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Record W1497849236

Public Infrastructure Financing: An International Perspective

2009· article· en· W1497849236 on OpenAlexaboutno aff
Ben Chung-Lap Chan, Danny Forwood, Heather Roper, Chris Sayers

Bibliographic record

VenueRePEc: Research Papers in Economics · 2009
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPublic-Private Partnership Projects
Canadian institutionsnot available
Fundersnot available
KeywordsFinanceAllocative efficiencyBusinessPublic infrastructureTransaction costEquity (law)Government (linguistics)Investment (military)Private sectorProject financeCritical infrastructurePublic economicsEconomicsEconomic growth
DOInot available

Abstract

fetched live from OpenAlex

General government investment in infrastructure has fallen in recent years for most of the countries in this study, (information is not available to assess whether this is true for public investment more generally). Nevertheless, overall investment in infrastructure has remained fairly steady in recent years, although volatile in some countries. Total Australian investment in infrastructure has rebounded in recent years to just below 6 per cent of GDP in 2006-07. Sub-national governments undertook 76 per cent of public infrastructure investment, with government trading enterprises accounting for around half of this. With the global financial crisis, governments are looking to infrastructure investment as a way of stimulating the economy. But financing options have also been constrained by the crisis. Financing decisions are separate from the investment decision and can be made independently. Financing differs from public funding - the latter being the commitment of public revenue to meet any gap between the costs of infrastructure provision and the revenue from user charges. Funding decisions carry an opportunity cost and deadweight loss of raising taxes. Budget appropriations, financed on a pay-as-you-go basis or from public debt, remain the major form of financing for government investment in infrastructure (63 per cent in 2006-07). Specific-purpose bonds, where repayment is linked to the performance of the asset, are a major source of finance in the United States and Canada, but were phased out in the 1980s in Australia. Public-private partnerships (PPP), where the government contracts a private partner to variously finance, design, build and operate infrastructure assets for a fixed period, are growing in use. Used extensively in the United Kingdom, in Australia they made up 6 per cent of public investment in 2006-07 - higher in New South Wales and Victoria. Some approaches used to finance public infrastructure can improve efficiency and lower the life-time project cost through - better management of project risk by aligning incentives for risk management with the capacity to manage the risk; improvements in information, contract negotiation and management and other transaction activities that pay-off in better risk management and cost savings; bringing greater market or other scrutiny to bear on the investment, and imposing the costs on potential beneficiaries to better reveal their willingness to pay. The most efficient financing vehicle will depend on the nature of the investment, the degree of asymmetry of information, the potential for competition, and the skills of the government as negotiators and contract managers. The potential for governments to shift risk onto private partners may be limited, and any non-diversifiable risk assumed by the private sector will be reflected in their required rates of return. PPPs offer considerable potential to reduce project risk, but are costly to transact. If such transactions are off-budget, this may inhibit the scrutiny needed to ensure efficient investment. The views expressed in this paper are those of the staff involved and do not necessarily reflect those of the Productivity Commission.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0050.007
Science and technology studies0.0010.002
Scholarly communication0.0090.010
Open science0.0010.002
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0240.002

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.

Opus teacher head0.055
GPT teacher head0.321
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations70
Published2009
Admission routes1
Has abstractyes

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