Tariff effects on MNC decisions to engage in intra‐firm and arm's‐length trade
Bibliographic record
Abstract
Abstract Using confidential firm‐level data from the Bureau of Economic Analysis (BEA) on activities of U.S. multinational corporations (MNCs) and their Canadian affiliates, we study the dramatic growth of intra‐firm and arm's‐length U.S.‐Canada trade over the 1984–95 period. We find that decisions to engage in intra‐firm and arm's‐length trade are essentially unrelated to tariff and transport cost reductions over this sample period. Thus, we find that the increase in trade occurred almost entirely on the intensive rather than the extensive margin. This is consistent with case study evidence in Keane and Feinberg (2006), where MNC executives consistently indicate that the modest tariff reductions of the 1984–95 period were not sufficient to justify fixed costs of overhauling international supply chains. Our results have important implications for recent influential models of international trade that rely on sensitivity of intra‐firm trade to tariffs at the extensive margin to explain how small tariff declines could have led to the explosion of intra‐firm trade since the 1980s. We also find that initial conditions (i.e., 1983 tariffs) are uncorrelated with whether firms engaged in intra‐firm or arm's‐length trade activity at the start of the sample period. This result is surprising as it implies that firms/industries with a greater propensity to engage in trade were not, in general, successful at lobbying for more favourable tariff treatment.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".