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Record W4220996732 · doi:10.1017/s0008423922000038

Deficit or Austerity Bias? The Changing Nature of Canadians’ Opinion of Fiscal Policies

2022· article· en· W4220996732 on OpenAlexafffundabout
Olivier Jacques, Éric Bélanger

Bibliographic record

VenueCanadian Journal of Political Science · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policies and Political Economy
Canadian institutionsMcGill UniversityUniversité de Montréal
FundersFonds de Recherche du Québec-Société et Culture
KeywordsAusterityPopularityDeficit spendingGovernment (linguistics)Fiscal policyEconomicsPoliticsPublic economicsEconomic policyPublic opinionFiscal imbalancePolitical economyPolitical scienceFiscal unionMacroeconomicsDebt

Abstract

fetched live from OpenAlex

Abstract Public choice theory suggests that citizens have a deficit bias: they approve governments for running large deficits that increase spending or reduce taxes. In contrast, others contend that citizens reward governments for balanced budgets. We contribute to this debate by modelling a popularity function for the Canadian federal government and show that the impact of fiscal policies on the executive's popularity changes over time. Until the early 1990s, Canadians preferred budget deficits. As deficits became unsustainable during the economic crisis of the early 1990s, the government shifted its fiscal policy paradigm, as balancing the budget became its primary fiscal objective and citizens were actively concerned about the deficits. Since 1993, citizens’ deficit bias morphed into an austerity bias: executive approval increases when deficits are reduced. These findings contribute to comparative political economy research by assessing how policy regimes and public preferences reinforce each other.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.834
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.046
GPT teacher head0.259
Teacher spread0.213 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations6
Published2022
Admission routes3
Has abstractyes

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