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Predicting and understanding law-making with word vectors and an ensemble model

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  • John J Nay
Abstract
Out of nearly 70,000 bills introduced in the U.S. Congress from 2001 to 2015, only 2,513 were enacted. We developed a machine learning approach to forecasting the probability that any bill will become law. Starting in 2001 with the 107th Congress, we trained models on data from previous Congresses, predicted all bills in the current Congress, and repeated until the 113th Congress served as the test. For prediction we scored each sentence of a bill with a language model that embeds legislative vocabulary into a high-dimensional, semantic-laden vector space. This language representation enables our investigation into which words increase the probability of enactment for any topic. To test the relative importance of text and context, we compared the text model to a context-only model that uses variables such as whether the bill’s sponsor is in the majority party. To test the effect of changes to bills after their introduction on our ability to predict their final outcome, we compared using the bill text and meta-data available at the time of introduction with using the most recent data. At the time of introduction context-only predictions outperform text-only, and with the newest data text-only outperforms context-only. Combining text and context always performs best. We conducted a global sensitivity analysis on the combined model to determine important variables predicting enactment.

Suggested Citation

  • John J Nay, 2017. "Predicting and understanding law-making with word vectors and an ensemble model," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-14, May.
    • Handle: RePEc:plo:pone00:0176999
    DOI: 10.1371/journal.pone.0176999
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    References listed on IDEAS

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    1. John Wilkerson & David Smith & Nicholas Stramp, 2015. "Tracing the Flow of Policy Ideas in Legislatures: A Text Reuse Approach," American Journal of Political Science, John Wiley & Sons, vol. 59(4), pages 943-956, October.
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    Cited by:

    1. Amedeo Santosuosso & Giulia Pinotti, 2020. "Bottleneck or Crossroad? Problems of Legal Sources Annotation and Some Theoretical Thoughts," Stats, MDPI, vol. 3(3), pages 1-20, September.
    2. Hsu, Sara & Fan, Zhihao, 2022. "Policy and media forces that shape the creation of Chinese state-owned enterprise policies," Journal of Policy Modeling, Elsevier, vol. 44(6), pages 1232-1250.

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