Partners or Creditors? Attracting Foreign Investment and Productive Development to Central America and Dominican Republic
Bibliographic record
Abstract
Promotion of foreign direct investment (FDI) has been a priority policy goal in Central America, Panama and Dominican Republic for the past twenty years. Fiscal benefits are among the policies that have been used to attract it. At first sight the model followed has been fruitful. In 2013 the eight countries of the region succeeded in attracting US$ 12.7 billion, the highest level of FDI in their history. But there are question marks about how FDI will perform in future and what the incentives to promote it should be now that World Trade Organization rules on the instruments used to promote FDI in the region have changed. The present book analyzes this situation in depth. Firstly, it reviews the importance of FDI in the region as a source of financing for the external deficit. Then it reviews the findings of international economic research on the impact of FDI on growth and the factors that attract it. It highlights that far from being assured, the benefits of FDI depend on complementary factors which are often not present in the region. Subsequently the book analyzes the international evolution of FDI and the growing importance of multinationals of Latin origin. It then tackles the controversial question of the efficacy of fiscal incentives as a means to attract investment, following an innovative technical approach based on firm level data which questions whether the free zones have had a net positive impact on development. This analysis is complemented by a study of investment promotion policies, which focuses particularly on the Investment Promotion Agencies. Finally, the book outlines the prospects for FDI attraction now the sun has set on strategies based on providing fiscal incentives. It argues that a new strategy should be based on the creation of new skills and capacities through instruments designed to complement productive development policies and thereby generate positive spillovers in the economy.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".