Government Subsidisation and Shareholder Wealth Impact: Evidence from Malaysia
Bibliographic record
Abstract
This paper investigates the shareholder wealth impact of government investment in listed companies (and by extension, government subsidisation of those companies), using data from Malaysia. We distinguish two overlapping categories of government-related investors: those whose principal mission relates to economic policy and those whose principal mission relates to social policy. The methodology entails Ordinary Least Squares regressions. There are two dependent variables measuring management success at generating shareholder wealth: an intrinsic value surrogate and return on equity. The final sample comprises 1732 company–year observations from the investigation period 2011–2014. The evidence indicates that companies subject to shareholder by a government-related investor with a social (economic) policy mission are more (less) successful at generating wealth than companies without any government shareholding at all. The findings indicate that for companies subject to ownership by government investors with a mission related to economic policy, government subsidies are wealth-enhancing, subject to diminishing marginal returns beyond a threshold level of government shareholding. The research design reflects adaptations to the Malaysian institutional setting via choice of control variables and usage of data from a leading Malaysian equity analyst.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".