School Entry, Educational Attainment and Quarter of Birth: A Cautionary Tale of LATE
Bibliographic record
Abstract
Previous studies of the e¤ect of school entry age on educational attainment may be severely biased because they violate the monotonicity assumption needed for LATE. We propose an instrument not subject to this bias and show no e¤ect on children born in the fourth quarter of moving from a December 31 to an earlier cuto¤. To determine whether the IV estimates diverge because of bias or because they estimate di¤erent LATEsm we estimate a structural model of optimal entry age that reconciles the di¤erent IV estimates. Our estimates imply that one standard instrument is badly biased but that the other diverges from ours because it estimates a di¤erent LATE. We also …nd that an early entry age cuto ¤ that is applied loosely (as in the 1950s) is bene…cial but one that is strictly enforced is not. We are grateful to Josh Angrist, Jim Heckman, Claudia Olivetti, Daniele Paserman and participants in seminars at Boston University and the University of Chicago for helpful comments and suggestions. The usual caveat applies. Barua acknowledges funding under NSF-AERA grant xxx. Lang acknowledges
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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 | 0.041 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".