Determinants of Households’ Access to Informal Credit: Evidence from Uganda
Bibliographic record
Abstract
Informal credit which is the most dominant in Uganda’s credit system involves loans provided by informal financial institutions not under the control of government or Bank of Uganda. This study examined the determinants of access to informal credit among households in Uganda. A logit model was used to establish the extent to which independent variables could explain access to informal credit among households in Uganda. Uganda National House hold survey (UNHS) data (2019/2020) was used. The results revealed that informal credit was positively and significantly influenced by; region, education, income, sector of employment and marital status. However, access to informal credit in Uganda was negatively and significantly influenced by residence as well as gender of the household head. The key policy recommendations; evidence showed that education had a positive association with access to informal credit therefore for better access and utilization of informal credit government should widen and strengthen its financial literacy programs. On the sector of employment, evidence indicated that informal credit was accessed mostly by people employed in the production sector which is mainly composed of Small and Medium Enterprises (SMEs). Therefore, government should provide tailor made credit specifically for SMEs to boost production. Additionally, evidence from the results also revealed that informal credit was accessed more by women. The policy recommendation is that government should widen and strengthen gender friendly policies and programs in support of informal financial credit directed to women financial needs. Concerning variable income, findings indicated that income had a positive association with access to informal credit. The policy recommendation therefore is that government should strengthen its socio-economic empowerment and livelihood programs to enhance household incomes so that households are able to invest and pay back the loans.
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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 | 0.000 | 0.000 |
| 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 | 0.001 | 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".