Local Content Requirements: A Global Problem
Bibliographic record
Abstract
In the wake of the Great Recession of 2008–09, economists feared that protectionist policies might sweep the world economy, echoing the wave of tariff escalations during the Great Depression of the 1930s. To some surprise, officials were more restrained and largely avoided traditional forms of protection (tariffs and quotas). As a result, economists underestimated the incidence of new protectionism because policymakers increasingly turned to more opaque behind-the-border nontariff barriers (NTBs). Using a combination of statistical analysis and case studies, the authors show that local content requirements (LCRs), a form of NTB, have become increasingly popular. How much was global trade actually reduced on account of LCRs? A conservative estimate might be $93 billion. Case studies featured cover the healthcare sector in Brazil, wind turbines in Canada, the automobile industry in China, solar cells and modules in India, oil and gas in Nigeria, and Buy American restrictions on government procurement.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.036 | 0.011 |
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".