Can cooperatives help commercial farms to access credit in China? Evidence from Jiangsu Province
Bibliographic record
Abstract
Abstract Chinese agriculture is experiencing a transition from smallholder farming to the emergence of commercial farms that are characterized by intensification and specialization in production, as well as commercialization and cooperation in management. It requires substantial capital to facilitate such a transition, but it is very difficult for farmers in China to access bank credit. One way that commercial farms have to overcome such handicap is by organizing themselves into cooperatives. To assess the effect of cooperatives on the credit accessibility of commercial farms, we have developed a theoretical model as well as an empirical study of commercial farms in Jiangsu Province based on data from a survey of 754 commercial farm owners. Instrumental variable (IV) methods and the Propensity Score Matching (PSM) method that control endogeneity problem are used in the analysis. The empirical results show that cooperatives have a significant positive impact on the credit access of commercial farms. Commercial farms participating in cooperatives may alleviated their credit constraints by about 17.3 percentage points and increase the average credit per capita by nearly 80,000 Yuan. Cooperatives improve the credit access of commercial farms by exerting strong market power and reputation effect based on its organizational advantages. A disaggregated analysis also reveals that small commercial farms tend to benefit more from cooperatives in improving credit access than large commercial farms.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".