Three Essays on How Migrant Remittances Respond to Natural Disasters in their Home Countries
Bibliographic record
Abstract
The home countries of many migrants frequently suffer from both low income and an exposure to natural disasters. Disasters are particularly devastating for lower income families, and can adversely affect their incomes and well-being. The New Economics of Labour Migration theory suggests that migration and the resulting remittances can be a critical risk-mitigation strategy adopted by households in the face of natural disasters. This thesis explores the extent to which remittances are sensitive to natural disasters in the migrant’s home country. Three separate chapters use different datasets to examine this main research question. The key finding of all three chapters is unequivocal: remittance inflows are consistently and positively related to the presence of natural disasters in the home country. Chapter 2 uses large-scale data on recent Canadian immigrants and finds compelling evidence that they remit significantly more in the aftermath of natural disasters affecting their home countries. In addition to examining other personal and family characteristics associated with remitting behaviour, the study found that the administrative category under which they entered Canada also influenced their remitting patterns. Chapter 3 examines a unique dataset based on primary data collected through 118 in-depth interviews from two migration-prone villages in Bangladesh. It explores how migrants and their families use migration and remittances as a coping mechanism in the face of frequent flooding. The role of gender appears as particularly important. While men tend to send more money home, they also tend to earn more as migrants. By contrast women remit a larger share of their income generally, and send almost all their residual income in the event of a natural disaster. Finally, using aggregate monthly remittances, Chapter 4 examines the responsiveness of remittances into Pakistan in the aftermath of natural disasters. The response is significant, with total remittances increasing on average more than US$9,666 per fatality. Few other factors were also analyzed to investigate their influence on remittances. While religious festivities are associated with higher remittances, remittance inflows appear largely non-responsive to terrorist events in the country, with weak evidence that remittances are actually deterred in the aftermath of these events.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".