Exploring Gender Differences in Marriage and Parental Income Premiums among Financial Advisors
Bibliographic record
Abstract
This study examined marriage and parental income premiums among financial advisors. Financial advisors provide an interesting context for exploring such premiums, as financial advising is a historically male-dominated profession that has been found to exhibit large unadjusted gender pay gaps. Using a large sample of financial advisors recruited via a professional continuing education website (n=555), this study investigates whether gender differences exist among financial advisors with respect to the marriage premium, the parenthood premium, the parental leave effect, and the stay-at-home spouse premium. This study examined premiums both with and without potentially endogenous human capital covariates. Without including potentially endogenous covariates, a marriage premium was observed among men but not women, a parenthood premium was observed among women but not men, a parental leave premium was observed among neither men nor women, and a stay-at-home spouse premium was observed among men but not women. When potentially endogenous covariates were included, a marriage penalty was observed among women but not men, a parenthood premium was observed among women while a parenthood penalty observed among men, a parental leave premium was observed among men but not women, and a stay-at-home spouse premium was observed among men while a stay-at-home spouse penalty was observed among women. Exploratory Blinder-Oaxaca decomposition analyses revealed sizeable unadjusted income gaps by gender (16.7%), marriage (32.8%), parenthood (8.1%), parental leave (16.7%), and spousal employment (39.8%).
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".