Occupational segregation and the gender earnings gap in China: devils in the details
Bibliographic record
Abstract
Purpose – The purpose of this paper is to analyze the gender earnings gap in China with a focus on the role of differences in the occupational distribution of males and females. Design/methodology/approach – The authors use a procedure to model occupational attainments and decompose differences in earnings into an inter-occupational portion due to differences in the occupational distribution between males and females, and an intra-occupational portion due to differences in pay. The analysis is based on Chinese census data. Findings – The authors find that the male-female pay gap is virtually completely explained by wage discrimination defined as females being paid less than males within the occupation groups based on six broad occupations. Occupational segregation explains virtually none of the overall male-female pay gap, and in fact the “segregation” slightly favors women. However, the picture changes substantially when the analysis is conducted at the more disaggregate sub-occupation level within each of the six broad groups. Wage discrimination remains the prominent contributor to the pay gap across the disaggregated sub-occupations in each of the broad occupations. But there is considerable heterogeneity in the effect of occupational discrimination within the sub-occupations within the different broad occupational groups. Social implications – When females have the same occupation-determining characteristics as men, they are in lower paying sub-occupations within the professional group and to a lesser extent within manufacturing and operations jobs. There is considerable heterogeneity in the effect of occupational discrimination within the sub-occupations in the different broad occupational groups. Originality/value – The paper systematically examines the degree to which the gender earnings gap in China is due to the differences in occupational distributions of males and females, highlighting that the conventional Blinder-Oaxaca decompositions can under- or over- estimate the unexplained portion of the gender pay gap by controlling or not controlling for differences in the occupational distribution of males and females. The paper also shows that previous studies that have examined occupational segregation across aggregate occupational groups can mask important differences in the effect of occupational discrimination within the sub-occupations in the different broad occupational groups.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".