Ways to improve the multi-level coordination of public investment management in Ukraine to ensure its effectiveness
Bibliographic record
Abstract
This article is dedicated to the study of multi-level coordination in public investment management, the analysis of experiences from OECD member countries, and the identification of ways to improve the multi-level coordination of public investment management in Ukraine, ensuring its effectiveness. It is emphasized that coordination is one of the three systemic challenges to multi-level public investment management that hinder the achievement of optimal outcomes. The complexity lies in the practical provision of intersectoral, interjurisdictional, and intergovernmental coordination, as well as in aligning the interests of the numerous stakeholders involved in public investments. The need to consider the context of decentralization for multi-level coordination is underscored, as it represents a challenge that involves delegating responsibilities, decision-making, and financial powers to lower levels of government. Three main dimensions of multi-level coordination are distinguished: institutional, territorial, and public governance. Two primary types of coordination are identified: vertical (coordination of decisions between national and subnational authorities) and horizontal (coordination between authorities within the same level of governance, whether national or subnational). It is concluded that vertical coordination is essential for aligning goals between central and lower levels of governance and is critical for improving the effectiveness and outcomes of public investments. The experiences of Canada, Greece, Austria, Australia, Italy, and the Netherlands in ensuring vertical coordination between levels of governance are outlined. The article also concludes that horizontal coordination is necessary to meet investment needs at the appropriate scale and avoid fragmentation, as infrastructure needs and projects, for example, often span jurisdictional boundaries. It is noted that significant administrative, financial, and political costs, as well as the absence of a joint investment strategy with neighboring cities/regions and incentives for cooperation between different jurisdictions, pose obstacles to effective horizontal coordination of public investment management at the local level. The experiences of Spain, the United Kingdom, Poland, and Iceland in ensuring horizontal coordination are analyzed. The article outlines the problematic aspects of multi-level coordination in public investment management and the risks to the effectiveness of multi-level coordination in Ukraine’s public investment management. Based on the experiences of OECD member countries in improving multi-level coordination of public investment management, a number of measures are recommended for adoption in Ukraine to enhance this coordination.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".