Industrial policy data here.
Working Paper
Measuring Industrial Policy: A Text-Based Approach
By Réka Juhász, Nathan Lane, Emily Oehlsen, Verónica C. Pérez (2025)
Figure 1. Model Classification
Figure 2. Time Trend of Industrial Policy
Figure 3. The Instruments of Industrial Policy
Overview
We develop a text-based approach to industrial policy (IP). We train and validate a Large Language Model (LLM), more specifically, BERT, to identify industrial policies based on information contained in policy text. 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).
Our data suggests that industrial policy:
is important and is on the rise.
uses subsidies and export promotion measures as opposed to tariffs.
used heavily by high-income economies.
tends to target sectors with an established comparative advantage, particularly in high-income countries.
We present here the key takeaways from our working paper, along with the data behind each figure. You can read the full Working Paper here, and access our main dataset here.
License
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International CC BY-NC-SA 4.0
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Reference
Juhász, Réka and Lane, Nathaniel and Oehlsen, Emily and Pérez, Verónica C., Measuring Industrial Policy: A Text-Based Approach (May 20, 2025). Available at SSRN: https://ssrn.com/abstract=5262841 or http://dx.doi.org/10.2139/ssrn.5262841
Acknowledgements
We gratefully acknowledge the use of the Global Trade Alert (GTA) dataset, which the GTA team kindly shared with us. We are particularly thankful to Johannes Fritz for helpful discussions about the GTA project.
