How does land titling affect credit demand, supply, access, and rationing: Evidence from China
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 | 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 it