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Record W4320153526 · doi:10.32721/ctj.2022.70.4.fon

Finances of the Nation: Mitigating the Economic Impacts of Population Aging on Growth and Public Revenues—Can the Tax Mix Help?

2022· article· en· W4320153526 on OpenAlexvenueaboutno aff
Bertrand Achou, Yann Décarie, Luc Godbout, Pierre‐Carl Michaud, Julien Navaux

Bibliographic record

VenueCanadian Tax Journal/Revue fiscale canadienne · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsRevenueTax revenueConsumption (sociology)Government revenuePublic economicsEconomicsPopulationStatus quoGovernment (linguistics)BusinessEconomic policyAgricultural economicsFinanceMarket economy

Abstract

fetched live from OpenAlex

In this article, the authors address concerns about the impact that the aging of Canada's population over the coming decades could have on economic growth and, consequently, growth in government revenues. They explore how revenue-neutral changes in the tax mix today could mitigate those concerns and raise more revenue than is projected in current forecasts with a status-quo scenario. Using data for Quebec, the authors show that a shift in the relative share of total revenues from personal income taxes to consumption taxes could be quite effective over the next four decades. A revenue-neutral shift equivalent to 1 percent of the province's consumption tax revenues today would result in an increase in revenues ranging between 0.3 percent and 1.0 percent by 2060, while a shift equivalent to 25 percent of consumption tax revenues would generate additional revenues of 1.4 percent to 4.8 percent.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.192
Threshold uncertainty score0.386

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0050.003
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.018
GPT teacher head0.220
Teacher spread0.202 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2022
Admission routes2
Has abstractyes

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