How to Forecast Constitutional Court Decisions? Legal and Political Context in a Machine Learning Application

Abstract

Ex ante forecasting approaches using machine learning become increasingly popular to analyze and predict judicial outcomes. Yet, existing work on the prediction of court decision-making has two important limitations. First, it exclusively focuses on the US Supreme Court. This raises concerns about the external validity of previous stud- ies and their implications for courts in different law traditions. Second, none of the existing studies have explicitly tested the relative contribution of legal context versus political context factors to the forecast of court decisions. This study addresses these two points by ex ante predicting over 2,900 decisions of the German Federal Con- stitutional Court. I find that similar methodological approaches successfully applied to predict Supreme Court decisions also work for Kelsenian European constitutional court types. My results also show that the legal context of a decision is already a good predictor. However, the predictive performance is significantly improved when information about the political context of a decision is added. These findings therefore support the view of a multifaceted decision-making of constitutional courts which is best characterized by the ensemble of both legal and political factors.