The Influence of Foreign Direct Investment, Portfolio Investment, Remittance Receipts, Exchange Rates on Economic Growth in 10 APEC Countries
Bibliographic record
Abstract
Every country has problems and challenges related to the influence of factors that influence economic growth. Many things influence the economic growth of a country. This research aims to determine the effect of foreign direct investment, portfolio investment, remittance receipts, exchange rates on the economic growth of 10 APEC countries (Australia, Canada, China, Chile, Japan, Malaysia, Mexico, New Zealand, the Philippines and Russia). This research method uses panel data regression analysis. The results of this research show that partially foreign direct investment (x1) has a positif and significant effect on the economic growth of 10 APEC countries. Portfolio investment (x2) has a negative and insignificant effect on the economic growth of 10 APEC countries. Remittance receipts (x3) have a negative and insignificant effect on the economic growth of 10 APEC countries. The exchange rate (x4) has a negative and insignificant effect on the economic growth of 10 APEC countries. In conclusion, this research shows that of the four variables studied, only the foreign direct investment has a significant positive influence on the economic growth of 10 APEC countries. Other variables, such as portfolio investment, remittance receipts, and excange rates do not show a significant influence on the economic growth of these countries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".