Privatization and Poverty Reduction in Vietnam Optimal choices and its potential impacts
Bibliographic record
Abstract
Since its transition from a centrally planned economy to a market economy, Vietnam has been under pressure to reduce the size of the state-owned sector. In this process, the private sector has emerged. The objective of this paper is to examine how the privatization could contribute better to economic growth and hence further accelerate poverty reduction in Vietnam. We use the multi-sectorial integrated activity analysis model, and apply it to the data of the Vietnamese economy in 2007 to measure the impacts of ownership restructuring on economic growth. If labour and capital could reallocate across sectors and type of ownership, what would be the optimal allocation of activities and the feasible level of domestic final demand? Factor inputs are capital and four types of labour, namely technicians, high skilled, low skilled and unskilled workers. The model keeps track on asymmetric mobility of labour endowments by skill levels. Main contributions of this paper are fourfold. First, we demonstrate that that at the optimum, privatization does not mean to weaken the economic power of state sector. Second, we propose a specific pattern of SOE reform for Vietnam. Third, alternative experiments on the mobility of labour to shows that there is a trade-off between further privatization toward economic efficiency gain and job-creation in Vietnam, which means privatization does not contribute to job creation. Last, the paper shows that current skill situation of Vietnam's labour force will be a 'bottle neck' for Vietnam economic growth in the near future.
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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 | 0.000 | 0.000 |
| 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) | 0.006 | 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".