Exchange Rate Uncertainty and Workers’ Remittances: Empirical Bayesian Approach
Bibliographic record
Abstract
Exchange rate is one of the important determinates of worker’s remittances to a country. Level of exchange rate as well as any fluctuation in it influences the volume of workers’ remittances. The present study uses data of workers’ remittances from ten major countries to Pakistan for the period 1973 to 2012. Uncertainty of exchange rate is estimated through GARCH model. We use Empirical Bayesian approach to compute posterior information (estimates, for which, the GMM estimates are used as prior in order to avoid biasness and inconsistency due to the presence of endogeniety in our model. The Empirical Bayesian estimates are found to be more efficient in terms of significance and correct signs of modeled variables. The findings suggest a significant role of home and host country characteristics in most of the cases. The findings also reveal a negative impact of exchange rate uncertainty on the inflow of remittances. The political instability reveals an insignificant impact on remittances. The study recommends different policy options for different host countries. Apart from the Middle East, the policy for other regions (like USA, Canada, and Germany etc.) must be considered separately to encourage inflow of remittances. Appropriate stabilization measures have to be taken on priority basis to curtail volatility of exchange rates and to ascertain regular inflow of remittances.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".