Pandemic Policymaking: Learning the Lower Dimensional Manifold of Congressional Responsiveness

TL;DR

This study uses manifold learning on bill-level data to analyze pandemic policymaking, revealing that COVID-19 policymaking closely resembles past crises, indicating consistent congressional responses over time despite political polarization.

Contribution

It introduces a novel application of high-dimensional manifold learning to understand the structure of pandemic policymaking based solely on bill characteristics.

Findings

COVID-19 policymaking is similar to past crises.

Policymaking shows uniformity despite political polarization.

No significant evolution in pandemic policymaking over time.

Abstract

A recent study leveraging text of pandemic-related policymaking from 1973--2020 explored whether pandemic policymaking has evolved, or whether we are witnessing a new, unique era of policymaking as it relates to large-scale crises like COVID-19. This research picks up on this approach over the same period of study and based on the same data, but excluding text. Instead, using high dimensional manifold learning, this study explores the latent structure of the pandemic policymaking space based only on bill-level characteristics. Results indicate the COVID-19 era of policymaking maps extremely closely onto prior periods of related policymaking. This suggests that there is less of an "evolutionary trend" in pandemic policymaking, where instead there is striking uniformity in Congressional policymaking related to these types of large-scale crises, despite being in a unique era of hyperpolarization, division, and ineffective governance.