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Record W3124366431 · doi:10.20381/ruor-25522

En quelle année vaut-il mieux être né? Les revenus des hommes et des femmes au Canada pendant un quart de siècle

2001· preprint· fr· W3124366431 on OpenAlexaboutno aff
Gilles Grenier

Bibliographic record

VenueuO Research (University of Ottawa) · 2001
Typepreprint
Languagefr
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Productivity
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

Il existe une perception qu’il y a des inégalités économiques entre les générations et plus précisément que la situation économique des jeunes travailleurs d’aujourd’hui est moins bonne que celle de leurs ainés. Dans ce texte, on cherche à tester cette hypothèse pour les hommes et les femmes au Canada. En combinant des micro-données des recensements canadiens de 1971, 1981, 1986, 1991 et 1996, on estime des régressions de gains qui isolent les effets de l’année de naissance et de l’âge. Les valeurs monétaires sont converties avec l’indice des prix à la consommation (IPC). Dans la spécification de base, il n’y a pas d’autres variables explicatives. Pour les hommes, on obtient le résultat que la génération la plus “chanceuse” est celle née en 1944 et que les moins fortunés sont ceux nés récemment. Pour les femmes, la génération ayant les gains les plus élevés est celle née en 1960 et les générations récentes gagnent beaucoup plus que les plus anciennes. L’inclusion de variables explicatives standards ne change pas la forme de la relation entre les gains et l’année de naissance, mais la génération qui a les gains les plus élevés est un peu plus ancienne et les écarts entre générations sont plus petits. Les résultats sont sensibles à l’utilisation de l’IPC pour convertir les revenus. Si on suppose, comme certains le pensent, que l’IPC a systématiquement surestimé les augmentations de prix dans le passé, la situation économique des jeunes générations est meilleure que ce qu’on aurait estimé autrement. Les résultats confirment en partie certaines idées courantes sur le bien-être relatif des générations, mais ils montrent aussi la difficulté de comparer les niveaux vie dans le temps. / In Which Year Is It Better to Be Born? The Earnings of Men and Women in Canada During a Quarter of a Century. There exists a perception of economic inequalities among generations and more precisely that today’s younger generations are facing worse economic conditions than those who preceded them. In this paper, this hypothesis is tested for men and women in Canada. By pooling micro-data from the Canadian censuses of 1971, 1981, 1986, 1991 and 1996, earnings regressions are estimated to isolate the effects of birth year and age. Monetary values are converted using the Consumer Price Index (CPI). In the base specification, there are no other explanatory variables. For men, it is found that the “luckiest” generation is the one born in 1944 and that the least fortunate are those born recently. For women, the generation with the highest earnings is the one born in 1960 and the recent generations earn a lot more than the earlier ones. The inclusion of standard explanatory variable does not change the shape of the relationship between earnings and birth year, but the generation with the highest earnings is a bit older and the differences among generations are smaller. The results are sensitive to the use of the CPI to convert incomes. If it is assumed, as claimed by some, that the CPI systematically overestimated price increases in the past, the economic position of the younger generations is better than estimated otherwise. The results partly confirm some current ideas about the relative well-being of generations, but they also show the difficulty of comparing living standards over time.

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 score0.036
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0050.002
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.083
GPT teacher head0.271
Teacher spread0.188 · 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

Citations0
Published2001
Admission routes1
Has abstractyes

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