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Record W3110696876 · doi:10.3138/cpp.2021-035

A Pervasive Economic Fallacy in Assessing the Cost of Public Funds

2022· article· en· W3110696876 on OpenAlexaffvenueabout
Marcel Boyer

Bibliographic record

VenueCanadian Public Policy · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCapital Investment and Risk Analysis
Canadian institutionsUniversité de Montréal
FundersAgence Nationale de la Recherche
KeywordsPublic sectorCeteris paribusPrivate sectorBusinessGovernment (linguistics)SubsidyOpportunity costEconomicsFinancePublic economicsMarket economyMicroeconomicsEconomic growthEconomy

Abstract

fetched live from OpenAlex

In the assessment of the cost of public funds, there is a pervasive economic fallacy that is frequently repeated in public policy circles: because the cost of borrowing is higher for a private-sector firm than it is for a public-sector firm, the cost of carrying out an activity (investment, production, distribution, provision of goods and services, and borrowing) will necessarily be lower ceteris paribus in the public sector than in the private sector. The statement is erroneous because part of the government’s cost of borrowing, namely the risk borne by citizens, customers, and taxpayers, is hidden from the casual observer of market interest rates or yields. The all-inclusive borrowing cost, more generally the all-inclusive cost of capital, is the same for both the public and the private sectors. I discuss four specific real cases in which the error is present: the Quebec Generations Fund, the Québec CDPQ Infra Réseau express métropolitain project, the Infrastructure Ontario methodology to assess the riskiness of costs, and the BC Hydro Site C hydroelectric megaproject. I also discuss a general fifth case, namely government support programs for businesses (grants, loans, guarantees, subsidies, etc.), which are generally justified on the fallacious claim that the cost of financing is lower for the government than for the private sector. I propose an auction process by which the true cost of business support programs could be made transparent. I conclude with an appeal for a more rigorous use and management of public funds because miscalculation, misinformation, mismanagement, and fallacious analysis will eventually backfire.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.843
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.065
GPT teacher head0.264
Teacher spread0.199 · 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 designTheoretical or conceptual
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

Citations2
Published2022
Admission routes3
Has abstractyes

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