PT - JOURNAL ARTICLE AU - Sharyn O’Halloran AU - Sameer Maskey AU - Geraldine McAllister AU - David K. Park AU - Kaiping Chen TI - Data Science and Political Economy: Application to Financial Regulatory Structure AID - 10.7758/RSF.2016.2.7.06 DP - 2016 Nov 01 TA - RSF: The Russell Sage Foundation Journal of the Social Sciences PG - 87--109 VI - 2 IP - 7 4099 - http://www.rsfjournal.org/content/2/7/87.short 4100 - http://www.rsfjournal.org/content/2/7/87.full AB - The development of computational data science techniques in natural language processing and machine learning algorithms to analyze large and complex textual information opens new avenues for studying the interaction between economics and politics. We apply these techniques to analyze the design of financial regulatory structure in the United States since 1950. The analysis focuses on the delegation of discretionary authority to regulatory agencies in promulgating, implementing, and enforcing financial sector laws and overseeing compliance with them. Combining traditional studies with the new machine learning approaches enables us to go beyond the limitations of both methods and offer a more precise interpretation of the determinants of financial regulatory structure.