Union pay premium in China: an individual-level analysis
Why this work is in the frame
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Bibliographic record
Abstract
Purpose – The purpose of this paper is to estimate the union-nonunion pay gap impact separately for wages and bonuses as well as total compensation to include both wages and bonuses in China. The way in which the impact varies as control variables are added is illustrated as is how the impact varies by the type of firm ownership. The overall pay gap is also decomposed into a component due to differences in their pay determining characteristics as well as a component due to differences in their returns to those characteristics. These separate components are also calculated throughout the pay distribution. Design/methodology/approach – Using the 2010 China Family Panel Studies Survey, a nationally representative survey in China, the methodology involves different estimation procedures as appropriate for the nature of the data and the dependent variables. First the authors estimate a single equation to determine the union-nonunion pay gap. Then the authors estimate the union impact on the various components of compensation (wages and bonuses). Next the authors decompose the relative contribution of each factor in explaining the wage gap. Finally, quantile regressions are used to examine the union impact across various levels of the pay distribution. Findings – The authors find a gross union-nonunion pay gap (wages + bonuses) of 42 percent, dropping to 12 percent after controlling for the effect of other pay determining factors. The union impact on wages is only 8 percent, but bonuses are about twice as high for union workers. The union impact is essentially zero for (state-owned firms) SOEs and for foreign-owned firms but it is large at 16 percent for private firms and even larger at 22 percent for government agencies. Of the overall pay gap of 42 percent, about three-quarters is attributable to differences in their endowments of pay determining characteristics and about one-quarter to differences in the returns for the same endowments of characteristics. Quantile regressions reveal that the pure or adjusted union wage premium exhibits a u-shaped pattern being highest in the bottom and to a lesser extent the top of the pay distribution. Originality/value – There are a dearth of studies examining the union-nonunion pay gap in China. Of the studies that examine this issue, all of them are at the enterprise level with no studies at the individual level. Taking a nationally representative dataset at the individual level, the authors are able to estimate the union-nonunion pay gap in China. The authors identify the portion of the gap that reflects differences in endowments of pay determining characteristics and the portion that reflects different returns to those characteristics, and the relative contribution of the different variables to those components; and how these components change over the pay distribution. The authors also offer explanations for many of these patterns.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it