Trade liberalization and `delocalization': new evidence from þrm-level panel data
Bibliographic record
Abstract
Abstract. We examine how U.S. multinational corporations (MNCs) and their Canadian afþliates responded to the substantial bilateral tariff reductions that occurred over the 1983{ 92 period. Using conþdential þrm-level data from the Bureau of Economic Analysis, we focus on the MNCs ' allocation of employment and capital across Canada and the United States. We þnd that Canadian afþliate employment and assets were negatively correlated with Canadian tariff rates, a pattern that contradicts the notion that Canadian tariff reductions would lead to a `hollowing out ' of Canadian manufacturing. We also þnd evidence of substantial heterogeneity in MNCs ' responses to tariff changes, even within narrowly deþned industries. JEL classiþcation: F23, F10 Liberalisation du commerce et `delocalisation': nouveaux resultats a partir de donnees au niveau de la þrme. Les auteurs examinent la reponse des entreprises plurinationales americaines et de leurs þliales canadiennes aux reductions bilaterales substantielles dans les barrieres tarifaires entre 1983 et 1992. Utilisant des donnees conþdentielles au niveau de la þrme en provenance du Bureau of Economic Analysis, ils examinent l'allocation de l'emploi et du capital de ces plurinationales a travers le Canada et les Etats-Unis. Il appert
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".