Canadian foreign direct investment in the United States by type: mergers and acquisitions, greenfield, real estate, joint ventures and investment increases
Bibliographic record
Abstract
This paper provides an overview of Canadian foreign direct investment (FDI) in the United States by type. Mergers and acquisitions (M&As), greenfield investments, joint ventures, real estate purchases and increases to existing investments are compared spatially, through time (using a data set that consists of transactions from 1977 to 1994) and by industrial classification. Over this study's time‐frame, direct investors from Canada were most likely to perform a real estate or M&A transaction in the United States (and least likely to be involved in a joint venture). The most consequential years for real estate purchases were the 1970s and early 1980s; whereas M&A and greenfield transactions have gained in proportionate importance through time. M&As, greenfield and investment increases were most often enumerated as manufacturing transactions, but mining and consumer and business services were also very common. A series of bivariate regression models established that growing state economies significantly increased M&A, real estate, greenfield and investment increase activity from Canada. Relative regional specializations (as given by location quotients) provided additional spatial information. M&As formed a pattern of specialization that emphasized the Great Lakes region and also the central portion of the US (roughly following the Mississippi River). Greenfield and investment increase transactions favoured states along the east coast and those adjacent to the Canada‐US border. Real estate investors were most attracted to the US south and extreme west.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.021 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".