Man vs. Machine: A Novel Evaluation of Data Analytics Using Occupational Licensing as a Case Study

Abstract

For researchers of state regulatory policy, the difficulty of gathering data has long presented an obstacle. This study compares two new databases for state-level occupational licensing laws. The Knee Center for the Study of Occupational Regulation (CSOR) database uses traditional manual reading to gather data, while RegData uses a machine learning algorithm. We describe both data-gathering processes, weigh their costs and benefits, and compare their outputs. The CSOR database allows researchers to find specific licensing requirements typically used in the occupational licensing literature, but the traditional methodology is time and labor intensive. RegData provides researchers with a better overall measure of stringency and complexity in regulation that allows for comparisons across states. However, RegData cannot reach the level of detail in the CSOR database. The variables gathered by CSOR and RegData are useful for researchers and policymakers and can be used as a model to build databases for other state-level regulations.

Keywords: complexity of law, data collection, occupational licensing, RegData

JEL Classification: C81, C63, J44