Foreign Aid–Human Capital–Foreign Direct Investment in Upper-Middle-Income Economies
Bibliographic record
Abstract
The study examined the influence of foreign aid on foreign direct investment (FDI) in upper-middle-income economies using panel data (2011–2021) analysis methods such as two-stage least squares (2SLS) and system GMM (generalized methods of moments). The study also explored if human capital development enhanced foreign aid’s influence on FDI in upper-middle-income economies during the same timeframe. The conflicting, divergent, and mixed results and views on the relationship between foreign aid, human capital development, and foreign direct investment (FDI) motivated the undertaking of this study to fill in the existing gaps. Apart from FDI enhanced by its own lag, foreign aid significantly improved FDI (under system GMM). FDI was also improved significantly by human capital development across all two panel methods. Under 2SLS and system GMM, foreign aid significantly improved FDI through the human capital development channel. To promote FDI inflows, upper-middle-income economies should develop and implement policies aimed at attracting foreign aid and enhancing the development of human capital. The study suggests that further research on threshold regression analysis on foreign aid–FDI nexus in upper-middle-income economies could better help develop an FDI policy that is beneficial toward economic growth.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".