The Determinants of Outward Foreign Direct Investment from Latin America and the Caribbean: An Integrated Entropy-Based TOPSIS Multiple Regression Analysis Framework
Bibliographic record
Abstract
Given that home country factors play a major role in the internationalization of emerging market firms, there is an ever-growing debate on how they influence the intensity of outward foreign direct investment (OFDI) from these regions. This study investigates how home country factors affect the OFDI intensity in Latin America and Caribbean (LAC) countries. We use the entropy weight method, which uses the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method and a balanced panel data consisting of 19 countries from 2007 to 2016. The results show a positive association between macroeconomic performance, formal institutions, infrastructure, technology and the OFDI intensity. Furthermore, we find that robust formal institutions, along with the quality of infrastructure and technology, positively moderate the relationship between macroeconomic performance and the OFDI intensity. These findings show that the internationalization of LAC firms is highly dependent on the contextual conditions in their markets.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".