An Egyptian case study: financial services for young people who work
Bibliographic record
Abstract
Purpose The purpose of this paper is to show some of the innovative ways loans are being disbursed to help microfinance institutions (MFIs) diversify their portfolios and reach a young and viable market. The paper attempt to highlight how MEDA/PTE's project in Egypt can contribute to the industry learning on microfinance (MF) and occupational hazards and young people. Design/methodology/approach The paper presents an Egyptian case study to present how financial products can impact social issues such as working children and at – risk youth. Findings The study finds that the young people market has been rarely researched in the MF sector. Children and youth like many other groups face a host of issues especially unemployed and poor ones. Program design has often focused on social interventions and keeping young people away from work and in the school system. Through a rights‐based approach, this project is learning that the young people are key actors in many micro enterprises as workers and in some cases as business owners themselves. The market is diverse and so are the needs of the children and youth who are involved. It is time to consider innovation in designing programs for young people. There are alternative learning techniques and skill development for young people in poor countries where school and social services do not meet their needs. Learning within actual workplaces can provide alternate educational opportunities for children provided the work is safe and age appropriate. Programs focused on young people and economic empowerment and job creation will assist many developing nations in stabilizing systems and supporting the productive human assets. The authors have found that despite the rhetoric for youth and employment, the youth arena has been neglected of practical and relevant research. MF industry can advance thinking for young people market. We are finding that MF may impact business owners to improve workplace conditions. Loans also contribute to increase learning, higher wages and lower work hours for young people who work. Research limitations/implications Lack of current studies focused on young people and MF. Studies carried out are based on very small samples and vignettes. A recently completed study carried out by MEDA/PTE with financial support from CIDA shows MF impacts on children as workers and business owners but there is plenty of opportunity for increasing levels of research in this area. Originality/value This paper shares original case material from Egypt's project, to share lessons on the ground and design and implementation learnings. This paper will be of interest to youth serving organizations, MFIs, banks, child rights community, donors and governments with an interest in children and youth.
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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 | 0.002 | 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 | 0.014 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".