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Record W2790072190 · doi:10.1080/02732173.2017.1409146

Does Institutional Trust Increase Willingness to Pay More Taxes to Support the Welfare State?

2018· article· en· W2790072190 on OpenAlexaff
Nazim Habibov, Alex Cheung, Alena Auchynnikava

Bibliographic record

VenueSociological Spectrum · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Capital and Networks
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsWillingness to payPublic economicsWelfareRetrenchmentUnit (ring theory)Welfare stateBusinessEconomicsState (computer science)MicroeconomicsPsychologyPublic administrationPolitical scienceLawMarket economy

Abstract

fetched live from OpenAlex

We evaluate the effect of institutional trust on the willingness to pay more taxes to support the welfare state. We found a positive effect of institutional trust on the willingness to pay more taxes to support the welfare state irrespective of the empirical approach used. Our instrumental variable analysis shows that causality run from institutional trust to welfare state support. A one-unit increase in institutional trust leads to a 15 percentage point increase in the willingness to pay more taxes to help the needy. Similarly, a one-unit increase in institutional trust leads to a 16 percentage point increase in the willingness to pay more taxes to support public health care and education. Consequently, institutional trust should be viewed as one of the most important mechanisms that protect the welfare state from dismantling and retrenchment. We also found a stronger effect of support for more universal programs such as public health care and education than for helping the needy.

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)0.005
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.039
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0130.000

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 head0.021
GPT teacher head0.309
Teacher spread0.288 · 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 designObservational
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

Citations48
Published2018
Admission routes1
Has abstractyes

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