Do happiness and foreign aid affect bilateral migrant remittances?
Bibliographic record
Abstract
Purpose Studies on the determinants of remittances focus primarily on a single country or undertake cross‐country analyses using aggregate data. By comparison, there is a dearth of empirical evidence on the determinants of remittances from multiple host to multiple destination countries. To address this deficiency, the purpose of this paper is to use a novel dataset which captures these bilateral flows. Design/methodology/approach The paper concentrates on three sets of explanatory variables: those which characterize the pair relationship, those that pertain to migrants' host country, and those related to the migrants' home country. Findings Cultural and political factors play a fundamental role. Altruism is not key in migrant remittances; investment motives are more important. Bilateral aid inflows bear a direct relationship to remittances. The marginal effect of happiness (in migrants' host and home countries) on remittances is positive for a large percentage of countries in the sample. Practical implications Results nullify the oft‐asserted role of remittances in assisting with adverse economic conditions, such as inflation. They also identify a possible nexus between remittances and foreign aid – a link that heretofore has not been identified or discussed in the literature or recognized by policy‐makers. Originality/value The contribution of the paper is its use of bilateral data to present evidence on remittances capturing not only North‐South, but also South‐South flows. The paper also contributes to the literature by considering, for the first time, some additional variables as potential determinants of remittances, chief among them the level of happiness of migrants' host and home countries, as well as the level of aid disbursed to migrants' home country.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".