Impact of Selected Determinants on Foreign Direct Investment (FDI) in Bangladesh: An Empirical Study Based on Panel Data
Bibliographic record
Abstract
This study analyzes the impact of some selective macroeconomic factors on FDI as it plays a vital role in any country’s economy. In this study, based on previous literature, we have selected GDP, inflation rate, interest rate and corporate income tax as determinants. We investigate empirically the impact of those macroeconomic variables on FDI. Panel data has been collected from three global and local sources for analysis. Total 29 observations from 1987 to 2015 for each variable have been analyzed to show the effect of the independent variables using regression model. Overall, the model was found to have significant predictability over FDI. The empirical result also revealed significant impact of GDP and corporate income tax on FDI individually, while inflation and interest rate were found statistically insignificant. The descriptive statistics and correlation coefficients matrix also observed to investigate the relationship among the dependent and selective independent variables. FDI helps to upgrade the socioeconomic condition of the country and hence, to compete in a competitive world, investment friendly policy adoption, enhanced infrastructure and improvement of overall investment climate are essential for Bangladesh to ensure the growth of FDI journey and ultimately foster the economic development journey.
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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 | 0.001 | 0.000 |
| 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".