Access to formal finance in the PRC: A rural and urban comparison
Bibliographic record
Abstract
It is commonly held that rural households in the PRC, particularly poorer households, have been credit rationed by government lenders. Credit rationing refers to the situation where lenders limit the supply of credit to borrowers who demand funds. This thesis investigates how extensive the problem of credit rationing is and the differences between rural and urban households ability to access finance. The study used data from China's Household Finance Survey (CHFS) in 2011 to explore credit rationing in formal credit markets. Taking into consideration both the household's propensity to borrow and the financial institution's probability to approve loans, a bivariate probit model with sample selection was used for the analysis. Political status and "Hukou" emerged as key determinants of access to credit among rural households, while wages and assets play more important roles for urban households. It was found that significant regional variation was evident. The results suggest that the likelihood to borrow is influenced by the age of the head of the household and their education level, and the likelihood of being credit rationed depends mainly on the household's ability to repay the loan and their creditworthiness. Efforts to improve access to credit markets would be effective not only when credit supply is increased, but also when determinants of low participation rates of formal finance markets are taken into consideration.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".