Revisiting the Determinants of Non-farm Labour Income in the Peruvian Andes: The Role of Intra-Seasonal Climate Variability and Widespread Family Networks
Bibliographic record
Abstract
As previous literature shows, non-farm income represents up to 50 per cent of rural household income in developing countries. Mostly due to a lack of representative information on climate and family networks, two key factors have been excluded in previous studies on income diversification: (i) the role of intra-seasonal climate variability (affected by climate change), and (ii) the role of family networks located in distant areas (increasingly important given population mobility due to internal conflicts and improved roads and communications). This study analyses the role of these factors on non-farm working hours and non-farm income shares in the Peruvian Andes. Controlling for other assets and environmental conditions, the study finds that households with distant, strong networks diversify more into non-farm activities. Increases in intra-seasonal climate variability (measured by temperature range during the main crop growing season) have heterogeneous effects across subregions. While we find no direct effect among Southern households (more isolated and indigenous), households in the cooler areas of the Central and Northern Andes (below 13 °C during the crop growing season) tend to increase non-farm income as climate variability increases. The study suggests that distant, strong ties facilitate non-farm opportunities for households facing increasing temperature variability in Central and Southern areas.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".