Concepts and measures of bureaucratic constraints in European Union laws from hand-coding to machine-learning

Scholars employ two main measures of the executive constraints embedded in European Union laws: one is based on the variation in the use of different types of restrictions, and the second is based on the frequency of such use. They reflect two alternative conceptualizations of bureaucratic control. We label them, respectively, as the “toolbox perspective” and the “design perspective”. We illustrate that the constraint frequency measure poses fewer validity problems in estimating legislators' intent to constrain implementation and tends to produce less severe measurement errors. We then evaluate the performance in estimating constraint variation of a recent computational application and identify potential drawbacks of automated learning from hand-coded provisions. We lastly introduce a skeletal framework for a machine-learning approach based on the syntactic structures employed by legislators that could improve the performance of this innovative technique.