The Evolution of the US Family Income-Schooling Relationship and Educational Selectivity
Bibliographic record
Abstract
We estimate a dynamic model of schooling on two cohorts of the NLSY and find that, contrary to conventional wisdom, the effects of family income on education have practically vanished between the early 1980?s and the early 2000?s. After conditioning on a cognitive ability measure (AFQT), family background variables and unobserved heterogeneity (allowed to be correlated with observed characteristics), income effects have lost between 30% and 80% of their importance on age-specific grade progression probabilities. A $70,000 family income differential raised college participation by 10 percentage points in the early 1980?s. In the early 2000?s, a $330,000 income differential had the same impact. The effects of AFQT scores also decreased substantially but did not vanish. Over the same period, the relative importance of unobserved heterogeneity has expanded so much that it has become by far the most important determinant of education.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".