Market expansion, state intervention and wage differentials between economic sectors in urban China: A multilevel analysis
Bibliographic record
Abstract
The rising earnings inequality in China has sparked a heated debate on the socioeconomic outcomes of market transformation. While a large body of literature has focussed on the temporal trend of wage inequality during the reform period, much less attention has been devoted to the structural causes of regional variations in sectoral wage differentials. Using a micro-data sample from the 2005 one percent population sample survey and multilevel methods, this article examines the geographic variability of wage differentials between economic sectors in urban China, with a particular focus on the combination effects of market expansion and state intervention. The results indicate that sectoral wage differentials vary substantially across regions, and that market expansion interacts with state intervention to reconfigure earnings outcomes. Specifically, prefectures located in the interior region tend to exhibit a large wage premium for the state sectors, while prefectures located in the coastal region tend to display a wage advantage of the foreign-invested sector. The wage gap between the state and non-state sectors is smaller in areas with diversified ownership; openness to foreign investment increases the relative wages of foreign-invested-sector employees; stringent government regulation of industries increases the wage gap between the state monopoly sector and the non-monopoly sector; and strong redistributive power increases the wage premium for the public service sector over other sectors. Our findings suggest the necessity to take into account contextually constituted and locally specific wage-setting mechanisms when studying China’s wage inequality.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".