A Simple and Flexible Dynamic Approach to Foreign Direct Investment Growth: The Canada‐United States Relationship in the Context of Free Trade
Bibliographic record
Abstract
This paper asks a simple question: Did Wilfred Laurier's dream of free trade with the United States, when it came to fruition in 1989, also impact on foreign direct investment (FDI) into Canada by US multinationals? This paper argues that the customary static econometric approach found in the FDI literature, along with the assumption that policy changes influence only the intercept term, are inadequate to address the question. Instead we introduce an innovative dynamic framework to support the testing of hypotheses on behavioural changes in the variables using a structural break framework. A key conclusion is that prior to signing the free trade agreement US FDI responded only to current growth in the Canadian economy, in a unitary fashion, and current exchange rate shifts. This can be described as a static relationship. The implementation of the free trade agreements between Canada and the USA increased the responsiveness of US FDI to growth in the Canadian economy by a factor greater than two. Furthermore, dynamics are found in the form of a lagged effect for changes in the growth in the Canadian economy and interest rate differentials. These conclusions challenge the dominant view, including that in official policy circles, that the free trade agreement had no impact on US firms’ FDI decisions in 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".