Pricing‐to‐Market: Simple Theoretical Insights, Formidable Econometric Challenges
Bibliographic record
Abstract
Pricing‐to‐market (PTM) arises when firms endowed with market power alter their pricing decisions in response to exchange rate changes. I argue that the empirical models commonly used in the literature are not properly specified, especially the ones pertaining to agri‐food trade. The nature of the relation between the export price and the exchange rate can be influenced by the time‐series properties of the data, by the choice of currency to be used for invoicing, by menu costs which posit that it is costly for firms to change prices, and by the production technology of the products traded. With menu costs, only large enough exchange rate changes can trigger systematic changes in export prices. The empirical implication is to test for threshold effects. Constant return to scale is analytically and empirically convenient, as it is a necessary assumption to support the typical single‐equation PTM specification. When goods are produced under increasing returns, there are also domestic PTM effects, and a system of equations is required to properly analyze the impacts of the exchange rate on pricing decisions at home and abroad. Jointness in production, so prevalent in the agri‐food sector, can support so‐called perverse PTM effects, even when firms maximize profits and there are no switching costs. Il y a tarification en fonction du marché (TFM) quand des entreprises exerçant une influence sur le marché modifient leurs prix d'après les fluctuations du cours du change. L'auteur soutient que les modèles empiriques couramment mentionés dans les textes ne sont pas assez bien spécifiés, en particulier ceux se rapportant au secteur agroalimentaire. La relation entre les prix d'exportation et la devise peut subir diverses influences, en l'occurrence celle des propriétés des données chronologiques, celle de la devise employée pour la facturation, celle des coûts d'ajustement, qui supposent que modifier les prix est une opération onéreuse pour l'entreprise, et celle des technologies de fabrication propres aux produits dont on fait le négoce. Avec les coǔts d'ajustement, seul un taux de change assez élevé enclenchera la modification systématique des prix d'exportation. L'implication empirique est qu'il faut vérifier les effets de seuil. Des rendements d'échelle constants s'avèrent commodes sur les plans analytique et empirique, et demeurent une hypothèse indispensable avec les spécifications typiques de la TFM à une équation. Quand le rendement s'accroît avec la production cependant, la TFM influe aussi sur le marché intérieur et l'on a besoin d'un jeu d'équations pour analyser correctement l'impact du taux de change sur les prix au pays et à l'étranger. Le regroupement des productions, si fréquent dans le secteur agroalimentaire, peut conforter les prétendus effets pervers de la TFM même quand les enterprises maximisent leurs bénéfices et le changement de fournisseur n'entraîne aucun frais.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 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".