A Quantitative Review of <i>Marriage Markets: How Inequality is Remaking the American Family</i> by Carbone and Cahn
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Bibliographic record
Abstract
June Carbone and Naomi Cahn argue that growing earnings inequality and the increased educational attainment of women, relative to men, have led to declining marriage rates for less-educated women and an increase in positive assortative matching since the 1970s. These trends have negatively affected the welfare of children, as they increase the proportion of poor, single-female-headed households. Using data on marriage markets defined by state, race and time, and the Choo–Siow marriage matching function, this review provides a quantitative assessment of these claims. We show that changes in earnings inequality had a qualitatively consistent but modest quantitative impact on marriage rates and positive assortative matching. Neither changes in the wage distributions nor educational attainments can explain the large decline in marriage rates over this period. (JEL C78, D63, J12, J15, J16, J31)
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it