The Incentive to Innovate? The Behavior of Local Policymakers in China
Bibliographic record
Abstract
Despite playing a key contributing role in China’s economic reforms and the Party’s regime durability, there has been a noted reduction in local policy experimentation. Using semi-structured interviews with policymakers in Beijing, Zhejiang and Shenzhen, we find that although recentralization efforts at the central-level are impacting local officials, a great deal of variation in policy experimentation outcomes still exists. Thus, the puzzle motivating this study is how do local officials react to these institutional changes to decide whether or not to engage in local policy innovation? Our study offers three potential explanations for why local officials vary in their willingness to continue policy experimentation: (1) the ineffectiveness of the vertical reward and punishment systems operated by the Party-state; (2) differing base preferences of local officials; and, (3) the presence of a cohort effect. These factors “filter” institutional changes to result in variation at the local level. As such, we find strong support for an evolutionary process predicated on individual preferences interacting with institutional incentives such as the evaluation system and the networked-structure of cadre knowledge. Although some officials are still conducting policy experimentation, the overall reduction in innovation strongly suggests that potential solutions to governance problems remain trapped at the local level, and that the central government might lose this “adaptable” governance mechanism that has contributed to its past economic and political successes.
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.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".