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Record W299474516

Unequal Inequality: The Distribution of Individuals' Earnings by Province *

2001· article· en· W299474516 on OpenAlexvenueaboutno aff
Ross Finnie

Bibliographic record

VenueCanadian Journal of Regional Science · 2001
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional Economic and Spatial Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsEarningsInequalityDemographic economicsDistribution (mathematics)Economic inequalityGovernment (linguistics)EconomicsIncome distributionInvestment (military)PoliticsPolitical science
DOInot available

Abstract

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Abstracts: Unequal Inequality: The Distribution of Individuals' Earnings by Province. This paper presents the results of an empirical analysis of earnings at the provincial level based on the recently available Longitudinal Administrative Database. The paper addresses the following questions: Are there significant differences in earnings by province? What are the patterns of any such differences, such as along regional lines or with respect to income levels? Were there any important shifts in the level of at the national level or in the patterns by province over the 1982-94 period covered by the analysis? Do the provincial patterns change when is measured using earnings averaged over several years--that is, taking earnings mobility into account--or when other income measures are employed, such as when self-employment, professional, or investment income, or government transfers, are included, or when taxes are deducted? Entrenched in the Canadian political-economi c-social psyche is the notion of and provinces, but the results reported here should allow us to now also speak in terms of inequality and less inequality provinces. Policy implications are discussed. ********** Thanks to a substantial accumulation of research, we now know a good deal about earnings in Canada in terms of its overall level, the sub-group patterns (e.g., by age, sex and level of education), and the shifts which have occurred over time.' There is, however, one area in which our understanding is virtually nil: earnings at the provincial level. (2) The contribution of this paper is to report the results of an empirical analysis of earnings at the provincial level over the 1982-1994 period based on the recently available Longitudinal Administrative Database. The paper addresses the following questions: * Were there significant differences in earnings by province over this period? If so, were there any clear tendencies along regional lines or any other provincial characteristics, such as industrial base or income levels (i.e., the richer versus poorer provinces)? * Were the patterns consistent across age-sex groups? For example, in provinces where there was greater among men, were women's earnings also more unequally distributed? Related to this, were the provincial differences at the aggregate level due to common patterns across all age-sex groups or due to composition (i.e., different proportions of lower/higher age-sex groups across the provinces)? * Were there any significant shifts in earnings at the national level or in the provincial patterns over the period covered by the analysis? In particular, was there convergence (with reductions in in the high provinces and increases in the low provinces), increased divergence, or were the changes more idiosyncratic than to allow for such broad characterisations? * Do the patterns change when is measured using earnings averaged over several years at the individual level--that is, taking earnings mobility into account? * What are the patterns when other income measures are considered, such as when other sources of market income and transfers are included, or when taxes are subtracted out? The results presented here thus provide an empirical view of labour market outcomes at the provincial level which should add to our general understanding of work and pay in Canada and also provide evidence on the effects of adding other income sources and taxes to the distribution of final incomes in each province. The paper thus furnishes new information on the regional nature of labour markets in Canada, a topic addressed at the international level in OECD (2000). Furthermore, this comes in a context where it is not obvious what the expected patterns would be. …

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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)0.010
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 score0.576
Threshold uncertainty score0.843

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.006
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.031
GPT teacher head0.213
Teacher spread0.181 · 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

Citations5
Published2001
Admission routes2
Has abstractyes

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