How Does Corporatization Improve the Performance of Government Agencies? Lessons From the Restructuring of State-Owned Forest Agencies in Australia
Bibliographic record
Abstract
Corporatization, or the adoption of more business-like practices or governance arrangements by government agencies, has been shown to lead to improvements in performance. However, why corporatization leads to improved performance is not well understood. There are competing theories as to how corporatization may improve performance, but because of confounding factors empirical studies have difficulty in identifying causal relationships. We address these issues in our analysis of the corporatization of six Australian state forest agencies that took place in the past two decades, focusing on: (1) improvements in efficiency and (2) improved profitability or cost recovery. The results confirm that corporatization leads to enhanced commercial performance through improving clarity around business decisions and increasing the autonomy of managers. A key feature is the establishment of new governance arrangements and how they are implemented. Our results suggest that mechanisms such as the creation of an “independent” board of directors are important to create an “arm's length” relationship between the government and the new entity, thereby improving clarity in business decisions.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".