Attracting Foreign Direct Investment in Developing Countries: Determinants and Policies-A Comparative Study between Mozambique and China
Bibliographic record
Abstract
Attraction of foreign direct investments has been deserving attention for many governments worldwide. Using different literature about Foreign Direct Investment, this paper analyzes the determinants and policies to attract foreign direct investments to developing countries and makes a comparative study between Mozambique and China. The results found, indicate the difference among countries in attracting investments due to their different geographic location, conditions of infrastructure (poor or developed), corruption, taxes as well as the implementation of the policies by the governments. These results also show that successful policies in China should not be copied or implemented by Mozambique. Foreign Direct Investment must only be allowed to operate according to local conditions and must conform to certain performance requirements that will ensure a positive impact on development. Evidence within this paper shows that Africa is different in attracting FDI due to the lack of high return on capital and infrastructure development, and openness to trade.
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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 | 0.005 | 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 | 0.000 | 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".