Rural Non-farm Employment in Ghana in an Era of Structural Transformation: Prevalence, Determinants, and Implications for Well-being
Bibliographic record
Abstract
Using data from recent nationally representative living standards surveys in Ghana, this study examines non-farm employment in rural Ghana. The data suggest an increase in rural non-farm employment over a seven-year period, with females outpacing males in that sector. The main factors driving the likelihood of non-farm employment include human capital, household financial resources, infrastructure availability and non-ownership of land. Rural well-being is improving, and this is taking place against the backdrop of structural transformation reflected in part by an increase in non-farm employment and educational attainment. Government policies aimed at providing rural areas with electricity and accessible roads, among others, have helped to create an enabling environment for improvement in well-being. Keywords: non-farm employment, rural well-being, structural transformation, human capital --------------------------------------------------------- L'Emploi Rural Non-Agricole au Ghana à une Époque de Transformation Structurelle: Prévalence, Facteurs décisifs et Implications pour le Bien-Être. Résumé En utilisant des données d'enquêtes sur les conditions de vie représentatives récentes à l'échelle nationale du Ghana, cette étude examine l'emploi non-agricole dans le Ghana rural. Les données suggèrent une augmentation de l'emploi non-agricole rural sur une période de sept ans, avec un nombre de femmes dépassant celui des hommes dans ce secteur. Les principaux facteurs qui changent la probabilité de l'emploi non-agricole incluent le capital humain, les ressources financières des ménages, la disponibilité de l'infrastructure et la non possession de la terre. Le bien-être rural s'améliore et cela se passe avec en arrière plan les transformations structurelles reflétées en partie par l'augmentation de l'emploi non-agricole et la réalisation éducationnelle. Les politiques gouvernementales ont eu notamment pour but de fournir les zones rurales en électricité et en routes accessibles, ce qui a aidé à créer un environnement favorable à l'amélioration du bien-être.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".