Do happiness and foreign aid affect bilateral migrant remittances?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it