The Impact of Minimum Wages on Wages, Wage Spillovers, and Employment in China: Evidence from Longitudinal Individual-Level Data
Bibliographic record
Abstract
We use the substantial variation in both the magnitude and frequency of minimum wage changes that have occurred in China since its new minimum wage regulations in 2004 to estimate their impact on wages, wage spillovers, and employment. We use county-level minimum wage data merged with individual-level longitudinal data from the Urban Household Survey for the period 2004–09, spanning the period after the new minimum wage regulations were put in place. Our results indicate that minimum wage increases raise the wages of otherwise low-wage workers by a little less than half (41%) of the minimum wage increases. Depending upon the specification, these wage effects also lead to a 2 to 4 percentage point reduction in the probability of being employed, with a 2.8 percentage point reduction being our preferred estimate. We also find statistically significant but very small wage spillovers for those whose wages are just above the new minimum wage, but they are effectively zero for those higher up in the wage distribution.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".