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Record W2281444009 · doi:10.55016/ojs/sppp.v8i1.42547

Striking the Right Balance: Federal Infrastructure Transfer Programs, 2002–2015

2015· article· en· W2281444009 on OpenAlexaffabout
Bev Dahlby, Emily Jackson

Bibliographic record

VenueThe School of Public Policy Publications · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPublic-Private Partnership Projects
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBalance (ability)Transfer (computing)BusinessComputer sciencePsychology

Abstract

fetched live from OpenAlex

Over the last 13 years, the federal government has helped fund a wide array of infrastructure programs: A total of 8,012 projects across the country between 2002 and 2015, funded to the tune of $20.3 billion. A substantial portion of that was done in the name of recession “stimulus.” But far from all of it. And, for better or worse, federal programs have become a permanent feature of fiscal federalism. The only question now is, whether Ottawa has been spending federal taxpayer money as effectively as possible when it does fund these projects. As it turns out, federal handouts for projects in Canadian provinces and municipalities have been relatively well deployed. An analysis finds that a greater amount of federal matching funds were dedicated to projects where provinces faced a higher marginal cost of public funds than the federal government, helping to at least somewhat minimize the negative economic impacts of the additional tax burden. And that a greater amount of funds was dedicated to projects that enhanced economic productivity, such as transit and roads, which increase the probability for national spillover benefits due to the potential for increased federal tax revenue, unlike quality-oflife projects (such as recreation projects) that do not. However, the persistent fiscal imbalance in the provinces’ and the federal government’s marginal cost of raising public funds can only continue to exacerbate the demands from provinces for federal matching funds. Despite federal fiscal equalization programs that provide transfers to provinces with below-average per capita tax bases, there remain notable horizontal fiscal imbalances across the provinces, and a vertical imbalance between lower and higher government levels. Recent estimates calculate the federal government’s marginal cost to be 1.17, compared to a range of 1.41 for Alberta to 3.60 for Ontario, more than three times as high as the federal government’s cost. There are already several programs that provide large block funding transfers to provinces: The Canada Health Transfer, the Canada Social Transfer, the Gas Tax Fund, and federal equalization grants. These block transfers reduce the fiscal imbalance between Ottawa and the provinces, but they have clearly not closed the gap completely. Were the federal government to increase these block transfers, it could arguably reduce its role in funding individual infrastructure projects, thereby encouraging lower levels of government to plan infrastructure more rationally, rather than being influenced by the distortions created by federal matching offers. Indeed, among all the projects that received federal matching funds since 2002, a concerning number were smallscale projects. More than half of the 8,000 projects funded had eligible costs of $1 million or less, and a startling 92 per cent had eligible costs under $10 million. A thousand were below $100,000. Small projects may have their benefits as a stimulus response if they are “shovel ready,” since large projects may require too much planning to offer the rapid employment and spending benefits desired. But the costs of co-ordination for small projects across multiple levels of government add inefficiencies and so should generally be avoided. Again, by providing more in the form of block grants, Ottawa can leave smaller stuff to smaller governments, where it, and much else, properly belongs.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.917
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
Science and technology studies0.0010.000
Scholarly communication0.0030.004
Open science0.0030.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.058
GPT teacher head0.303
Teacher spread0.245 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations0
Published2015
Admission routes2
Has abstractyes

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