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Record W2112403419 · doi:10.1177/0956797611420882

Progressive Taxation and the Subjective Well-Being of Nations

2011· article· en· W2112403419 on OpenAlexaff
Shigehiro Oishi, Ulrich Schimmack, Ed Diener

Bibliographic record

VenuePsychological Science · 2011
Typearticle
Languageen
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsUniversity of Toronto
FundersAssociation for Psychological Science
KeywordsLife satisfactionGovernment (linguistics)Economic JusticeSubjective well-beingPsychologyInequalityPublic goodEconomicsEconomic inequalityPublic economicsDemographic economicsSocial psychologyHappinessMicroeconomics

Abstract

fetched live from OpenAlex

Using data from the Gallup World Poll, we examined whether progressive taxation is associated with increased levels of subjective well-being. Consistent with Rawls's theory of justice, our results showed that progressive taxation was positively associated with the subjective well-being of nations. However, the overall tax rate and government spending were not associated with the subjective well-being of nations. Furthermore, controlling for the wealth of nations and income inequality, we found that respondents living in a nation with more-progressive taxation evaluated their lives as closer to the best possible life and reported having more positive and less negative daily experiences than did respondents living in a nation with less-progressive taxation. Finally, we found that the association between more-progressive taxation and higher levels of subjective well-being was mediated by citizens' satisfaction with public goods, such as education and public transportation.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.034
GPT teacher head0.353
Teacher spread0.318 · 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

Citations172
Published2011
Admission routes1
Has abstractyes

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