The Long-Run Impact of Financial Development on Remittances: Evidence from Developing Countries
Bibliographic record
Abstract
We investigate the long-run relationship between remittances and the overall as well as the ifferent aspects of financial sector development including markets, institutions, access, depth, and efficiency for 85 countries from Africa, the Americas, Asia, and the Middle East for the years between 1995 and 2014. We first check for stationarity, co-integration, and the direction of causality. We find that all employed variables are stationary in levels and first difference, apart from the case of the host country per capita income which is only stationary in first difference. Our cointegration test results indicate robust evidence of a long-run relationship between the remittances and the covariates. Thus, establishing a case for the long run relationship between remtitances and our covariates including financial services development. In terms of causality between remittances and financial services development, we find a mixture of results ranging from bi-directional, unidirectional, and no causality at all between remittances and the financial services development, depending on the region and type of financial services development in question. For the long-run impacts, we find that all the determinants are important including the financial services development. But the size, direction, and significance of the impact of financial services development and its different components differ by the type of financial development measures and region under consideration. Our estimate of the long-run elasticity of the financial development measures of remittances provides mixed results: it is either significantly positive, insignificant, or negative depending on the regions and measure of financial development employed. Further, we find that the migrant stock, exchange rate volatility, home and host country per capita income, and inflation all play significant roles in the determination of the remittance flows into a given country.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".