Replication data for: Estimating Average and Local Average Treatment Effects of Education when Compulsory Schooling Laws Really Matter
Bibliographic record
Abstract
The change to the minimum school-leaving age in the United Kingdom from 14 to 15 had a powerful and immediate effect that redirected almost half the population of 14-year-olds in the mid-twentieth century to stay in school for one more year. The magnitude of this impact provides a rare opportunity to (a) estimate local average treatment effects (LATE) of high school that come close to population average treatment effects (ATE); and (b) estimate returns to education using a regression discontinuity design instead of previous estimates that rely on difference-in-differences methodology or relatively weak instruments. Comparing LATE estimates for the United States and Canada, where very few students were affected by compulsory school laws, to the United Kingdom estimates provides a test as to whether instrumental variables (IV) returns to schooling often exceed ordinary least squares (OLS) because gains are high only for small and peculiar groups among the more general population. I find, instead, that the benefits from compulsory schooling are very large whether these laws have an impact on a majority or minority of those exposed.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.087 | 0.060 |
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".