Determinants of foreign direct investment in Southeast and South Asian countries
Bibliographic record
Abstract
The purpose of this study is to describe the Foreign Direct Investment (FDI) determinants of 6 countries, each of which is 3 countries from Southeast Asia and South Asia. Foreign Direct Investment (FDI) shows capital flows in 6 countries (Indonesia, Philippines, Malaysia, India, Pakistan and Bangladesh) that are affected by inflation, wage, Exchange Rate, Market Size and Trade Openness. The research method used is the regression of panel data for the period 2004-2019 from 6 countries in Southeast Asia obtained from the World Bank database. The results showed simultaneously and partially variable wage, exchange rate, market size and trade openness had a significant relationship with FDI inflows, except for variable inflation at α = 10%. Of the several variables studied, the dominant variable affecting the inflow of FDI in a country is the size of the market and followed by the wage level. In addition, of the 6 countries studied by countries that have great potential as recipients of FDI is Philippines.
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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 | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".