Predictive Effect of ideological distance/popularity Opposition on petitioner’s success

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


Ex ante forecasting approaches become increasingly used to analyze and predict judicial outcomes, reaching impressive forecasting performances. 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 these studies 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 variables to the forecast of court decisions. This study addresses these two points by ex ante predicting over 2,900 proceedings decided by the German Federal Constitutional Court between 1974 and 2010, using only information available prior to the respective decision. The findings show that similar methodological approaches successfully applied to the Supreme Court also work when being applied to a Kelsenian European constitutional court. The results also demonstrate that legal context alone is already a good predictor for the outcome of a case. Most importantly, the predictive performance is significantly improved when information about the political context of a decision is added. These findings are important because they support the view of a multifaceted decision-making of constitutional courts which is best characterized by the ensemble of both legal and political factors.

Working Paper