Estimating a marriage matching model with spillover effects
Bibliographic record
Abstract
We use marriage matching functions to study how marital patterns change when population supplies change. Specifically, we use a behavioral marriage matching function with spillover effects to rationalize marriage and cohabitation behavior in contemporary Canada. The model can estimate a couple's systematic gains to marriage and cohabitation relative to remaining single. These gains are invariant to changes in population supplies. Instead, changes in population supplies redistribute these gains between a couple. Although the model is behavioral, it is nonparametric. It can fit any observed cross-sectional marriage matching distribution. We use the estimated model to quantify the impacts of gender differences in mortality rates and the baby boom on observed marital behavior in Canada. The higher mortality rate of men makes men scarcer than women. We show that the scarceness of men modestly reduced the welfare of women and increased the welfare of men in the marriage market. On the other hand, the baby boom increased older men's net gains to entering the marriage market and lowered middle-aged women's net gains.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".