Educational assortative mating in the United States and the effect on income inequality by household from 1960 to 2005
Bibliographic record
Abstract
This study examines the effect assortative mating by education has on income inequality by household. In contrast to the majority of other literature in this field which focus on the United States (U.S.) as a whole, this study makes use of state-level data to examine the marriage mating market with respect to education attainment. It also examines how homogamous partnerships increase income inequality across households by analyzing changes in the Gini coefficient over time. Panel data for this analysis is from the U.S. Bureau of Economic Analysis and the Integrated Public Use Microdata Series (IPUMS-International and IPUMS-USA) from the U.S. Census of the Population. Assortative mating by education is shown in this analysis to be a contributing factor to increasing inequality among homogamous heterosexual partnerships in the U.S. from 1960 to 2005.
 
 Keywords: assortative mating; education level; United States (state-level, from 1960-2005); income inequality (household); labour economics; welfare economics
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".