Reduced migration under climate change: evidence from Malawi using an aspirations and capabilities framework
Bibliographic record
Abstract
For farmers in rural Africa, climate change could significantly alter the natural environment, leading to a loss of income, food security and well-being; however, much remains unknown about the way a change in climate may affect a person's decision to migrate away from their home. Using a framework based on migration aspirations and capabilities, this paper examines how climate stresses (such as droughts that cause a long-term decline in harvests) and climate shocks (i.e. acute food shortages and sudden flooding) may affect migration decision-making in rural Malawi. Drawing on survey (n = 255), interview (n = 75) and focus group (n = 93) data from rural and urban dwellers, we find that climate stresses typically do not change rural dwellers’ aspiration to leave their homes, except for a small group of younger farmers from better-off households. However, these same stresses may erode human, financial and social capital, thus reducing migration capability. Data also reveal that acute shocks erode both the migration aspirations and capabilities of even the most dedicated would-be migrant. Drawing from these two findings, this paper concludes that climate change is likely to increase barriers to migration rather than increasing migration flows in countries like Malawi where the economy is still predominately rural.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".