The Unequal Reach of Transnational Legal Institutions: Mapping, Predicting and Explaining Spatial Disparities in EU Law Use

Abstract

We investigate spatial disparities in the use of supranational law using a new dataset compiling the geographic coordinates of the entire universe of courts that have passed on cases to the European Court of Justice over the lifetime of the EU. We adopt a data-driven approach which integrates exploration, prediction and explanation into a single research design. Our cartography reveals that involvement in the preliminary ruling procedure tends to be concentrated in a relatively small subset of regions within member states. Next, applying machine learning methods, we find that the presence of organizations like peak courts and infrastructures like large cargo ports constitute strong predictors of EU law use. So too are capital cities and EU trademark registrations. Finally, we rationalize these findings using text mining and additional empirical tests. We argue that spatial variations are induced by the team structure of the EU court system and the division of labour within national judiciaries as well as by the resources and geographic location of litigants.

Keywords: Courts; machine learning ; text-mining; inductive design; geography

JEL Classification: K40