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Record W1568595176

Income Inequality and Redistribution in Canada: 1976 to 2004

2007· preprint· en· W1568595176 on OpenAlexaffabout
Andrew Heisz

Bibliographic record

VenueRePEc: Research Papers in Economics · 2007
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsStatistics Canada
Fundersnot available
KeywordsEconomicsIncome inequality metricsEconomic inequalityIncome distributionInequalityGross incomeRedistribution of income and wealthAdjusted gross incomeRedistribution (election)Labour economicsHousehold incomeTotal personal incomeIncome in kindDemographic economicsIncome taxState income taxTax reformPublic economicsMacroeconomicsGeographyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Using data from the 1976-to-1997 Survey of Consumer Finances and the 1993-to-2004 Survey of Labour and Income Dynamics, we examine developments in family income inequality, income polarization, relative low income, and income redistribution through the tax-transfer system. We conclude that family after-tax-income inequality was stable across the 1980s, but rose during the 1989-to-2004 period. Growth in family after-tax-income inequality can be due to an increase in family market-income inequality (pre-tax, pre-transfer), or to a reduction in income redistribution through the tax-transfer system. We conclude that the increase in inequality was associated with a rise in family market-income inequality. Redistribution was at least as high in 2004 as it was at earlier cyclical peaks, but it failed to keep up with rapid growth in family market-income inequality in the 1990s. We present income inequality, polarization, and low-income statistics for several well-known measures, and use data preparations identical to those used in the Luxembourg Income Study in order to facilitate international comparisons.

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)0.003
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 score0.069
Threshold uncertainty score0.500

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.014
Science and technology studies0.0040.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.044
GPT teacher head0.290
Teacher spread0.246 · 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

Citations34
Published2007
Admission routes2
Has abstractyes

Explore more

Same venueRePEc: Research Papers in EconomicsSame topicHousing, Finance, and NeoliberalismFrench-language works237,207