Local Policy Experimentation, Social Learning, and Development of Rural Pension Provision in China
Bibliographic record
Abstract
The rural pension system, co-financed by rural residents' contributions and government subsidies, is a remarkable institutional innovation in China. To better understand the establishment and policy design of this system, this article studies the local experimentation of (partly) government-funded new rural pension schemes prior to the national policy guideline issued in 2009. The focus is on the role of social learning as a crucial driving force in this process. Through a process tracing based on in-depth interviews in Daxing of Beijing and Baoji of Shaanxi Province, this article illustrates how local governments struggled to find suitable financing models for rural pensions, and relied primarily on hands-on experimentation and experiences. During the mobilization of participation in the schemes, the repeated and constant interactions between local officials and rural residents promoted a form of mutual learning that contributed to local policy adaptation and rural residents' internalization of the value and basic rules of contributory pension provision. The local experience had a cumulative impact on the ideational reorientation of the central officials regarding the state's financial role in provision. Specifically, the financing model in Baoji created new options that facilitated the reconciliation of a set of different concerns and objectives at the centre, notably fiscal affordability, wide coverage, and modest managerial burden, which, this article argues, was the major reason for the incorporation of this model into the national policy. The article concludes by discussing the implications of the establishment of the rural pension system and its provisions on rural state-society relations in China.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".