MétaCan
Menu
Back to cohort
Record W4405920878 · doi:10.1257/app.20230403

Careers and Intergenerational Income Mobility

2024· article· en· W4405920878 on OpenAlexaff
Catherine Haeck, Jean‐William Laliberté

Bibliographic record

VenueAmerican Economic Journal Applied Economics · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational and Educational Inequality Studies
Canadian institutionsUniversity of CalgaryUniversité du Québec à Montréal
Fundersnot available
KeywordsMicrodata (statistics)Demographic economicsEconomicsPersistence (discontinuity)Social mobilityIncome distributionLabour economicsOccupational mobilitySocioeconomic statusCensusPopulationDemographySociologyInequality

Abstract

fetched live from OpenAlex

This paper uses census microdata linked with tax records to quantify the contribution of occupations to intergenerational income mobility. We document substantial segregation into occupations by parental income. Children of high-income parents are significantly more likely to pursue high-paying and more desirable occupations. Since parents may pass on their occupations to their children, we further describe patterns of intergenerational occupational following and show they vary substantially across occupations, with low-income occupations showing more persistence across generations on average. Yet, occupational persistence plays a limited role for income mobility, explaining only 10 percent of the income rank-rank relationship. (JEL J13, J16, J31, J62)

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 score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.019
GPT teacher head0.306
Teacher spread0.286 · 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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Economic Journal Applied EconomicsSame topicIntergenerational and Educational Inequality StudiesFrench-language works237,207