Rural Youth Welfare along the Rural-urban Gradient: An Empirical Analysis across the Developing World
Bibliographic record
Abstract
We use survey data on 170,000 households from Asia, Latin America and Africa, global geo-spatial data, and an economic geography framework to highlight five findings about rural youth in developing countries. First, the youth share in population is falling rapidly, and youth numbers are stable or falling slowly everywhere, except in Africa. In Africa, youth share is rising very slowly, but numbers are set to double in 40 years. Second, large majorities of rural youth live in spaces that are not inherently limiting: two-thirds live in zones with highest agricultural potential, and one-quarter combine this with highest commercialisation potential. The 4% that do live in inherently challenging spaces are concentrated in pockets of persistent poverty in middle-income countries. Third, rural spaces’ commercial potential has large impacts on welfare outcomes, but their agricultural potential has no detectable impact. Fourth, households with young members face income- and poverty ‘penalties’ in all regions and spaces within them, compared to households without young members. The poverty penalty declines sharply over space as commercial potential rises, but the income penalty shows ambiguous patterns. Fifth, households with young members earn lower relative returns to education, with varying patterns over space.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".