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 a une Epoque de Transformation Structurelle: Prevalence, Facteurs decisifs et Implications pour le Bien-Etre. Resume En utilisant des donnees d'enquetes sur les conditions de vie representatives recentes a l'echelle nationale du Ghana, cette etude examine l'emploi non-agricole dans le Ghana rural. Les donnees suggerent une augmentation de l'emploi non-agricole rural sur une periode de sept ans, avec un nombre de femmes depassant celui des hommes dans ce secteur. Les principaux facteurs qui changent la probabilite de l'emploi non-agricole incluent le capital humain, les ressources financieres des menages, la disponibilite de l'infrastructure et la non possession de la terre. Le bien-etre rural s'ameliore et cela se passe avec en arriere plan les transformations structurelles refletees en partie par l'augmentation de l'emploi non-agricole et la realisation educationnelle. Les politiques gouvernementales ont eu notamment pour but de fournir les zones rurales en electricite et en routes accessibles, ce qui a aide a creer un environnement favorable a l'amelioration du bien-etre.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".