Multi‐product exporters, variable markups and exchange rate fluctuations
Bibliographic record
Abstract
Abstract In this paper we investigate how firms adjust markups across products in response to fluctuations in the real exchange rate. We estimate markups at the market–product–plant level using detailed panel production and cost data from Mexican manufacturing between 1994 and 2007. Exploiting variation in the real exchange rate in the aftermath of the peso crisis in December 1994, we provide robust empirical evidence that plants increase their markups and producer prices in response to a real depreciation and that this increase is greater for products with higher productivity. Thus, we provide direct evidence for the theoretical mechanism of variable markup response behind incomplete and heterogeneous exchange rate pass‐through on producer prices. Our empirical methodology allows us to decompose the producer price response to exchange rate shocks into a markup and a marginal cost component using our markup estimates. Using these estimates, we establish that marginal cost at the product–plant level increases more in response to real exchange rate depreciation if the plant has higher share of imported inputs.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".