I’m a second year PhD student in economics at Boston University. My research focuses on economic history, political economy, development and growth. I’m interested in applying machine learning and text analysis methods to economic research.
I hold a BSc and MA in economics from Universidad de los Andes. I previously worked as a predoctoral fellow at PER. I’m originally from Montería, Córdoba, Colombia.
Although questions surrounding industrial policy are fundamental, we lack both measures and comprehensive data on industrial policy. Consequently, scholars and practitioners lack a systematic picture of industrial policy practice. This paper provides a new, text-based approach to measuring industrial policy. We take the tools of supervised machine learning to a comprehensive, English-language database of economic policy to construct measures of industrial policy at the country, industry, and year level. We use this data to establish four fundamental facts about global industrial policy from 2009 to 2020. First, IP is common (25 percent of policies in our database) and has been trending upward since 2010. Second, industrial policy is technocratic and granular, taking the form of subsidies and export promotion measures targeted at individual firms, instead of tariffs. Third, the countries engaged most in IP tend to be wealthier (top income quintile) liberal democracies, and IP is very rare among the poorest nations (bottom quintile). Fourth, IP tends to be targeted toward a small share of industries, and targeting is highly correlated with an industry’s revealed comparative advantage. Thus, we find contemporary practice is a far cry from industrial policy’s past and tends toward selective, export-oriented policies used by the world’s most developed economies.
@techreport{JLOP,title={The Who, What, When, and How of Industrial Policy: A Text-based Approach.},author={Juhász, R. and Lane, N. and Oehlsen, E. and Pérez, V.C.},year={2022},month=aug,}
