Import Price-Elasticities: Reconsidering the Evidence # By Hélène Erkel-Rousse * and Daniel Mirza** Second revised draft for the Canadian Journal of Economics
Bibliographic record
Abstract
Recent economic geography and trade empirical studies based on monopolistic competition [Hanson, 1998; Head and Ries, 1999; Hummels, 1999], suggest high levels of trade price elasticities (between 3 and 11). However, direct estimations of price-elasticities in trade equations, using price indexes at aggregate or industry levels, usually lead to much lower values (around unity). In this paper, we show that those inconclusive results may be due to an econometric misspecification of these equations, as well as measurement errors in import price indexes. We re-estimate import price-elasticities from gravity-like equations using methods of transformed least squares and instrumental variables. Our study is based on compatible bilateral trade and activity data from the OECD and INSEE 1 for 14 importing countries, 16 trading partners, 27 industries and 23 years. When suitable instrumental variables are used, we find relatively high price-elasticities, in majority ranging from 1 to 13, the highest estimates corresponding to industries producing homogeneous goods. These results support recent studies on substitution elasticity estimates using monopolistic competition. They are also consistent with finite markup evaluations in the literature as well as reasonable estimates of the elasticity of trade costs with respect to distance.
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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 | 0.008 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".