Corruption, FDI, and Trade Freedom Relationship Between Turkey and Latin American Countries
Bibliographic record
Abstract
Foreign Direct Investment (FDI) has been always associated with a high level of trade openness and freedom environment, and with a lower incidence of institutional corruption. Because it is assumed that a high level of international capital mobility makes foreign investors more cautious when there is a fluctuation in political stability and institutional transparency. Most American countries (except Canada and the USA) have been always related to a lack of transparency in their institutional and bureaucratic procedures, which conduct to important levels of corruption and in consequence, to other serious issues such as prominent level of violence, or even the inequality phenomena. This study investigates whether the variables of corruption indices, trade openness and inflation rates have positive or negative effects on FDI between Turkey and American countries. For analyzing this econometric model, it was gathered information from OECD, COMTRADE, TUIK, Heritage Foundation, UNCTAD, and the World Bank Database. The observed data correspond to a decade (2005-2014) and were only taken a sample of 14 American countries.The empirical results of the present study revealed that there is a positive correlation between the trade openness index and FDI; and, it was found a positive correlation between corruption index, inflation, and FDI. The increase in the corruption index causes a 41% increase in FDI inflow.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".