Fiscal Decentralization and Public Education Provision in China
Bibliographic record
Abstract
After reform and opening up, China is experiencing rapid economic growth but inefficient public services provision. Public education expenditure-to-GDP ratio is too low to keep sustainable growth of China’s social and economic development. Some scholars believe that fiscal decentralization is an important reason. Firstly, this paper analyzes the main factors and path of how fiscal decentralization affects public education provision. While the 1994 tax-sharing reform raised the fiscal revenue of central government, it also increased the fiscal expenditure burden of local governments. Under local officials’ yard-stick competition regime, fiscal decentralization on expenditure may make local governments tend to allocate fiscal expenditure in infrastructure, to attract outside capital to develop local economy, but in the same time, reduce provision of public services, such as education, which has positive externalities. Then, empirical tests based on 1996-2007 prefectural jurisdications panel-data verifies that this phenomenon does exist in China. Further empirical tests make comparisons among different regions and we find that negative effect of fiscal decentralization on public education provision is the highest in Cenral and West China, and the lowest in Northeast China. At last, according to the analysis and empirical results, we give policy proposals on how to improve the public education provision in China.Key words: Fiscal Decentralization; Tax-Sharing Reform; Public Education Provision; Externalities; Panel Data
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".