Macroeconomic determinants of foreign direct investment in emerging economies in turbulent times – A case of COVID’19 pandemic
Bibliographic record
Abstract
The study investigates the macroeconomic determinants of foreign direct investment (FDI) in emerging economies in turbulent times, taking the case of COVID’19 pandemic. Fifteen (15) countries were included for empirical investigations and the period of investigation spans 2019q1–2023q2 and the analytical framework is the Wang and Wong (2007) model. With recourse to various data stability tests, the panel system generalized method of moment is adopted as the technique of analysis to obtain optimal identification solution and address inherent problems of endogeneity and heterogeneity in estimations. For robustness, the sample was decomposed into two; emerging economies with history of high and low FDI receipts respectively. The results obtained show the sensitivity of macroeconomic determinants to these disaggregation and that lag counterparts of these variables play significant roles. The results further suggests that FDI is a substitute for real gross domestic product in emerging economies with history of high FDI receipts.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".