How Predictable is Your State? Leveraging Lexical and Contextual Information for Predicting Legislative Floor Action at the State Level

TL;DR

This paper develops models to predict legislative floor action at the U.S. state level using lexical and contextual features, revealing key factors influencing legislative success and improving prediction accuracy by 18%.

Contribution

It introduces a comprehensive approach combining lexical content and contextual features to predict state legislative outcomes, filling a gap in state-level legislative analysis.

Findings

Lexical and contextual features both significantly improve prediction accuracy.

Models achieve an average 18% accuracy improvement over state-specific baselines.

Signals from content and context are complementary in predicting legislative success.

Abstract

Modeling U.S. Congressional legislation and roll-call votes has received significant attention in previous literature. However, while legislators across 50 state governments and D.C. propose over 100,000 bills each year, and on average enact over 30% of them, state level analysis has received relatively less attention due in part to the difficulty in obtaining the necessary data. Since each state legislature is guided by their own procedures, politics and issues, however, it is difficult to qualitatively asses the factors that affect the likelihood of a legislative initiative succeeding. Herein, we present several methods for modeling the likelihood of a bill receiving floor action across all 50 states and D.C. We utilize the lexical content of over 1 million bills, along with contextual legislature and legislator derived features to build our predictive models, allowing a comparison of the factors that are important to the lawmaking process. Furthermore, we show that these signals hold complementary predictive power, together achieving an average improvement in accuracy of 18% over state specific baselines.