Abstract

Emotions are said to underlie moral decision-making. We detect intra-judge variation spanning three decades in 1.5 million judicial decisions driven by factors unrelated to case merits. U.S. immigration judges grant an additional 1.4 % points of asylum petitions–and U.S. district judges assign 0.6 % points fewer prison sentences and 5 % longer probation sentences—on the day after their city's NFL team won, relative to days after the team lost. Bad weather has the opposite effect of a team win. Unrepresented parties in asylum bear the brunt of NFL effects. The effect on district judges only appears for judges born in the same state as the current state of residence, providing clean evidence of extraneous influences on judge decision-making as opposed to lawyer or applicant behavior. Moving beyond OLS, we utilize models from machine learning to estimate the sentence length relative to the sentencing guideline. We find that while several appropriate features predict sentence length, such as details of the crime committed, other features seemingly unrelated, including daily temperature, sport game scores, and location of trial, are predictive as well. The predictive power of the unrelated events is derived from the permutation based variable importance score in random forests. We address recent criticism of the reliability of these scores with double residualization.

Keywords

Replaces

Daniel L. Chen, and Markus Loecher, “Mood and the Malleability of Moral Reasoning: The Impact of Irrelevant Factors on Judicial Decisions”, IAST Working Paper, n. 16-49, 2016, revised September 2019.