Foreign Direct Investment, Economic Freedom and Economic Growth: Evidence from Developing Countries
Bibliographic record
Abstract
This paper has explores the interplay between economic freedom, foreign direct investment and economic growth using panel data analysis for a sample of 79 developing countries from 1998 to 2014 by considering the level of economic freedom, as provided by the “Heritage Foundation”. Panel unit root, pedroni residual co-integration test, generalized least square (GLS), feasible GLS (FGLS), pooled OLS, random effect, fixed effect, poisson regression, prais-winsten, generalized method of movement (GMM) and generalized estimating equation (GEE) methods have used to estimates the relationship. According to the OLS and generalized method of movement the coefficient implies that a one standard deviation improvement in business freedom, trade freedom, size, investment freedom, property rights, freedom from corruption, labor freedom, financial freedom, fiscal freedom, monetary freedom increases FDI by 21.4%, 15.6%, 21.6%, 17.5%, 11.55, 9.1%, 6.9%, 8.5%, 7.4%, 10.3% and 56.1%, 45.3%, 58.3%, 51.6%, 33.7%, 39.2%, 47.4%, 41.6%, 32.5%, 38.5% points respectively and for the economic variable ,the coefficient implies that a one standard deviation improvement in GDPG and GDPPC increases FDI by 24.1%, 17.4% and 30.2%, 33.4% points respectively. By using the other method like random effect, fixed effect, poisson regression, prais-winsten and generalized estimating equation (GEE) method explores that economic freedom in the host country is a positive determinants of FDI inflows in developing countries and also the result suggests that foreign direct investment is positively correlated with the economic growth in the host countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.002 | 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".