Effect of Remittance-Sending Countries’ Type on Financial Development in Recipient Countries: Can the Pandemic Make a Difference?
Bibliographic record
Abstract
This study examines the effect of remittances on selected recipient countries’ financial development. Using weights for bilateral remittances from 1990 to 2015, this study calculates the weighted gross national income per capita of remittance-sending countries. This study then uses the weighted gross national income as an instrument to address the endogeneity between remittance and financial development. Using the instrument variable (IV) model, this study finds that remittances from low-skilled migrant-abundant sending countries have different effects than the highly skilled labor-abundant sending countries. Assuming the Gulf Cooperation Council (GCC) countries as a source of low-skilled and the Group of Seven (G7) as the source of high-skilled labor-abundant sending countries, remittance from relatively low-skilled emigrants has a greater impact on financial inclusion in the recipient countries than their high-skilled counterparts. In contrast, remittance from high-skilled countries has a greater impact on the development of the stock market. Similar types of effects of remittance on financial development have also been observed during the COVID-19 pandemic. The results suggest that policymakers should provide better foreign employment opportunities and improved transaction and investment policies in the home financial markets.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".