Is microfinance better compared to other financial institutions? Analyzing the impact of various financial ac-cess on household welfare in Indonesia
Bibliographic record
Abstract
This study examines how access to different types of financial services influences household welfare in Indonesia. Using data from a large sample of 331,068 households, the research applies Ordinary Least Squares (OLS) regression to evaluate the impact of financial institutions on household income, which serves as a proxy for welfare. The findings reveal that access to microfinance and commercial banks significantly improves household income, highlighting the critical role these institutions play in enhancing welfare. Conversely, households relying on informal financial institutions tend to have lower incomes, indicating a negative effect on welfare. Further analysis reveals important variations based on gender and geographic location. Microfinance and cooperatives are particularly beneficial for female-headed households and those in rural areas, underscoring their importance in supporting underserved populations. On the other hand, access to commercial banks benefits both male and female headed households but has a stronger impact in urban areas where formal banking services are more readily available. While informal financial institutions negatively affect urban households, they provide modest advantages for female-headed and rural households, serving as an alternative in areas lacking formal financial services. These findings underscore the need for targeted financial inclusion policies that address gender and regional disparities. Such policies should prioritize expanding access to microfinance and cooperatives for rural and female-headed households while also improving access to formal banking services in urban areas to enhance overall welfare.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".