Bibliographic record
Abstract
This study is a statistical disparity analysis of the wages of the staff and working (Zhigong) class in China. The Chinese government strictly controlled the wage system of state owned enterprises before the reform and opening of China. However, this system is gradually being reformed and each enterprise can independently decide their own wage system. As a result, the wage disparity has expanded since the reform and opening of China. In 2006, the staff and workers (Zhigong) were 110 million people, which is about 14.6 percent of the workers and about 8.5 percent of the population of China. To understand the recent wage disparities in China, disparity was estimated with a one stage Mean Logarithm Deviation Decomposition Index and from two directions in the decomposition pattern of disparity across region and industrial sector. Several findings are presented in this paper. First, a rapid expansion of disparity occurred during the measurement period. The index was below 0.02 at the start and increased to about 0.08 at the end. Second, the main factor of disparity gradually changed from regional disparity to sector disparity. Third, the regional disparity in each sector expanded in the higher value sectors but decreased in the agriculture and industry sectors. Fourth, the tendencies in the disparity of each sector in each region differed. From these results, wage disparity is a very serious problem in China. Therefore, several difficult correspondences are required from the government to reduce various disparities in the future.JEL classification: J31, O5
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".