Remittances and inequality: A meta‐analytic investigation
Bibliographic record
Abstract
Abstract This article provides a comprehensive meta‐analysis that addresses an important gap in the literature by examining the relationship between remittances and inequality in recipient countries. While numerous empirical studies have explored this relationship, there has been no prior attempt to systematically and rigorously synthesise the evidence. This study employs advanced meta‐analysis techniques, such as Bayesian model averaging, to analyse 578 estimates reported in 45 studies. The overall finding is that the effect of remittances on inequality is negative but economically small. However, significant regional variations exist, with remittances contributing to increased inequality in South Asia, while having a substantial inequality‐reducing effect in East Asia, Eastern Europe and Latin America. In the Middle East and North Africa and Sub‐Saharan Africa, only marginal economic impact is found. We recommend that future studies should control for educational attainment, income level and institutional quality to improve the accuracy of their estimates.
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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 | 0.027 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.026 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".