Do Dropouts Drop Out Too Soon? Evidence from Changes in School-Leaving Laws
Bibliographic record
Abstract
Abstract: This paper investigates if decisions to leave school early are sub-optimal, and whether would-be-dropouts benefit from policies, such as a minimum school leaving age, that oblige them to continue. I use changes in minimum school-leaving laws in Great Britain and Ireland, which were remarkably influential, to measure pecuniary and nonpecuniary gains from education. I find, similar to previous tudies, students compelled to take an extra year of school experienced an average increase of 12 percent in annual earnings. I also find significant gains from education to health, leisure and labor activities, and subjective measures of well-being, which hold up against a wide array of specification checks. Comparing these estimates with intertemporal models of educational choice, the main conclusion of this paper is that it is very difficult to explain early school leaving decisions without the presence of time inconsistent preferences, misguided expectations, or disutility from identifying with a social group that considers dropping out the norm. To prefer dropping out early, the one-year cost from attending school would likely have to exceed a dropout’s maximum lifetime annual earnings by a factor of at least five.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".