GENDER EARNINGS DIFFERENCES IN CHINA: BASE PAY, PERFORMANCE PAY, AND TOTAL PAY
Bibliographic record
Abstract
We utilize a data set that has not been used in literature—the Life Histories and Social Change in Contemporary China (LHSCCC)—to provide new evidence on male‐female pay differences in China. The data set not only enables us to control for a wide range of pay‐determining characteristics but also is the first to enable an analysis of the different components of pay (e.g., base pay and performance pay) as well as for total pay. We find: (1) Women receive about three‐quarters of male pay for each of the dimensions of base pay, performance pay, and total pay, before adjusting for the effect of different pay‐determining factors; (2) Approximately two‐thirds of the gap reflect the fact that females tend to be paid less than males for the same wage‐determining characteristics (often labeled as discrimination), while about one‐third reflects the fact that males have endowments or characteristics that tend to be associated with higher pay, especially supervisory responsibilities, general labor market experience, occupational skills, education, and membership in the Communist party; (3) Marriage has a large positive effect on the earnings of women in China (and none for men), but childcare responsibilities for children under the age of 6 have a large negative effect on the earnings of women although these are offset almost completely if an elder family member is present, highlighting that childcare responsibilities disproportionately fall on women unless an elder family member is present; (4) Pay premiums for higher level skills and higher supervisory ranks are remarkably small for both males and especially females; (5) With respect to the unexplained or “discriminatory” portion of the gap, females get a huge pay penalty for simply being female, but a substantial portion of this gets offset by the higher pay premium they receive for such factors as Han ethnicity, being married, and education. This suggests that discrimination tends to occur in the form of a pay penalty for simply being female and not from lower returns to the same endowments of pay‐determining characteristics. ( JEL J3, J7, M5)
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".