Marriage, cohabitation, and institutional context: Household specialization among same‐sex and different‐sex couples
Bibliographic record
Abstract
Abstract Objective This study examines how marriage‐cohabitation gaps in household specialization (labor supply and earnings) vary across institutional contexts for same‐sex couples (SSCs) and different‐sex couples (DSCs) in Canada. Background Prior research suggests that marriage‐cohabitation gaps are smaller in contexts where cohabitation is more prevalent, but it has overlooked how legal protections (at the contextual level) and gender composition (at the couple level) moderate this association. As a result, little is known about whether differences in household specialization stem from heightened gendered expectations attached to marriage or stronger legal protections for married couples. This study posits that marriage‐cohabitation gaps will be larger in contexts where legal protections for cohabitors are less marriage‐like. Methods Using the 2006 and 2016 Canadian Census and the 2011 National Household Survey, I estimate ordinal and fractional logit models to examine marriage‐cohabitation gaps in specialization among all couples ( N = 2,788,055) and couples with young children ( N = 826,305). Results Among DSCs, marriage‐cohabitation gaps were larger in Québec than in English Canada vis‐à‐vis earnings but not labor supply. Patterns among SSCs were more heterogeneous: gaps in labor supply were larger in English Canada for female couples but larger in Québec for male couples. Gaps in earnings were generally larger in Québec, with few exceptions. However, DSCs consistently specialized more than SSCs. Conclusion While existing research suggests marriage‐cohabitation gaps in household specialization are largely explained by the prevalence of cohabitation, my results indicate that legal protections (at the contextual level) and gender composition (at the couple level) play a more decisive role.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| 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".