Optimal Public Infrastructure: Some Guideposts to Ensure we don’t Overspend
Bibliographic record
Abstract
It’s time to consider a more economically efficient model for financing roads, bridges and other public infrastructure. It’s true that Canada has become one of the biggest spenders on infrastructure among OECD countries, at four per cent of GDP, but using GDP to measure the share the government should spend on infrastructure is an anachronistic and arbitrary measure. We all know it is important that Canada keep pace with maintaining and building the necessary infrastructure to maximise our productive capacity and economic prosperity. But how do we know if we are on the right track? How much investment is enough, and what is the optimal level of public investment in infrastructure? This paper proposes a framework for evaluating current and future levels of financing for public infrastructure. Rather than relying on arbitrary comparisons with Canadas post war ‘golden age’ of infrastructure investment (an all too common standby in political circles), we propose a standard that is based in economic efficiency and which aims to maximise the public benefits associated with infrastructure investment. We also take a historical look public capital spending in Canada, as well as the trend toward privatization of public infrastructure and core services that began some 30 years ago after the Mulroney government was elected. This trend has seen many core services and assets that were once publicly run transition to outright privatization. It is interesting to note that the most heavily privatized sectors (utilities and communications) are also the sectors most often spared from the label of ‘inadequate’, a label that befalls so much of Canada’s public infrastructure. When infrastructure is financed through taxation, there is a tendency for spending to be discouraged to ease the burden on taxpayers; however, this inevitably leads to infrastructure maintenance and construction being deferred, with a significant deficit inevitably built up. A user-pay model would work to eliminate political influence, create revenue for infrastructure renewal, and facilitate an optimal allocation of infrastructure resources. All of this further helps maximise the benefits derived from public infrastructure. This model of infrastructure finance and provision could be further advanced and reinforced through the creation of provincial and federal bodies whose mandate would be to actively evaluate infrastructure investments in their respective jurisdictions, prioritizing funding for the most meritorious projects, and those offering the highest public return on investment. When projects are funded through taxation and access is not priced, there is often little or no incentive for individuals to make efficient use of them. The lack of direct accountability means individuals fail to use infrastructure judiciously and sparingly to preserve the life of public assets or prevent unnecessary congestion. Moreover, the lack of a clear sense of cost means governments do not know the true value that the public places on one type of infrastructure over another. Thus, government budgeting for such projects remains inefficient and skewed. The best level and mix of public infrastructure can only be determined when government and private providers can reliably establish user demands in a priced (efficient) system. The current model of funding public infrastructure deprives users of infrastructure as well as government planners from vital information they need to make informed and efficient decisions. Both remain unclear on value for money, and costaccountability, but are bound by an innate aversion to increased tax-financing. Governments, fully cognizant of both consumer attitudes and the need to retain vote-getting power, thus swing between funding necessary infrastructure and allowing infrastructure deficits to grow. This paper advocates for a more efficient, accountable system with greater dependence on user-pay models and reinforced by and active arm’s length government agency designed to advance merit based project selection, and maximise public benefit.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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 teacher head, 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".