Computational analysis of US Congressional speeches reveals a shift from evidence to intuition

TL;DR

The paper introduces the Evidence-Minus-Intuition (EMI) score to quantify how Congress speaks about truth, operationalizing it via dictionary-based embeddings trained on 8.4 million speeches. EMI declines from its mid-20th century peak in the 1970s and tracks rising polarization and income inequality, while contemporaneously showing a positive association with congressional productivity. The findings imply that a shift away from evidence-based rhetoric coincides with democratic health concerns and that EMI may precede inequality shifts, offering a measurable lens for monitoring political discourse and guiding interventions to foster evidence-based policymaking.

Abstract

Pursuit of honest and truthful decision-making is crucial for governance and accountability in democracies. However, people sometimes take different perspectives of what it means to be honest and how to pursue truthfulness. Here we explore a continuum of perspectives from evidence-based reasoning, rooted in ascertainable facts and data, at one end, to intuitive decisions that are driven by feelings and subjective interpretations, at the other. We analyze the linguistic traces of those contrasting perspectives in Congressional speeches from 1879 to 2022. We find that evidence-based language has continued to decline since the mid-1970s, together with a decline in legislative productivity. The decline was accompanied by increasing partisan polarization in Congress and rising income inequality in society. Results highlight the importance of evidence-based language in political decision-making.

Figures (9)

• Figure 1: Time series of Evidence-Minus-Intuition (EMI) score in each congressional session between 1879 and 2022 (A), EMI scores separated by party (B), congressional polarization and inequality (C) and congressional productivity, measured as the Major Legislation Index (MLI) and the number of public laws passed by each session (D). We compute bootstrapping 95% CIs for EMI with 10000 samples, which may appear too small to be visible due to the large sample size.
• Figure 2: Inequality measured as the share of income of the top 1 % versus the EMI score in the previous legislature. The shaded area shows a LOESS fit and labels indicate the year corresponding to the inequality measurement.
• Figure 3: EMI score versus Congressional productivity measured as MLI (left), LPI (center), and log-transformed number of laws (right). Points are colored according to public mood towards regulation during the legislative period and gray lines and shaded areas show linear regression models of each productivity variable as a function of EMI alone.
• Figure S1: Number of speeches across both House and Senate for Congressional sessions between 1879 and 2022
• Figure S2: Time series of EMI by party in the U.S. Senate and House