The Determinants of FDI in OIC Countries
Bibliographic record
Abstract
Foreign Direct investment (FDI) is considered to be an important source of capital especially in developing countries. FDI supplements local savings and brings a series of benefits in host countries. This research has focused OIC on countries since these countries are still far behind in attracting FDI compared to other developing countries. OIC member countries inhibit diversity in their resources from resource rich to resource poor countries. They lack behind the developed world in terms of economic development pertaining to weak economies. Since for these types of countries FDI can prove to be a vital source of capital, it becomes important to study the factors that affect it. This study exactly does the same by incorporating a series of determinants (inflation, size of the economy, trade openness, infrastructure, and institutional quality) to assess the impact they have in attracting FDI. We have used data for 42 countries spanning over 1996-2013. The choice of data selection has been dictated by data availability. For estimation we have used panel fixed effects and random effects estimators. Our results indicate that size of economy, infrastructure and trade openness are positively and significantly related in attracting FDI in those countries. Institutions on the other hand are negatively related. The effects of inflation are somewhat mixed according to our estimation and not robust. The implications of our findings are that policy makers should expend efforts in making more trade oriented policies, improve infrastructure and increase the size of economy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".