Import price elasticities: reconsidering the evidence
Bibliographic record
Abstract
Recent economic geography and trade empirical studies based on monopolistic competition suggest high levels of trade price elasticities (between 3 and 11). However, price elasticity estimations in trade equations using unit values as price proxies usually lead to lower values of around unity. We show that those inconclusive results may be due to some misspecification in these equations as well as measurement errors in prices. When suitable instrumental variables are used, within a panel of industrialized countries, we obtain high price elasticities, the majority ranging from 1 to 13. The highest estimates correspond to industries producing homogeneous goods. JEL classification: C2, C3 and F1. Les élasticités prix des importations : un nouveau coup d’oeil aux résultats. Plusieurs études empiriques récentes fondées sur des modèles de concurrence monopolistique exhibent des élasticités prix estimées des échanges élevées (entre 3 et 11). Or, la plupart des élasticités prix estimées dans la littérature approchant les prix par des valeur unitaires sont plus faibles, de l’ordre de 1. Nous montrons que ces résultats non concluants pourraient provenir d’une mauvaise spécification des équations d’échanges ou d’erreurs de mesure sur les prix. Quand ceux‐ci sont correctement instrumentés, sur un panel de pays industrialisés, nous obtenons des élasticités prix élevées (de 1 à 13). Les plus fortes correspondent aux secteurs produisant des biens homogènes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".