Level of Income Inequality and Determinants of Poverty Incidence among Youth Farmers in Akwa Ibom State, Nigeria
Bibliographic record
Abstract
The prevalence of poverty among youths engaged in agricultural production is partly responsible for increase in agricultural diversification and rural-urban migration among other social vices in developing societies. To help generate suitable policies in order to tackle this rampaging issue, the study specifically analyzes poverty and income inequality as well as identified determinants of poverty among youths involved in agricultural production in the rural areas of Akwa Ibom State located in southern Nigeria. Data were collected from 300 youths spread across the rural areas of the State. Combinations of sampling methods were employed to sample cross sectional data from respondents. The study used descriptive tools and regression analysis (Logit regressions) to analyze information collected. The socio-economic analysis reveals that, most youth farmers were educated; social capital formation was poor, while land size averaged at 0.48ha per youth. About 45.1% of male youths and 72% of female youths live below poverty line in the study area. Income inequality index revealed 0.4009 for male youths and 0.3797 for female youths. The Logit model estimates revealed that, youths’ years in social organization, level of formal education, age of youths; amount of non-farm income, farm size, agricultural extension activities and commercial purposes of agricultural production reduced the probability of poverty incidence among youth farmers in the State. Household size and dependent ratio were positive drivers of poverty among rural youths. Based on the research findings, it is recommended that poverty and income inequality among youths engaged in agricultural production in the rural area of the State can be successfully handled, if sound family welfare packages are implemented in the rural communities. Also, rural youth’s groups should be strengthen to promote social capital formation; while farm enterprise development capacity should be encourage among youths as well as strengthening agricultural extension system in the State. Marginal lands should be developed and distributed to youth farmers in the area.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".