Listed public–private enterprises: stock market information, agency costs and productive efficiency outcomes
Bibliographic record
Abstract
Purpose This paper addresses the social value of commercial enterprises that are jointly owned by a government and private sector investors and where the shares are listed on a stock exchange: thus, “listed public–private enterprises” (LPPEs). The theoretical part of the paper addresses how differences in ownership patterns influence the behavior and performance of LPPEs. Design/methodology/approach We develop a conceptual taxonomy, drawing on the empirical evidence on the behavior and performance of public–private hybrid enterprises and on the application of agency theory to that evidence. The taxonomy discussion predicts how different ownership patterns affect enterprise productive efficiency and the ability of governments to achieve social goals through LPPEs. We review the empirical literature on government enterprise ownership and on the concentration of private share ownership to deduce how these matter for owner and managerial behavior and productive efficiency. We review the literature that considers the informational content that listing of an enterprise's shares on a stock exchange can provide to enterprise owners, managers and other domestic audiences with a policy interest. We employ a social welfare perspective to derive policy implications as to when the LPPE governance structure is most appropriate. Findings We show how the monitoring and performance weaknesses of state ownership are offset by some private ownership, particularly when combined with listing on a stock exchange. We demonstrate the effects of different governance structures on enterprise productive efficiency. We find that the LPPE structure is particularly appropriate as an alternative to nationalization or to full privatization and regulation of natural monopoly public utilities, and as an alternative to full private ownership and taxation of non-renewable natural resource extractive enterprises. Originality/value This paper explicitly addresses the question of why and how the combination of government ownership, private investor ownership and listing on an exchange is socially valuable in providing information on productive efficiency to governments.
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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 | 0.003 | 0.017 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".