Institutional Efficiency and Attraction of Foreign Direct Investment to Developing Countries
Bibliographic record
Abstract
The paper estimates the impact of institutions’ quality on the attraction of foreign direct investment (FDI) to developing countries. Data Envelopment Analysis (DEA) was used to develop a new measure of quality of institutions: Institutional Efficiency Index (IEI). In order to appraise quantitatively the effect of institutional quality on FDI entry, we used a panel data regression analysis on a dataset covering 40 countries from different developing regions for which the necessary data were accessible during the period 2011-2015. The paper argues that the institutional efficiency, as a measure of institutional quality, enhances the attractiveness of developing countries to FDI. The results of this paper suggest that FDI is mainly determined by institutional quality. A host country endowed with a high quality of institutions will be more attractive to foreign investors. In order to improve their competitiveness in term of attraction of foreign investment, developing countries should work more on providing a stable environment as well as on the transparency of policy implementation regarding the entry of multinational companies.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".