Minimum wage impacts on wages, employment and hours in China
Bibliographic record
Abstract
Purpose The purpose of this paper is to estimate the causal effect of minimum wages (MWs) on the wages, employment and hours of migrant workers in China, and to show their inter-relatedness and how employers can offset some of the costs through subtle adjustments. This paper also illustrates the importance of disaggregating by region and sex. Design/methodology/approach Causal estimates are provided through difference-in-differences (DID) analysis, and robustness checks through propensity score matching. The analysis is based on micro data at the individual level from the household survey on migrant workers by the National Population and Family Planning Commission, combined with macro data regarding municipalities’ population, GDP and employment information based on the China Economic Information Network database. Findings MW increases for those paid by the month increased the earnings of both low-wage males and females. However, males tend not to experience an adverse employment effect because part of the cost increase is offset by employers increasing their monthly hours of work. Hours of work do not increase for females, so they experience an adverse employment effect. This highlights the importance of examining cost offsets such as increases in hours of work, as well as analyzing effects separately for males and females. Research limitations/implications The reason behind why employers offset some of the cost increase for males paid by the month by increasing their hours of work, but this cost-offsetting adjustment does not occur for females is uncertain. Social implications For workers paid by the month, employers can offset some of the cost increase by increasing their hours of work, leading to no reductions in employment. But this adjustment occurs only for males. Hours are not increased for females, but they experience reductions in employment. Clearly, MW increases have adverse effects either in the form of employment reductions (for females) or increases in hours of work for the same monthly pay (for males). Originality/value This paper provides causal estimates through DID analysis and robustness checks through Propensity Score Matching, and also indicates how employers can offset the cost of MW increases by increasing hours for those paid by the month, resulting in no adverse employment effect for such workers, but an adverse employment effect when such an adjustment does not occur.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".