The Incidence of Social Security Payroll Taxes: Evidence From China
Bibliographic record
Abstract
The Chinese government enforced public security system reform in the economic transition period. Now, the enterprise’ social insurance premium, a kind of payroll tax, is nearly 40% of the total wage in China. It is thought enterprises may transfer the burden of payroll taxes to workers by reducing their wages. Does the level of an enterprise’s social security payroll taxes influence their workers’ wages? Using the Chinese Large and Medium-size Manufacturing Enterprises (CLMME) dataset to construct an enterprise panel data from 2004 to 2007, we employ an empirical study to provide evidence on the issue. We utilize the fixed effects model, random effects model and Generalized Method of Moments (GMM) method to address the heterogeneity problem, initial dependent problem and endogenous problem. It is found that in general, increased social security payroll taxes negatively affect the workers’ wages, which indicates that many enterprises may transfer the payroll taxes burden onto their workers. Increased social security payroll taxes may decrease the wage levels for workers in both the public sector and the private sector, but the negative effect is greater for workers in the private sector than in the public sector.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".