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Record W3149067451 · doi:10.1093/0199271410.003.0009

Increased Income Inequality in OECD Countries and the Redistributive Impact of the Government Budget

2004· book-chapter· en· W3149067451 on OpenAlexaboutno aff
Anthony B. Atkinson

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsUnemploymentEconomic inequalityGovernment (linguistics)InequalityDistribution (mathematics)Income distributionGovernment budgetDemographic economicsDevelopment economicsLabour economicsMacroeconomicsPublic finance

Abstract

fetched live from OpenAlex

Abstract The recent rise in inequality in the distribution of disposable income in many, although not all, countries has led to a search for explanations, particularly since for much of the postwar period falling income inequality has been the norm. In the OECD countries, on which this chapter concentrates, the cause has been identified as rising wage dispersion, coupled with persistent unemployment in Europe. However, a number of factors need to be brought into any explanation of the extent and timing of changes in income distribution, including movements in factor shares, changes in real interest rates, and the impact of the government budget. This chapter focusses on the last of these. It has five sections: Introduction; Redistributive Impact of the Government Budget in selected OECD countries—a review of the statistical evidence from five OECD countries where a time series of studies covering the 1980s and the 1990s is available (UK, Canada, West Germany, Finland, Sweden; The Government Budget in Principle and Policy Reaction to Demographic Shifts—a simple framework within which the distributional implications of different government policy responses to changes in economic conditions and the different elements influencing the choice of response are explored; Policy Changes in Redistributive Taxes and Transfers: Case Studies of Unemployment Benefit and Personal Taxation—in the five European countries already studied, and in the US; and Summary of Conclusions.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.912
Threshold uncertainty score0.896

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.017
GPT teacher head0.215
Teacher spread0.198 · 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

Citations21
Published2004
Admission routes1
Has abstractyes

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