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Record W3122334868

Economic Mobility, Family Background, and the Well-Being of Children in the United States and Canada

2010· preprint· en· W3122334868 on OpenAlexaboutno aff
Miles Corak, Lori Curtis, Shelley Phipps

Bibliographic record

VenueRePEc: Research Papers in Economics · 2010
Typepreprint
Languageen
FieldSocial Sciences
TopicIntergenerational and Educational Inequality Studies
Canadian institutionsnot available
FundersUniversity of Wisconsin-MadisonRheinische Friedrich-Wilhelms-Universität BonnSage Foundation
KeywordsDisadvantagedEarningsSocial mobilityDemographic economicsInvestment (military)Distribution (mathematics)Economic mobilityPolitical scienceEconomic freedomPublic policyEconomicsEconomic growthPovertyMarket economy
DOInot available

Abstract

fetched live from OpenAlex

This comparative study of the relationship between family economic background and adult outcomes in the United States and Canada addresses three questions. First, is there something to explain? We suggest that the existing literature finds that there are significant differences in the degree of intergenerational economic mobility between these two countries, relative mobility being lower in the United States. This is the result of lower mobility at the very top and the very bottom of the earnings distribution. Second, does this reflect different underlying values of the citizens in these countries? Findings from comparable public opinion polls suggest that this is not the case. The citizens of both countries have a similar understanding of a successful life, one that is rooted in individual aspirations and freedom. They also have similar views on how these goals should be attained, but with one important exception: Americans differ in that they are more likely to see the State hindering rather than helping the attainment of these goals. Finally, how do the investments these countries make in the future of their children through the family, the labour market, and public policy actually differ? Using a number of representative household surveys we find that the configuration of all three sources of investment and support for children differs significantly, disadvantaged American children living in much more challenging circumstances, and the role of public policy not as strong in determining outcomes.

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)0.005
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.245
Threshold uncertainty score0.478

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.041
GPT teacher head0.347
Teacher spread0.306 · 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 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

Citations4
Published2010
Admission routes1
Has abstractyes

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Same venueRePEc: Research Papers in EconomicsSame topicIntergenerational and Educational Inequality StudiesFrench-language works237,207