The impact of minimum wages on wages, wage spillovers, and employment in China: Evidence from longitudinal individual‐level data
Why this work is in the frame
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Bibliographic record
Abstract
Abstract We utilize 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 2004–2009, 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 on the specification, these wage effects also lead to a 2–4 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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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it