Do the Inward and Outward Foreign Direct Investments Spur Domestic Investment in Bangladesh? A Counterfactual Analysis
Bibliographic record
Abstract
The net contribution of the decomposed measures of foreign direct investment (FDIs), e.g., the inward and outward flows of FDIs, to domestic investment is still inconclusive in the case of underdeveloped and developing countries. The current literature bears testimony to this fact. Hence, this research examines the impact of inward and outward foreign direct investments (FDIs) on the domestic investment in Bangladesh. This study considers annual time series data from 1976 to 2019 and estimates this data property under the augmented ARDL approach to cointegration. In addition, this research employs the dynamic ARDL simulation technique in order to forecast the counterfactual shock of the regressors and their effects on the dependent variable. The results from the augmented ARDL method suggest that the inward FDI has a positive impact on domestic investment, while the outward FDI is inconsequential in both the long run and the short run. Besides, our estimated findings also show the economic growth’s long-run and short-run favorable effects on domestic investment. At the same time, there is no significant impact of real interest rates and institutional quality on domestic investment in the long run or the short run in Bangladesh. In addition, the counterfactual shocks (10% positive and negative) to inward FDI positively impact domestic investment, indicating the crowding-in effect of the inward FDI on the domestic investment in Bangladesh. As the inward FDI flow is a significant determinant for sustained domestic investment in Bangladesh, the policy strategy must fuel the local firms by utilizing cross-border investment.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".