How does land titling affect credit demand, supply, access, and rationing: Evidence from China
Bibliographic record
Abstract
Abstract Based on official survey data from the Chinese Ministry of Agriculture and Rural Affairs collected in 2010 and 2015, we use the difference‐in‐differences method to study how the Chinese land titling reform beginning in 2009 in tiers (“the Reform”) affected the demand, supply, access, and rationing on the Chinese rural credit market. Our main findings are: (1) the Reform increased households’ hidden credit demand, but not their effective credit demand; (2) the Reform had no significant effect on effective credit supply or a household's credit access; (3) the Reform increased the likelihood of non‐price credit rationing, in particular risk rationing; and (4) in the subsample of households living in counties where the local governments explicitly permitted the use of land as collateral, the Reform had a positive effect on credit supply; but in the subsample of households living in counties where land collateral was not explicitly permitted, the Reform was associated with an increase in non‐price rationing. Findings of this study are not only useful to assess the economic and social implications of rural land titling in China, but they also offer insights in understanding similar policies in other countries, particularly developing economies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".