Political Language in Economics

Abstract

Does academic writing in economics reflect the political orientation of economists? We use
machine learning to measure partisanship in published academic economics articles. We predict
observed political behavior of a subset of economists using the phrases from their academic articles, show good out-of-sample predictive accuracy, and then predict partisanship for all economists. We then use these predictions to examine patterns of political language in economics. We estimate journal-specific effects on predicted ideology, controlling for author and year fixed effects, that accord with existing survey-based measures. We show considerable sorting of economists into fields of research by predicted partisanship. We also show that partisanship is detectable even within fields, even across those estimating the same theoretical parameter. Using policy-relevant parameters collected from previous meta-analyses, we then show that imputed partisanship is correlated with estimated parameters, such that the implied policy prescription is consistent with partisan leaning. For example, we find that going from the most left-wing authored estimate of the taxable top income elasticity to the most right-wing authored estimate decreases the optimal tax rate from 84% to 58%.