Measuring Industrial Policy: A Text Based Approach
Bibliographic record
Abstract
Since the 18th century, policymakers have debated the merits of industrial policy (IP). Yet, economists lack basic facts about its use. This study sheds light on industrial policy by measuring and studying global policy practice for the first time. We first create an automated classification algorithm for categorizing industrial policy practice from text. We then apply it to a global database of commercial policy descriptions and quantify policy use at the country, industry, and year levels (2009-2020). These data allow us to study fundamental policy patterns across the world. We highlight four findings. First, IP is common (25% of policies in our database) and has expanded since 2010. Second, instead of blunt tariffs, IP is granular and technocratic. Countries tend to use subsidies and export promotion measures, often targeted at individual firms. Third, the countries engaged most in IP tend to be wealthier (top income quintile) liberal democracies. In our data, IP is rarer among the poorest nations (bottom quintile). Fourth, IP is targeted toward a subset of industries and is highly correlated with an industry’s revealed comparative advantage. We show that industrial policy is a prominent feature of the global economy and a far cry from industrial policies of the past.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.036 | 0.028 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".