Glass ceiling or sticky floor? Quantile regression decomposition of the gender pay gap in China
Bibliographic record
Abstract
Purpose – The purpose of the paper is to analyse how the male-female pay gap in China varies across the pay distribution and to provide evidence on the factors that influence that gap. Design/methodology/approach – The authors use the Recentered Influence Function modification of quantile regressions to estimate how the male-female pay gap varies across the pay distribution. The authors also decompose the pay gaps at different quantiles of the pay distribution into differences in endowments of wage determining characteristics and differences in the returns for the same characteristics. The analysis is based on data from the Life Histories and Social Change in Contemporary China survey. Findings – The authors find evidence of a sticky floor (large pay gaps at the bottom of the pay distribution) and some limited and weaker evidence of a glass ceiling (large pay gaps at the top of the distribution). This pattern prevails based on the overall pay gap as well as on the adjusted or net gap that reflects differences in the pay that males and females receive when they have the same pay determining characteristics. The pattern largely reflects the coefficients or unexplained differences across the pay distribution. Factors influencing the pay gap and how they vary across the pay distribution are discussed. The variation highlights considerable heterogeneity in the Chinese labour market with respect to how pay is determined and different characteristics are rewarded, implying that the conventional Blinder-Oaxaca decompositions that focus only on the mean of the distribution can mask important differences across the full pay distribution. Social implications – At the bottom of the pay distribution most of the lower pay of females reflects their lower returns to job tenure, experience and a greater negative effect of family responsibilities on females’ wages, and to a lesser extent their lower level of education, less likelihood of being CPP members and their concentration in lower paying occupations. At the top of the pay distribution most of their lower pay reflects their lower returns on education, job tenure and work experience, and to a lesser extent their lower levels of experience and lower likelihood of being in managerial and leadership positions. Originality/value – The paper systematically examines the male-female pay gap and its determinants throughout the pay distribution in China, highlighting that the conventional Blinder-Oaxaca decompositions that focus only on the mean of the distribution can mask important differences across the full pay distribution and not capture the considerable heterogeneity in that labour market.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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".