Microfinance with education in rural Ghana: Men's perception of household level impact
Bibliographic record
Abstract
Microcredit schemes have been shown to enhance women’s Income Generation Activities (IGA), household food security, and child nutrition. However, spouses or Male Household Heads (MHH) can influence how women’s loans are invested and how incomes ensuing from the investments are expended. This study describes how MHH perceived and experienced the participation of female caregivers from their household in the Enhancing Child Nutrition through Animal Source Food Management (ENAM) project. The ENAM project was designed as an integrated intervention providing microcredit, entrepreneurship and nutrition education to women in rural communities in Ghana. Eighty-five MHH of ENAM project caregivers in two regions of Ghana were interviewed about their awareness of the microcredit and education intervention, their involvement in the IGA that the caregivers’ loans were invested in, and their perceptions of the impact of the project on the caregivers’ IGA as well as household and child nutrition. The majority of MHH indicated that they had been consulted by the caregivers about the decision to participate in the ENAM project. The most common reasons given for consenting to the caregivers’ decision to participate in the program were expectations that the caregiver would receive business capital (30.6%), education on optimal child feeding (36.5%), and income to enable caregivers to contribute more to household expenses (31.8%). With respect to the project’s impact, MHH perceived that the caregivers’ project participation had a positive impact on their business practices, particularly with respect to improved customer relations. The MHH perceived that caregivers’ incomes increased because of their participation in ENAM as evidenced by regular income savings and increased contributions to household food and non-food expenditures. However, MHH reported decreases in their own contributions to almost all household expenditure categories in response to the perceived increase in caregivers’ incomes. The MHH also perceived improvements in home meal quality. In summary, MHH credited the ENAM project with improved caregiver’s incomes and increased share of household expenses. However, this outcome resulted in unanticipated declines in MHH contribution to household expenses. Further studies are needed to understand the impact of empowering women through social experiments on households.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".