Multiple Principals and Collective Action: China's Rural Credit Cooperatives and Poor Households' Access to Credit
Bibliographic record
Abstract
Ample empirical evidence suggests that Rural Credit Cooperatives (RCCs), which are the core credit institutions in rural China, are not accountable to their member households. This article argues that this conundrum can be explained by an institutional analysis of the credit cooperatives using the multiple principals-agent framework: the credit cooperatives as agents are accountable to multiple heterogeneous principals—with multiple conflicting objectives. The multiple principals are (1) the County RCC Unions, which exercise control using the evaluation criteria on which the remuneration of grassroots RCC officers is assessed; (2) local party secretaries, who exert influence through top personnel appointment and dismissal in the credit cooperatives; and (3) member households, which are a “collective” principal. In a multiple-principals scenario, the “collective” principal has weaker control over the agents due to the “collective action” problem.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".