Equilibrium effects of abortion restrictions on cohort fertility: Why restricting abortion access can reduce human capital, social welfare, and lifetime fertility rates
Bibliographic record
Abstract
The United States Supreme Court’s ruling in Dobbs v. Jackson Women’s Health Organization has made understanding the impact of abortion laws increasingly important and timely. We investigate recent claims by policymakers that abortion restrictions increase birth rates; we also evaluate consequences for human capital and women’s welfare. We motivate our theoretical contribution by presenting some simple empirical analysis of cross-country associations. These provide no evidence of a significant association between abortion legality and birth rates. Our main contribution is an applied economic theory model. Contrary to some policy claims, but in line with stylized empirical facts, abortion bans can lower equilibrium fertility: An abortion ban might cause women to have more unintended births at young ages, but this could reduce their accumulation of capabilities that would prepare them to have a larger family later. We solve a 2-period version of the model, and simulate it and a 3-period version. If women with more resources can afford to choose more children (because of costs of having, raising, and educating children), then the sign of the effect on lifetime fertility depends on whether the increase in fertility due directly to unintended births is outweighed by the effect on subsequent fertility choices. But either way, abortion restrictions are likely to reduce human capital and harm women’s welfare.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".