Economic Consequences of Kinship: Evidence From U.S. Bans on Cousin Marriage
Bibliographic record
Abstract
Abstract Close-kin marriage, by sustaining tightly knit family structures, may impede development. We find support for this hypothesis using U.S. state bans on cousin marriage. Our measure of cousin marriage comes from the excess frequency of same-surname marriages, a method borrowed from population genetics that we apply to millions of marriage records from the eighteenth to the twentieth century. Using census data, we first show that married cousins are more rural and have lower-paying occupations. We then turn to an event study analysis to understand how cousin marriage bans affected outcomes for treated birth cohorts. We find that these bans led individuals from families with high rates of cousin marriage to migrate off farms and into urban areas. They also gradually shift to higher-paying occupations. We observe increased dispersion, with individuals from these families living in a wider range of locations and adopting more diverse occupations. Our findings suggest that these changes were driven by the social and cultural effects of dispersed family ties rather than genetics. Notably, the bans also caused more people to live in institutional settings for the elderly, infirm, or destitute, suggesting weaker support from kin.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".