Measuring Industrial Policy: A Text-Based Approach
Réka Juhász, Nathan Lane, Emily Oehlsen, and Verónica C. Pérez
We provide a new, text-based approach to measuring industrial policy at scale and deliver a global data set on industrial policy practice.
Abstract
Since the 18th century, policymakers have debated the merits of industrial policy (IP). Yet, economists lack basic facts about its use due to measurement challenges. We propose a new approach to IP measurement based on information contained in policy text. We show how off-the-shelf supervised machine learning tools can be used to categorize industrial policies at scale. Using this approach, we validate long-standing concerns with earlier measurement approaches that conflate IP with other types of policy. We apply our methodology to a global database of commercial policy descriptions, and provide a first look at IP use at the country, industry, and year levels (2010-2022). The new data on IP suggest that i) IP is on the rise; ii) modern IP tends to use subsidies and export promotion measures as opposed to tariffs; iii) rich countries heavily dominate IP use; iv) IP tends to target sectors with an established comparative advantage, particularly in high-income countries.
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Related Work
This paper is part of the Industrial Policy Group’s broader research agenda on understanding and measuring industrial policies globally. See our main project page for more information.