An Assessment of the Role of Economic Empowerment Schemes on Poverty Reduction among Youth and Women Groups: A Case of the Arusha Region, Tanzania
Bibliographic record
Abstract
Globally, the Women and Youth Development Fund provided via women's groups, has contributed a significant role in ensuring women’s and youth's economic empowerment. In Tanzania, the increase in population growth has brought both opportunities and challenges. One of the major challenges of population growth is the increase in unemployment and poverty among youth and women. To redress the situation, the Women, Youth, and People with Disabilities Revolving Fund (WYDF) was established at LGAs to ease lending challenges to the poor who are deprived of loan access from conventional financial institutions. The study assessed the role of economic empowerment schemes in reducing poverty among youth and women groups in, Arusha Region, Tanzania. Specifically, the study determined the influence of the provision of start-up capital, financial literacy training, and business capacity building on Poverty reduction among youth and women groups. The study adopted a survey research design based on a quantitative approach. Data was collected from 95 respondents from the Arusha region, Tanzania using questionnaires. The sample was selected using purposive sampling methods. Descriptive statistics was used to analyze the data using SPSS. Furthermore, inferential analysis was conducted whereby regression analysis was used to show the relationship between variables that influence poverty reduction among youth and women groups supported through economic empowerment schemes. The study found that the provision of start-up capital, financial literacy training, and business capacity building has a statistically significant influence on poverty reduction among youth and women groups. The study recommends that financial literacy should be provided to help entrepreneurs save sufficient money to provide adequate capital to invest in their businesses. The Government through youth and women empowerment programs should continuously offer training to youths and women groups especially those with no technical skills, on new production, proper allocation of funds, and marketing strategies/ techniques that aim at increasing the quality and quantity of harvest to achieve lasting impacts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".