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 real (as opposed to relative) 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 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 1980's, but only one year in the early 2000's. Put differently, a $70,000 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 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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".