Adjustments to Minimum Wages in China: Cost-Neutral Offsets
Bibliographic record
Abstract
Based on qualitative interviews of workers, managers and labour inspectors in China, we examine how employers adjust, often in subtle fashions, to minimum wage increases. Our findings highlight the “law of unintended consequences” in that their effects are often “undone” or offset by subtle adjustments such as reductions in fringe benefits and in overtime work and overtime pay premiums that are otherwise valued by employees. Employees often feel that they are no better off in spite of minimum wage increases because of these offsetting adjustments. This study also suggests possible reasons for the small or zero effect of minimum wage on employment in China. Lack of enforcement may be one of the reasons, but the employees we interviewed seem well aware of the legal minimum wage and employers do not want to get involved in disputes over this matter. For employers who would otherwise be affected by the minimum wage increase, the cost increase is mitigated by the offsetting adjustments. As a result, minimum wages do not seem to weaken the competitive position of employers in China.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".