School Entry, Educational Attainment and Quarter of Birth: A Cautionary Tale of LATE
Bibliographic record
Abstract
Partly in response to increased testing and accountability, states and districts have been raising the minimum school entry age, but existing studies show mixed results regarding the effects of entry age. These studies 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 effect on the educational attainment of children born in the fourth quarter of moving from a December 31 to an earlier cutoff. We then estimate a structural model of optimal entry age that reconciles the different IV estimates including ours. We find that one standard instrument is badly biased but that the other diverges from ours because it estimates a different LATE. We also find that an early entry age cutoff that is applied loosely (as in the 1950s) is beneficial but one that is strictly enforced is not.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".