FDI-growth and trade-growth relationships during crises: evidence from Bangladesh
Bibliographic record
Abstract
Abstract This study examines foreign direct investment (FDI)-growth and trade-growth relationships in Bangladesh during three major crises: the economic crisis of 2007–2008, the commodity crisis of 2016, and the coronavirus (COVID-19) pandemic of 2020. The augmented autoregressive distributed lag (AARDL) bounds testing approach and Bayer and Hanck cointegration are employed on time-series data spanning the period 1974–2020. The results suggest that exports have positive effects on economic growth, while imports have insignificant effects in both the short run and long run. Total trade (the sum of exports and imports) has a positive but weakly significant effect on economic growth only in the long run, whereas FDI exhibits a positive effect in both the short run and long run. Although the crises are not found to affect economic growth directly or through trade (i.e., no dampening effect on trade-led growth), they are found to distort FDI-led growth in both the short run and long run. As robustness tests for long-run elasticities, the fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) cointegration techniques are implemented, yielding results similar to those obtained with the AARDL.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".