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Record W2987820361 · doi:10.1002/jae.2785

The evolution of the US family income–schooling relationship and educational selectivity

2020· article· en· W2987820361 on OpenAlex
Christian Belzil

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Applied Econometrics · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational and Educational Inequality Studies
Canadian institutionsConcordia UniversityCenter for Interuniversity Research and Analysis on Organizations
FundersSocial Sciences and Humanities Research Council of CanadaAgence Nationale de la Recherche
KeywordsDifferential (mechanical device)Demographic economicsEconomicsDifferential effectsAffect (linguistics)Family incomeNational Longitudinal SurveysCognitionEconometricsPsychologyDemographySociologyEconomic growthMedicine

Abstract

fetched live from OpenAlex

Summary We estimate a dynamic model of schooling on two cohorts of the National Longitudinal Survey of Youth and find that, contrary to conventional wisdom, the effects of real (as opposed to relative) family income on education have practically vanished between the early 1980s and the early 2000s. After conditioning on a cognitive ability measure (AFQT), family background variables and unobserved heterogeneity (allowed to be correlated with observed characteristics), income effects vary substantially with age and have lost between 30% and 80% of their importance on age‐specific grade progression probabilities. After conditioning on observed and unobserved characteristics, a $300,000 differential in family income generated more than 2 years of education in the early 1980s, but only 1 year in the early 2000s. Put differently, a $70,000 differential raised college participation by 10 percentage points in the early 1980s. In the early 2000s, a $330,000 income differential had the same impact. The effects of AFQT scores have lost about 50% of their magnitude but did not vanish. Over the same period, the relative importance of unobserved heterogeneity has expanded significantly, thereby pointing toward the emergence of a new form of educational selectivity reserving an increasing role to noncognitive abilities and/or preferences and a lesser role to cognitive ability and family income.

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.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.649
Threshold uncertainty score0.597

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.068
GPT teacher head0.302
Teacher spread0.235 · 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