Foreign direct investment and institutional stability: who drives whom?
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the relationship between foreign direct investment (FDI) flows and institutional stability. The focus country is Canada. It is one of the few countries where the economy remained relatively stable compared to other economies during the Global Financial Crisis. It is crucial for Canada to determine the optimal level of institutional development to attract more FDI and sustain the sound financial stability in future. Design/methodology/approach This study uses the auto-regressive distributive lag (ARDL) approach to understand the relationship between FDI and institutional stability along with other controlled variables, for instance, gross national product, inflation and exports. Findings The key finding of this work is that FDI and institutional stability are cointegrated in the long run. The error correction model of ARDL shed light on institutional stability being an exogenous variable, and FDI is an endogenous variable. Institutional stability affects FDI, as it is exogenous. The findings will help policymakers to implement policies to strengthen the institution’s settings, and this, in turn, will attract more investment. Originality/value Based on previous theoretical and empirical literature, most of the research points to FDI positively affect institutional stability. In some cases, the relationship does not always hold true. This study will fix the gap in the literature by investigating the relationship between FDI and institutional stability of 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".