Minimum wage effects on employment and wages: dif‐in‐dif estimates from eastern China
Bibliographic record
Abstract
Purpose The purpose of this paper is to estimate the impact of minimum wages on employment and wages in China. Design/methodology/approach The paper uses the difference‐in‐difference methodology to estimate the employment and wage impacts of the minimum wage increase in 2003 – a year when substantial minimum wage increases occurred in some provinces (treatment provinces) but not in others (comparison provinces). The analysis is restricted to the eastern region so as to make comparisons across relatively homogeneous and contiguous provinces with large numbers of women and rural migrant workers in urban areas – the target groups for minimum wages. Findings The study finds that overall, minimum wages in China do have an adverse employment effect but the effect is statistically insignificant and quantitatively inconsequential. The adverse employment effects are generally larger in the more market‐driven sectors, in the low‐wage sector of retail and wholesale trade and restaurants, and for women; however even these effects are extremely small. Minimum wages also had no impact on aggregate wages. These estimates appear consistent with many of those based on this methodology which tends to find no substantial adverse employment effect from minimum wages. Practical implications Good news: minimum wages do not seem to have any substantial adverse employment effect in China. Bad news: this could simply reflect the fact that they are not enforced. Originality/value This is one of the few studies of effect of minimum wages in China in English, and using a difference‐in‐difference methodology as first employed by Card.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".