The Rise and Fall of Local Elections in China: Theory and Empirical Evidence on the Autocrat's Trade-off
Bibliographic record
Abstract
We propose a simple informational theory to explain why autocratic regimes introduce local elections. Because citizens have better information on local officials than the distant central government, delegation of authority via local elections improves selection and performance of local officials. However, local officials under elections have no incentive to implement unpopular centrally mandated policies. The model makes several predictions: i) elections pose a trade-off between performance and vertical control; ii) elections improve the selection of officials; and iii) an increase in bureaucratic capacity reduces the desirability of elections for the autocrat. To test (i) and (ii), we collect a large village-level panel dataset from rural China. Consistent with the model, we find that elections improve (weaken) the implementation of popular (unpopular) policies, and improve official selection. We provide a large body of qualitative and descriptive evidence to support (iii). In doing so, we shed light on why the Chinese government has systematically undermined village governments twenty years after they were introduced.
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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 | 0.020 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".