Does institutional stability granger-cause foreign direct investment? evidence from Canada
Bibliographic record
Abstract
Global FDIs have increased substantially since the 1990’s. This was seen as a favorable \ndevelopment among developing countries, however developed countries have had a mixed \nreaction. In this paper we look at the effects of FDI flows on institutional stability, to better \nunderstand what drives FDI. The focus country for this paper is Canada, as it is one of the \nfew countries where the economy remained relatively stable compared to other economies \nduring the global financial crisis. As such, the findings from this study can shed light on \nwhat allowed Canadian policy makers to maintain economic stability. The methodology \napplied is Auto-Regressive Distributive Lag (ARDL) to understand the relationship \nbetween FDI and institutional stability along with other controlled variables (GNP, \ninflation, and exports). This study is different from others in that it examines the Canadian \neconomy, and similar papers have examined different countries (to my knowledge). Based \non previous theoretical and empirical literature, most of the research points to FDI \npositively affecting institutional stability. However, there is some literature that makes the \ncase for this relationship not always holding true. Our empirical findings tend to show that \nit is in fact institutional stability that positively impacts FDI in the long run. As such, the \npolicy makers should consider implementing policies that ensure that the strength of \ninstitutions is enhanced, and this in turn will attract more investment.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".