Workers’ Remittance Inflow, Financial Development and Economic Growth: A Study on Bangladesh
Bibliographic record
Abstract
The objective of this article is to analyze the role of remittance on economic growth and to investigate the interaction of remittance with the financed development of Bangladesh using annual data, relatively liberalized regime spanning from 1981 to 2013. This study employed the mostly used Johansen co-integration test along with vector error correction model to reveal both the short-run and the long-run association between remittance-growth and remittance-financed development. Granger Causality tests are also used to explore the possible endogenous relationship between remittance-growth and remittance-financed development. This study finds a long run positive relationship between inflow of remittance and gross domestic products (GDP) indicating that remittance will be more likely to contribute to longer-term growth in Bangladesh. It is also revealed that remittances have a significant positive effect on financial development. Therefore, this study posits that the inflow of workers’ remittance matters for countries like Bangladesh, which has a relatively growing economy and a developing financial sector. The paper also discusses a number of policy issues arising from the results of the analysis in relation to remittance in association with managing migration and remittance, skilled development of the migrants, Dutch disease effect, returnee expatriates and gender issues. While examining the channels the direct impact of remittance on poverty reduction (household welfare) remains an area of research for the future. The outcome of this study will be a clear understanding of the key factors affecting workers’ remittance, growth and financial development, which will be relevant to policy makers to have detailed appraisals of remittance polices and their expected economic impact on Bangladesh and other remittance recipients country as well.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".