Bibliographic record
Abstract
Power couples, defined as pairs of highly educated partners, tend to cluster in cities to take advantage of more developed labor markets, better jobs, and higher wages. This research examines to what extent being a partner in a power couple brings additional wage income benefits. We examine what the effects of power couple partnering is on wage income. Furthermore, we examine how the results are affected by gender and place of residence. To determine this, the research uses detailed Swedish micro data on power couples 23–39 years of age over the period 2007–2016. Our analysis finds positive and significant results from being in a power couple on wage income after controlling for individual, workplace, and geographical characteristics. This is the case for both men and women in power couple households without children, but for women only when children are present. For power couples in denser urban areas, we find a positive effect for men in power couples with or without children. We suggest this effect is due to a more equal “balance of power” between partners in highly educated power couples located in bigger cities, where norms and values may favor a relatively greater sharing of household duties between men and women, and where men face a different competitive situation in the labor market.
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 | 0.000 | 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) | 0.001 | 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".