An Examination Into the Causal Links Among Inward FDI Determinants: Empirical Evidence From Jordan
Bibliographic record
Abstract
This paper examined the causal links between inward foreign direct investments (FDI) and its determinants (i.e., gross domestic product, education, trade openness, infrastructure, and technological abilities) for Jordan over (the period 1980 – 2018). The paper used vector error correction model. The results of the study considered that gross domestic product, trade openness, education, infrastructure, and technological abilities are primary engine of inward FDI in (long term and short term). Thus, the results have vital role for the policy makers in Jordan to formulate domestic and foreign policies. This study relied on three essential parts. Firstly, FDI is a significant source of capital that promotes economic growth. Secondly, the question of what are the leading drivers of FDI remains inadequate in the literature. Finally, this research adds to the literature by using different econometrics techniques and long span of yearly time series data.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".