Bibliographic record
Abstract
Income inequality is a major problem in Thailand. A key determinant of income inequality in Thailand is the lack of financial access to financial institutions for low-income families. Microfinance institutions (MFIs) play an important role in enabling poor households to access financial resources at a reasonable cost. The purpose of this paper is to investigate factors that affect Thai households participating in microfinance programs in Thailand. A multinomial logit model is used to investigate the factors that impact the Thai households’ access to microfinance. The study employs secondary data from the Thai Socioeconomic Survey (cross-sectional data in 2017) to identify factors affecting Thai household participation in microfinance programs. The results show that the Village Fund (VF) targets low-income rural households and encourages those with older household heads who have lower levels of education, and female household heads, to participate in their program. Larger households are more likely to access the VF. Households with higher dependency ratios are less likely to borrow from the VF. Households with well-educated, young household heads in regional areas are more likely to borrow money from Saving Groups for Production (SGPs). SGP borrower households have higher household incomes than VF borrower households. Our findings indicate that VFs and SGPs are credit sources in the rural credit market; these sources enable rural households to access credit to meet their needs. In addition, rural Thai households borrow from many sources so that they can rotate their loan repayments. Low-income households refinance their loans by borrowing from different sources.
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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 | 0.001 | 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 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".