Analyzing FDI trends in emerging markets: Turkey vs CSEE and the Middle East
Bibliographic record
Abstract
Purpose This paper aims to deconstruct the economic position of Turkey in comparison to its immediate neighbors, Central and South‐Eastern Europe (CSEE), and the Middle East, with a specific emphasis on Japanese Foreign Direct Investment (FDI). Design/methodology/approach Several determinants of FDI intensity identified in the extant research are used to conduct this comparative analysis. The data is based on the foreign subsidiaries of Japanese firms. Findings The results confirm the ambiguous position of Turkey. It enjoys a high Gross Domestic Product growth despite a relatively low openness to trade. From cultural and political risk perspectives it is closer to the Middle East than to CSEE. In spite of its location advantages, the institutional environment continues to be an impediment, preventing Turkey from realizing its full investment potential. Thus, Japanese investors choose to invest in CSEE economies, which are slightly closer culturally to Japan, and significantly less risky. Research limitations/implications The descriptive nature of this study is due to a limited sample size. The comparison of a country with two regions might be too simplistic. Focus on Japanese FDI limits generalizability. Practical implications The results of this analysis confirm the importance of continuous market liberalization and political stabilization measures for attracting FDI. Government policies in the region should not only attempt to remedy local deficiencies but strive to create a comprehensive institutional framework. Originality/value This study contributes to the emerging market research in the region with paucity of data. Although comparisons among various European regions are common, such tri‐lateral analyses are rare.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".