RT Journal Article
SR Electronic
T1 Data Science and Political Economy: Application to Financial Regulatory Structure
JF RSF: The Russell Sage Foundation Journal of the Social Sciences
FD Russell Sage Foundation
SP 87
OP 109
DO 10.7758/RSF.2016.2.7.06
VO 2
IS 7
A1 Sharyn O’Halloran
A1 Sameer Maskey
A1 Geraldine McAllister
A1 David K. Park
A1 Kaiping Chen
YR 2016
UL http://www.rsfjournal.org/content/2/7/87.abstract
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.