Women's emancipation through education: A macroeconomic analysis
Bibliographic record
Abstract
In this paper, we study the role of education as insurance against a bad marriage.Historically, due to disparities in earning power and education across genders, married women often found themselves in an economically vulnerable position, and had to suffer one of two fates in a bad marriage: either they get divorced (assuming it is available) and struggle as low-income single mothers, or they remain trapped in the marriage.In both cases, education can provide a route to emancipation for women.To investigate this idea, we build and estimate an equilibrium search model with education, marriage/divorce/remarriage, and household labor supply decisions.A key feature of the model is that women bear a larger share of the divorce burden, mainly because they are more closely tied to their children relative to men.Our focus on education is motivated by the fact that divorce laws typically allow spouses to keep the future returns from their human capital upon divorce (unlike their physical assets), making education a good insurance against divorce risk.However, as women further their education, the earnings gap between spouses shrinks, leading to more unstable marriages and, in turn, further increasing demand for education.The framework generates powerful amplification mechanisms, which lead to a large rise in divorce rates and a decline in marriage rates (similar to those observed in the US data) from relatively modest exogenous driving forces.Further, in the model, women overtake men in college attainment during the 1990s, a feature of the data that has proved challenging to explain.Our counterfactual experiments indicate that the divorce law reform of the 1970s played an important role in all of these trends, explaining more than onequarter of college attainment rate of women post-1970s and one-half of the rise in labor supply for married women.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".