Unpacking the patterns of corporate restructuring during China’s SOE reform
Bibliographic record
Abstract
State-owned enterprises (SOEs) in China have undergone significant restructuring since the mid-1990s. To date, scholars have devoted considerable attention to the constraints upon and motives for corporate restructuring in China. Yet the majority of the existing studies treat restructuring as a simple ownership transfer from the state to non-state entities without considering the resulting ownership structure of the firm. Consequently, we know relatively little about why otherwise similar SOEs were restructured at different times and through different means. This study intends to fill this gap by examining the determinants of both the timing and the methods of restructuring in a unique longitudinal survey of 145 SOEs over an 11-year period. Using a competing-risks model, we demonstrate that political as well as economic factors determine the possibility, nature and speed of restructuring. In particular, we show that political constraints on employee retention increase the likelihood that a SOE will be restructured as shareholding as opposed to its ownership being directly transferred to private hands. These findings shed new light on the economic and political logic of corporate restructuring in China.
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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 | 0.002 |
| 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) | 0.001 | 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".