PRIVATIZATION IN CHINA: TECHNOLOGY AND GENDER IN THE MANUFACTURING SECTOR
Bibliographic record
Abstract
This paper examines the impact of privatization on gender discrimination in China across firms with different technology intensities. Using a comprehensive firm‐level survey, the paper identifies gender wage‐productivity differentials by directly estimating the relative productivity levels of workers from the production function of firms. The panel structure of the survey is taken advantage of by following firms that were fully state‐owned in the initial year, and distinguishing them from firms that were later privatized. The main results show that privatization was associated with an increase in relative productivity of female workers in high technology industries, and a reduction in relative productivity of female workers in low technology industries. Time varying coefficient results suggest that the improvements in gender outcomes in high technology industries may not be maintained in the long run as the relative wage and productivity ratios tend to deteriorate, potentially due to low supply of highly educated female workers. At the same time, outcomes in privatized low technology industries increase over time, lowering the wage and productivity gaps between male and female workers. (JEL J16, J31, P20)
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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 | 0.001 | 0.001 |
| 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".