Macroeconomic impacts of remittances in Bangladesh: The role of reverse flows
Bibliographic record
Abstract
Abstract Das and Serieux (2010; 2015) and Serieux (2011) used the term “reverse flows” to define the part of external resources that is not domestically absorbed; instead used to finance debt obligations, capital flight, and accumulate reserves. While there is a vast literature on the growth and development impact of remittances in developing countries, the existing empirical literature has mostly ignored the potential diversion of remittances to reverse flows. This paper bridges the gap in the literature by estimating the reverse flows in the case of Bangladesh, which is one of the top remittance recipient countries in the world. The data set runs from 1976 to 2015. Econometric results obtained by employing the Autoregressive Distributed Lag (ARDL) approach show that almost 13–14% of remittances (as the ratio of gross domestic product, GDP) are diverted to finance reverse flows. In other words, the effects of remittances (as the ratio of GDP) on consumption and investment rates are no more than 86–87%. Therefore, the underlying assumption made in the existing literature that all remittances are used to increase consumption and/or investment overstates the impact of this external resource flow in Bangladesh. Findings from this study have important policy implications not only for Bangladesh but for other remittance recipient developing countries. Our findings will help the government to design policies to ensure the optimum allocation of remittances in the domestic economy.
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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 | 0.001 |
| 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) | 0.004 | 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".