Role of Remittances on Gross Domestic Product (GDP) Growth in Developing Countries: The Case of Bangladesh
Bibliographic record
Abstract
Bangladesh is one of the top ten remittance recipient countries and the contribution of remittance to the GDP has increased dramatically in recent years.This study examines the role of remittances on GDP growth in Bangladesh and Time series data for decades from 1996 to 2019 extracted from the World Bank database as well as Bangladesh Bank statistics were used.The study was conducted being motivated from conflicting results that have emerged in the literature on the impact of remittance on the economic growth of different countries.Using ordinary least squares (OLS) and Pearson correlation method, the empirical results of the study have been consistent with some previous studies while also contrasted to some literature on the impact of remittances on GDP, Gross Domestic Savings and Domestic Expenditure of a country.We have found that the remittances have a significant impact on the growth of GDP as well as Gross Domestic Savings and Domestic Expenditure.Since, there are no recent studies on the role of remittances on GDP, therefore, the findings of the study provide a significant insights for policymakers for strengthening policies and regulations relating to harnessing remittances for economic growth.Implications of the study have been discussed.
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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 | 0.002 | 0.004 |
| 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) | 0.002 | 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".