Export Market Dynamics and Plant-level Productivity: Impact of Tariff Reductions and Exchange Rate Cycles
Bibliographic record
Abstract
This paper examines how trade liberalization and fluctuations in real exchange rates affect export-market entry/exit and plant-level productivity. It uses the experience of Canadian manufacturing plants over three separate periods that featuring different rates of bilateral tariff reduction and differing movements in bilateral real exchange rates. The patterns of entry and exit responses as well as the productivity outcomes differ markedly in the three periods. Consistent with much of the recent literature, the paper finds that plants self-select into export markets-that is, more efficient plants are more likely to enter and less likely to exit export markets. The reverse also occurs: entrants to export markets improve their productivity performance relative to the population from which they originated and plants that stay in export markets do better than comparable plants that exited, lending support to the thesis that exporting boosts productivity. Finally, we find that overall market access conditions, including real exchange rate trends, significantly affect the extent of productivity gains to be derived from participating in export markets. In particular, the increase in the value of the Canadian dollar during the post-2002 period almost completely offset the productivity growth advantages that new export-market participants would otherwise have enjoyed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 |
| 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".