The Impact of Education on Family Formation: Quasi-Experimental Evidence from the UK
Bibliographic record
Abstract
In this paper we examine the fertility and mating market effects of education. We exploit a quasi-experiment generated by a change in UK compulsory schooling laws. This change, introduced in 1972, forced all students to stay in full-time education until at least age 16. The reform was recent enough that access to legal abortion and modern contraception was quite similar to today, granting insight into the fertility effects of education in a modern context. This reform was binding for many girls, inducing around one quarter of the female population of England and Wales in the relevant cohorts to attend an additional year of school. For identification, we leverage the fact that compulsory school requirement was discontinuous with respect to cohort of birth using regression discontinuity methods. We show that the affected girls had significantly lower fertility in their teen years. Instrumental variables estimates imply a 30 % reduction in births at ages 16 and 17 caused by the additional year of schooling. The decline was not accompanied by any increase in abortions. We also find that the reform had negligible impacts on completed fertility. Our findings suggest that education-based policies might reduce teen pregnancies without impacting completed fertility rates. On the mating market front, the reform induced both men and women to marry more educated
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".