Building world-class enterprises though mixed-ownership reform: explaining performance differences in minority and majority state-owned enterprises
Bibliographic record
Abstract
Purpose In the context of China’s efforts to build world-class enterprises through mixed-ownership reform, this study aims to build an agency theory framework to analyze the differential relation between ownership structure and firm performance in majority versus minority state-owned enterprises (SOEs). It also evaluates the differential influence that political connectedness has on firm performance in the two types of SOEs. Design/methodology/approach Using a panel data set of Chinese state-controlled mixed-ownership enterprises covering the period 2010–2019, this paper uses ordinary least squares, random-effects, fixed-effects and three stage least squares regression analysis to study the differential impact of ownership structure and political connectedness on firm performance in majority versus minority SOEs. Findings In minority SOEs, firm performance is positively related to the ownership share of the largest private shareholder and state ownership positively moderates this relation. Furthermore, minority SOEs with a politically connected chairman perform worse than those with a politically connected chairman. In majority SOEs, there is no relation between the ownership share of the largest private shareholder and firm performance. In addition, majority SOEs with a politically connected chairman perform similar to those without a politically connected chairman. Originality/value The theoretical framework demonstrates that agency problems are substantially different in minority versus majority SOEs and that this influences how changes in ownership structure and in the type of chairman that is assigned affect firm performance. The empirical analysis confirms these predictions.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".