FDI and Income Inequality: How MNEs Can Have Negative Impacts on Developing Countries
Bibliographic record
Abstract
Foreign direct investment (FDI) has the potential to drive economic growth in developing countries by facilitating the transfer of capital, technology, and managerial expertise from developed nations. However, FDI can also have adverse effects, often resulting in the exploitation and depletion of vital resources. Through the lens of dependency theory, this dynamic underscores how the dominance of one country over another is frequently mirrored in their FDI relationships. Thus, we question whether FDI is leading to socio-economic disparities and, as a result, income inequality within developing countries. Specifically, we study how natural resources seeking FDI (NR-FDI) impacts income inequality in developing countries and what role rural communities and entrepreneurial opportunities play in coping with this inequality. Drawing on dependency theory and empirically studying NR-FDI, we find that the investments increase inequality in developing countries. However, strong local communities and high levels of entrepreneurial opportunity mitigate these negative effects. Our findings contribute to a better understanding of the impact of FDI on developing countries and how dependency theory can be applied to make sense of MNE activities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".