School Starting Age and Cognitive Development
Bibliographic record
Abstract
Abstract We present evidence of a positive relationship between school starting age and children's cognitive development from ages 6 to 18 using a fuzzy regression discontinuity design and large‐scale population‐level birth and school data from the state of Florida. We estimate effects of being old for grade (being born in September vs. August) that are remarkably stable—always around 0.2 SD difference in test scores—across a wide range of heterogeneous groups, based on maternal education, poverty at birth, race/ethnicity, birth weight, gestational age, and school quality. While the September‐August difference in kindergarten readiness is dramatically different by subgroup, by the time students take their first exams, the heterogeneity in estimated effects on test scores effectively disappears. We do, however, find significant heterogeneity in other outcome measures such as disability status and middle and high school course selections. We also document substantial variation in compensatory behaviors targeted towards young‐for‐grade children. While the more affluent families tend to redshirt their children, young‐for‐grade children from less affluent families are more likely to be retained in grades prior to testing. School district practices regarding retention and redshirting are correlated with improved outcomes for the groups less likely to use those remediation approaches (i.e., retention in the case of more affluent families and redshirting in the case of less affluent families.) Finally, we find that very few school policies or practices mitigate the test score advantage of September‐born children.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".