  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.
