The Nexus between Corporate Performance and State Ownership in Vietnam: Evidence of State Ownership’s Inverted U-Shape and Provincial Business Environment Influences
Bibliographic record
Abstract
The level of state ownership in corporations is still a controversial topic because of its duality: on the one hand, it brings resource advantages, and on the other hand, it causes agency problems. Thus, our study aims to investigate the relationship between state ownership and corporate performance within the Vietnamese context, unraveling the impacts of state ownership’s non-linear and provincial business environment. Analyzing financial data spanning over a decade from 359 listed corporations on the Vietnamese stock markets (2010–2021), our empirical findings derived through the General Method of Moments (GMM) reveal that state ownership emerges as a potent “strategic asset” with a positive influence on corporate performance. However, a critical point is identified when state ownership surpasses the threshold of 32 percent and a decline in corporate performance ensues—a confirmation of an inverted U-shaped impact. These results substantiate the necessity of the equitization process and underscore the imperative of judiciously managing state ownership in Vietnam. Notably, our study unveils a more critical dimension: the enhanced provincial business environment bolsters corporate performance and amplifies the positive impact of state ownership. Thus, a strategic dual approach is suggested to improve corporate performance: improving the business environment and recalibrating the percentage of state shareholders. Our study serves as empirical evidence, referencing Vietnam and other transitional economies, toward mannerly policy decision-making related to state ownership and the business environment to boost corporate performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".