The Impact of Village Savings and Loan Associations as a Financial and Climate Resilience Strategy for Mitigating Food Insecurity in Northern Ghana
Why this work is in the frame
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Bibliographic record
Abstract
In semi-arid Northern Ghana, smallholder farmers face food insecurity and financial risk due to climate change. In response, the Village Savings and Loan Association (VSLA) model, a community-led microfinance model, has emerged as a promising finance and climate resilience strategy. VSLAs offer savings, loans, and other financial services to help smallholder farmers cope with climate risks. In northern Ghana, where formal financial banking is limited, VSLAs serve as vital financial resources for smallholder farmers. Nevertheless, it remains to be seen how VSLAs can bridge financial inclusion and climate resilience strategies to address food insecurity. From a sustainable livelihoods framework (SLF) perspective, we utilized data from a cross-sectional survey of 517 smallholder farmers in northern Ghana’s Upper West Region to investigate how VSLAs relate to food insecurity. Results from an ordered logistic regression show that households with membership in a VSLA were less likely to experience severe food insecurity (OR = 0.437, p < 0.01). In addition, households that reported good resilience, owned land, had higher wealth, were female-headed, and made financial decisions jointly were less likely to experience severe food insecurity. Also, spending time accessing the market increases the risk of severe food insecurity. Despite the challenges of the VSLA model, these findings highlight VSLAs’ potential to mitigate food insecurity and serve as a financially resilient and climate-resilient strategy in resource-poor contexts like the UWR and similar areas in Sub-Saharan Africa. VSLAs could contribute to achieving SDG2, zero hunger, and SDG13, climate action. However, policy interventions are necessary to support and scale VSLAs as a sustainable development and food security strategy in vulnerable regions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it