Economic rationales for kin marriage: Assessing the evidence using Egyptian panel data
Bibliographic record
Abstract
BACKGROUNDAlthough kin marriage is widely practiced in the Middle East, its underlying motivations have not been thoroughly tested. METHODSWe assess evidence for two economic rationales motivating kin unions using a national sample of Egyptians who wed between the 2006 and 2012 waves of the Egypt Labor Market Panel Surveys.The first rationale for kin marriage involves consolidating family property through the marriage of relatives; the second involves avoiding the costly outlays made when nonrelatives wed. RESULTSWe find that respondents whose natal households owned agricultural land had higher relative risks of marrying a first cousin, and this relationship is significant for the overall sample and for men.Additionally, we find a positive association between value of natal household agricultural enterprise and marriage to a relative for men only.These findings provide limited evidence supporting the first rationale.For the second rationale, we find that women who wed relatives, reported lower bride's side matrimonial expenditures and lower deferred dower values.However, women who wed first cousins reported higher prompt dower values, indicating mixed support for the second rationale. CONTRIBUTIONThis study uses nationally representative longitudinal data with proper temporal ordering of key variables to statistically test two motivations for kin marriage.This analysis is carried out for Egypt, the most populous country of the world region containing some of the highest rates of kin marriage.Our results call into question two
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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 | 0.028 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".