    11. Garett Jones & W. Schneider, 2006. "Intelligence, Human Capital, and Economic Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach," Journal of Economic Growth, Springer, vol. 11(1), pages 71-93, March.
    12. Diego Winkelried & Luis A. Iberico, 2018. "Calendar effects in Latin American stock markets," Empirical Economics, Springer, vol. 54(3), pages 1215-1235, May.
    13. Tjeerd M. Boonman & Andrea E. Sanchez Urbina, 2020. "Extreme Bounds Analysis in Early Warning Systems for Currency Crises," Open Economies Review, Springer, vol. 31(2), pages 431-470, April.
    14. Krzysztof Beck, 2016. "Business Cycle Synchronization In European Union: Regional Perspective," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 11(4), pages 785-815, December.
    15. Leopoldo Fergusson & Carlos Molina & Juan Felipe Riaño, 2018. "I Sell My Vote, and So What? Incidence, Social Bias, and Correlates of Clientelism in Colombia," Economía Journal, The Latin American and Caribbean Economic Association - LACEA, vol. 0(Fall 2018), pages 181-218, November.
    16. Rup Singh, 2015. "Forces of economic growth in China, India, and other Asian countries," Asian-Pacific Economic Literature, The Crawford School, The Australian National University, vol. 29(1), pages 62-81, May.
    17. Chambers, Dustin & O'Reilly, Colin, 2022. "Regulation and income inequality in the United States," European Journal of Political Economy, Elsevier, vol. 72(C).
    18. Freire, Danilo & Uzonyi, Gary, 2018. "What Drives State-Sponsored Violence?: Evidence from Extreme Bounds Analysis and Ensemble Learning Models," SocArXiv pzx3q, Center for Open Science.
    19. Guo, Li-Yang & Feng, Chao & Yang, Jun, 2022. "Can energy predict the regional prices of carbon emission allowances in China?," International Review of Financial Analysis, Elsevier, vol. 82(C).
    20. Rao, B. Bhaskara & Cooray, Arusha, 2010. "Determinants of the long-run growth rate in the South-Asian countries," MPRA Paper 26493, University Library of Munich, Germany.

    More about this item

    Keywords

    Judge ideology; Circuit courts; Text data; NLP;
    All these keywords.

    JEL classification:

    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • K0 - Law and Economics - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:irlaec:v:62:y:2020:i:c:s0144818819303667. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/irle .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.