Transmission des coûts et hausse de l’inflation
Bibliographic record
Abstract
We use micro-level price data underlying the French producer price index from January 2018 to July 2022, along with external measures of firms' exposure to imported inputs and energy cost shocks, to study the role of external shocks in the recent inflation surge. Within our sample, firms pass through 30% of changes in the price of imported inputs and 100% of changes in energy costs when resetting their prices, conditional on their exposure to these shocks. For the average firm in our data, this implies that a 10% increase in foreign costs leads to a 0.74% rise in output prices, while a 10% energy cost shock induces prices to increase by 0.73%. We examine how pass-through rates vary across firms within and across industries, depending on their size and exposure to shocks. We find that pass-through rates are asymmetric, with positive cost shocks inducing significantly more pass-through than negative shocks. The heterogeneity in exposure to external shocks across firms and sectors drives important differences in inflation dynamics along firms' distribution. To illustrate this, we predict price changes from cumulative imported inputs and energy price changes between January 2021 and July 2022, and find that between 70% and 75% of the variance in predicted price changes happens within 2-digit industries, across firms. The chemical and metal industries are the most impacted by both imported and energy cost shocks, which contribute to an increase in producer prices in those sectors of at least 9% to 14%.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".