Credit Segmentation and Household Vulnerability in Thailand: Formal Versus Informal Debt Risks
Bibliographic record
Abstract
This study investigates the determinants of household borrowing choices in Thailand, with a focus on the risks associated with formal and informal credit markets. Using cross-sectional survey data from 6949 respondents across 77 provinces collected in September 2021, we employ multinomial regression models to analyze how demographic, occupational, and income factors shape debt outcomes. The results indicate that younger and lower-income individuals in Bangkok are more likely to remain debt-free, while older, higher-income, and farming households are strongly associated with formal borrowing. In contrast, unemployed individuals, retirees, business owners, and freelancers disproportionately rely on informal credit channels, exposing them to high interest rates, repayment difficulties, and heightened financial risk. Regional disparities further underscore structural inequalities: households in the north and northeast are more likely to access formal finance, whereas those in Bangkok and the south tend to turn to informal lenders. These findings highlight the risks of financial exclusion and the persistence of informal lending in emerging economies. Policy measures that expand access to regulated credit, promote microfinance, and strengthen consumer protection frameworks are essential to mitigate household financial vulnerability and reduce exposure to debt traps.
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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 | 0.001 |
| 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".