PRELIMINARY DRAFT; PLEASE DO NOT QUOTE U.S. Acquisitions of Canadian Firms and the Role of the Exchange Rate
Bibliographic record
Abstract
The decline of the Canadian dollar relative to the U.S. dollar over the past 15 years has raised concerns that Canadian firms have been left vulnerable to takeovers by U.S firms. Theory and empirical studies on cross border mergers and acquisitions (M&As) have generated mixed support for a link between exchange rates and M&As. This paper argues that exchange rate movements may affect acquisition M&As because acquisitions involve firm-specific assets which can generate returns in currencies other than that used for purchase. We use data on U.S. acquisitions in Canada across four-digit SIC industries from 1985 to 2001, and, along with the real exchange rate, account for factors such as industry level Canadian tariff rates, value added of U.S. industries, the number of Canadian M&As and the number of Canadian establishments. Maximum-likelihood estimates from fixed and random effects negative binomial models reveal that a Canadian dollar depreciation leads to U.S. M&As in Canada, but only involving target firms that posses firm-specific assets (high R&D firms). Tariff rates are not significant for such industries. The real exchange rate is not significant for U.S. M&As involving low R&D firms, whereas tariff rates are.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.222 | 0.037 |
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".