Quantitative Restrictions and Quality Upgrading: The Case of the Multi-Fibre Agreement
Bibliographic record
Abstract
The thesis uses the end of the Multi-Fibre Agreement to test the theory of quality-upgrading, which states that firms facing quotas will export higher-quality products to generate the greatest economic rent from each individual exported good. The Multi-Fibre Agreement (MFA) is a set of quantitative restrictions placed by the US on textile exports from Asian nations that began in 1974 and ended January 1, 2005. The thesis constructs a hedonic regression to isolate the quality and price characteristics of goods exported from China, and it creates a fixed effects regression to estimate the impact of quotas on the quality and price of goods exported. The study finds that the end of the MFA led to a 9 percent decrease in the quality of goods exported in previously restricted groups and a 26 percent decrease in the price of goods exported from China. Furthermore, it is the first study to evaluate how the reinstatement of ex ante textile quotas in 2006 impacted the price and quality of Chinese goods exported and found the resulting increase in quality to be 19 percent and the increase in price to be 21 percent (all statistically significant at the 1 percent level). The thesis also generates a theory for how quotas on Chinese textiles lead to a decrease in quality of goods produced by firms in nations that do not themselves face quotas on exports (specifically Canada,
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".