Residual State Ownership and Firm Performance: A Case of Vietnam
Bibliographic record
Abstract
Privatization has played an important role in national economic reform in Vietnam. However, unlike other transitional countries in Central and Eastern Europe, Vietnam has chosen a partial and gradual privatization where the government still holds significant ownership in most privatized firms. Whether partial privatization can enhance privatized firms’ performance or full privatization should have been implemented is a critical question that needs to be answered. This paper utilizes semiparametric regressions to study the relationship between residual state ownership and firm performance. The results indicate an inverted U relationship between state ownership and firm performance. We show that the performance of privatized firms improves with an increase in the level of state ownership until around 40%, after which the effect of state ownership on firm performance tends to decline. This demonstrates that in a transitional context, relinquishing governmental control via privatization can significantly benefit privatized firm performance. However, further reduction of state ownership may decrease the performance of privatized firms. Overall, the study contributes significantly to the growing body of evidence on the nonlinear effects of state ownership. This suggests that in the transitional context of Vietnam, due to weak corporate governance and limited protection of minority shareholders, there could be a temporary optimal position where state and private investors hold balanced ownership to simultaneously supervise operations and promote the performance of privatized firms.
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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 | 0.002 |
| 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 | 0.001 |
| 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".