The impact of China's millennium labour restructuring program on firm performance and employee earnings<sup>1</sup>
Bibliographic record
Abstract
Abstract Around the turn of the century, China experienced perhaps the largest labour restructuring program in the world. This paper uses a new dataset of Chinese industrial enterprises to examine what leads to downsizing, and tries to understand the effects of labour downsizing on firms’ technical efficiency, financial performance and employee wages. We find that downsizing is more prevalent in state‐owned enterprises (SOEs), and is more likely when enterprises are older, larger and have higher excess capacity. For both SOEs and private firms, downsizing is more likely when the prices of their products drop, but private firms respond more dramatically. Moreover, downsizing has serious short‐term costs in terms of total factor productivity (TFP). For mild downsizing, private firms suffer more deterioration in productivity. The distribution of surplus after downsizing is more favourable to labour in SOEs. For severe downsizing, both SOEs and private firms exhibit lower TFP growth with similar magnitudes. Our findings imply that private firms emphasize profit goals, while SOEs place a greater weight on labour protection.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".