Legislative-Data

State legislatures copy each other, and measuring that means finding bills that share text and picking a cutoff. One CC0 corpus of state bills got its cutoff from a parenthetical example, and it stuck. Classifying all 2,968,117 pairs above it, 785,330 are page furniture rather than legislative text, and the most connected pair of states in the data is 99.9% two PDFs agreeing about their printer settings. That part is fixable. The second finding is not: two states implementing the same federal statute share text word for word without either copying the other, and no score can tell that apart from real diffusion.

We test three ML models on 48K congressional bills to see how well they can predict policy areas from bill text. Results show logistic regression performs best, with a certified weighted-F1 of ~0.88 within-Congress (0.877) and ~0.87 out-of-Congress (0.871).

Only 2% of congressional bills become law. We analyze 15K bills from 2021-2023 to understand what drives legislative success and failure.

A 47,000-bill dataset from Congress.gov with a co-sponsorship graph and TF-IDF baselines for 33-class policy-area classification, at 0.88 weighted F1. On Hugging Face.