Unraveling Determinants of FDI: Insights from Oil-Abundant Economies
Bibliographic record
Abstract
The significance of natural resources in shaping foreign direct investment (FDI) dynamics cannot be overstated. In oil-abundant countries, these resources act as catalysts and magnets for investment inflows. Against this backdrop, this research aims to dissect the determinants of FDI within oil-rich nations, focusing on four critical factors: natural resources, exchange rates, openness to trade, and market size. The study seeks to unravel the intricate interplay between resource endowments and investment attractiveness by leveraging panel data from six oil-abundant countries (the United States, China, Russia, Canada, Saudi Arabia, and the UAE) over nine years (2011–2019) and employing the fixed effect model as a robust methodology. The results reveal that all factors: natural resources, openness to trade, and market size are statistically significant in affecting FDI inflows. Due to the robust economic conditions in the countries studied exchange rate fluctuations have a limited impact on FDI. Instead, investors prioritize microeconomic factors such as labor wages, logistics costs, and telecommunication tariffs when evaluating business efficiency in investment destinations. Thus, this research provides actionable insights for policymakers to enhance the market environment for local producers, support trade through subsidies and incentives, and focus on resource exploration to attract foreign investors and stimulate economic growth.
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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 | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".