Political Turbulence and Its Impact on Foreign In-vestor Confidence in Bangladesh's Garments Sector: An Analysis of Withdrawn Investments in 2023-2024
Bibliographic record
Abstract
Bangladesh has relied on the garments sector for many years and is a major destination for FDI due to low wages and demand globally. This trend was disrupted in early 2023-2024 by renewed political turbulence, causing a significant number of key foreign investors to withdraw or freeze their investments. In this paper, we explore the effect of political instability on Foreign Investors' confidence in garments sector in Bangladesh against this backdrop of recent upheaval. This study identifies the top political and economic factors behind China's slowdown in inward direct investment, based on an analysis of withdrawn investments, case studies of impacted firms, and the analysis of investor feedback. It also analyzes the impact of declined FDI on output, employment and export performance in the garments industry. Lastly, it offers policy recommendations to curb the risks of political instability and to regain confidence on investors. This study seeks to encourage discussions on the sustainable investment approaches of developing economies (like Bangladesh) by focusing on the relationship between governance and economic resilience.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.005 | 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".