Bibliographic record
Abstract
We study the role of wealth in the marriage contract by developing a model of the household where investments in public goods can be made at the cost of future earnings.If couples cannot commit ex ante to a sufficiently equal post-divorce allocation, specialization and public good creation will be sub-optimal.However, accumulating joint assets, which the marriage contract specifies are to be divided in the case of divorce, can reduce this problem by offering insurance to the lower earning partner.Our model demonstrates that access to this "collateralized" version of the contract will lead to more household specialization, more public goods, and a higher value of marriage.To test the model's predictions, we use homeownership as a proxy for access to joint savings technology, since homes are particularly likely to be divided in a way that favors the lower earning partner.We use idiosyncratic variation in housing prices at the time of marriage and an instrumental variables strategy to show that quasi-exogenous variation in homeownership access leads to greater specialization.We then show that as policies made marriage and nonmarital fertility more similar in other ways, wealth has become a more important determinant of who marries.Our model and empirical results suggest wealthy individuals can access a more advantageous marriage contract, which has important policy implications.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 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".