Income Diversification and Economic Welfare of Rural Households in the Volta Region of Ghana
Bibliographic record
Abstract
We established the factors influencing income diversification, and the linkage between income diversification and economic welfare of rural households, in the Volta Region of Ghana, using data from 894 randomly-selected households, obtained through the latest round of the Ghana Living Standards Survey undertaken by the Ghana Statistical Service from October 2016 to October 2017. The overall household income diversification, measured by the Simpson Index was positively influenced by the age of the household head, remittances received by the household, and the size of the household. Using another measure of diversification, the number of income-based activities (NIBA), we established that the age of the household head influenced NIBA in a cubic fashion, similar to an S-shaped curve. Income diversification declined at very young ages from 17 to 31 years; it then increased from 31 years to 74 years before declining during the household head’s advanced age and retirement period. The positive drivers of NIBA included moderate levels of formal educational attainment, remittance, household size and electricity connection. We showed that income diversification influenced economic welfare only when used at moderate to high levels.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".