The Offshoring Strategies of US Multinational Corporations Operating in Canada
Bibliographic record
Abstract
Using Confidential Data on 1300 U.S.-based multinational corporations (MNCs), we examine whether MNCs that moved jobs out of Canada between 1983 and 2003 systematically moved the jobs elsewhere within the MNC. We also look at whether trends in the movement of jobs within MNCs differ for manufacturing and service industries. We find no evidence of systematic offshoring of Canadian jobs by U.S. MNCs: those that increase (shrink) employment in Canada tend to exhibit the same pattern elsewhere within the firm. We do find, however, that the sectors with the fastest job growth by U.S. MNCs in Canada are those with the lowest median real wages. Similarly, we find that the relative importance of U.S. MNCs’ Canadian operations seems to decline over the 20-year time window. U.S. MNC employment in Canada, relative to employment in other foreign countries, drops from 27.6 percent of total foreign employment in 1983–1985 to 21.6 percent in 2001–2003. This change in relative employment does not appear to be the result of job cuts in Canada but of U.S. MNCs’ choosing to grow — including high-paying jobs — in countries other than Canada.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".