Protection for sale with imperfect
Bibliographic record
Abstract
Abstract. Grossman and Helpman (1994) explain tariffs as the outcome of a lobbying process. In most empirical implementations of this framework protection is instead mea-sured using non-tariff barriers. Since tariffs allow the government to fully capture the rents from protection, while non-tariff barriers do not, the existing parameter estimates of the protection for sale model are likely to be biased. To address this problem, we augment the framework by considering instruments that allow partial capturing. Our specification is supported by the data, where we find that only 72–75 % of the rent from protection is appropriated by the government. JEL classification: F13 Protection a ̀ vendre quand la rente est imparfaitement capturée. Grossman et Helpman (1994) expliquent les tarifs douaniers comme la résultante d’un jeu de lobbying. La plupart des usages de ce cadre d’analyse ont porte ́ sur des barrières non-tarifaires pour mesurer le degre ́ de protection. Les tarifs douaniers permettent au gouvernement de capturer pleinement les rentes, ce n’est pas le cas pour les barrières non tarifaires. En conséquence, cela peut engendrer des estimations fautives des paramètres. Pour s’attaquer a ̀ ce problème, les auteurs enrichissent le modèle original en considérant explicitement des instruments
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.061 | 0.005 |
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".