Price Discrimination within and across EMU Markets: Evidence from French Exporters
Bibliographic record
Abstract
We study the cross-sectional dispersion of prices paid by EMU importers for French products.We document a significant level of price dispersion both within product categories across exporters, and within exporters across buyers.This latter source of price discrepancies, sellers' price discrimination across buyers, is indicative of deviations from the law-of-one price.Price discrimination (i) is substantial within the EU, within the euro area, and within EMU countries; (ii) has not decreased over the last two decades; (iii) is more prevalent among the largest firms and for more differentiated products; (iv) is lower among retailers and wholesalers; (v) is also observed within almost perfectly homogenous product categories, which suggests that a nonnegligible share of price discrimination is triggered by heterogeneous markups rather than quality or composition effects.We then estimate a rich statistical decomposition of the variance of prices to shed light on exporters' pricing strategies.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".