Estimating the Effects of Family Background on the Return to Schooling
Why this work is in the frame
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Bibliographic record
Abstract
This article examines the causal link between family background characteristics—parental education and family size—and returns to schooling. I implement a model of schooling and earnings with heterogeneous returns to education using data from the Occupational Change in a Generation Survey. I find that men raised in larger families have substantially lower returns to education, whereas the combined effects of parental education are more modest. In addition, like other “supply-side” instrumental variables studies of the causal effect of education, I find two-stage least squares estimates that are larger than the corresponding ordinary least squares estimates. The results suggest an alternative explanation for this phenomenon: constant marginal return to schooling, combined with a negative absolute ability bias and a positive comparative advantage bias.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 it