Abstract
This study examined marital 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, cross-sectional sample of financial advisors recruited via a professional continuing education website (n = 459), 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 and a penalty observed among men, a parental leave penalty 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 was 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 (15.0%), marriage (40.2%), parenthood (7.4%), parental leave (11.6%), and spousal employment (41.0%).
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Notes
Calculation based on median weekly earnings of $1905 for men and $1232 for women as reported in Bureau of Labor Statistics (n.d.). This calculation does not account for differences in experience and other human capital characteristics.
Consistent with previous studies among financial advisor populations (e.g., see Tharp et al., 2019), we do not include outliers with reported income greater than $1.5 million in this study. The justification for this decision is that financial advisors making more than $1.5 million are unlikely to be operating solely as financial advisors and likely are generating income from atypical sources, therefore introducing unobserved heterogeneity if these outliers are included.
The one exception was a within the parental leave pay gap analysis when PECs were not included. In that instance, instead of 25.9% of the log gap being explained, − 82.3% of the log gap was explained. Results are available from the authors upon request.
As illustrated by O'Donnell et al. (2007), explaining more than 100% of the gap is possible. This would suggest favorable outcomes for the lower-earning group after accounting for other factors.
A negative gap is consistent with even more favorable outcomes for the higher-earning group after accounting for other factors.
49.7% of the gap could be explained if PECs were excluded.
25.9% of the gap could be explained if PECs were excluded.
-9.7% of the gap could be explained if PECs were excluded.
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Appendices
Appendix 1
Predictors of Revenue Production
Results from eight OLS regressions predicting the natural log of revenue are reported in “Appendix 2”. Full results for demographic variables are not reported in the following section (see “Appendix 2”), but, generally speaking, gender differences were observed in demographic predictors of revenue among both men and women. Among both men and women, education was not a significant predictor of revenue in any model. Experience was positively associated with revenue among both men and women in the full sample. However, once the sample was restricted to only married individuals, experience was generally only associated with revenue among men. Hours worked per week was positively associated with income among men but not women.
Full Sample (Marriage Premium)
Among the full sample (NMen = 430 and NWomen = 78), being married (compared to single) was positively associated with revenue among both men (p < .05) and women (p < .1). Neither having children nor taking time out of the workforce to care for children was associated with revenue production among men or women.
Married Individuals (Fatherhood Premium)
Among only married individuals (NMen = 390 and NWomen = 62), neither having children nor time taken out of the workforce to care for children was associated with revenue production among men or women.
Married Individuals with Children (Parental Leave Effect)
Among married individuals with children (NMen = 351 and NWomen = 51), time taken out of the workforce to care for children was not associated with revenue production among men or women.
Married Individuals with Children (Stay-at-Home Spouse Premium)
Among married individuals with children for which spousal employment information was available (NMen = 340 and NWomen = 49), neither time taken out of the workforce to care for children nor spousal work status was a significant predictor of revenue among men or women.
Appendix 2 Ordinary least squares regressions predicting natural log of revenue produced
Variable | Full sample | Married individuals | Married individuals with children | Married individuals with children | |||||
|---|---|---|---|---|---|---|---|---|---|
Coefficient/Sig (Std. error) | Coefficient/Sig (Std. error) | Coefficient/Sig (Std. error) | Coefficient/Sig (Std. error) | ||||||
Men | Women | Men | Women | Men | Women | Men | Women | ||
Intercept | 6.509*** | 4.010* | 8.427*** | 9.203*** | 8.299*** | 12.285 | 8.259*** | 12.186*** | |
(0.903) | (1.969) | (0.870) | (1.985) | (0.832) | (2.201) | (0.855) | (2.367) | ||
Education (ref. bachelor’s degree) | |||||||||
Less than bachelors | − 0.102 | 0.212 | − 0.317 | 0.132 | − 0.348 | 0.082 | − 0.385 | 0.294 | |
(0.837) | (1.923) | (0.871) | (2.136) | (0.890) | (2.573) | (0.889) | (2.402) | ||
Graduate degree | − 0.173 | 0.595 | − 0.216 | 0.184 | − 0.285 | 0.559 | − 0.265 | 0.964 | |
(0.325) | (0.865) | (0.332) | (0.940) | (0.362) | (1.003) | (0.363) | (0.962) | ||
Years of experience (ref. 0 years) | |||||||||
0 < Exp. < 5 | 1.499** | 3.462** | 1.087* | 1.528 | 0.704 | 0.645 | 0.616 | 1.293 | |
(0.506) | (1.166) | (0.525) | (1.334) | (0.604) | (1.517) | (0.606) | (1.469) | ||
5 ≤ Exp. < 10 | 2.200*** | 3.208* | 2.019*** | 2.171 | 1.686** | 0.872 | 1.565** | 0.089 | |
(0.521) | (1.469) | (0.535) | (1.615) | (0.590) | (1.882) | (0.597) | (1.909) | ||
10 ≤ Exp. < 20 | 2.752*** | 4.151*** | 2.601*** | 2.601* | 2.268*** | 1.995 | 2.242*** | 2.688† | |
(0.468) | (1.178) | (0.483) | (1.284) | (0.524) | (1.412) | (0.525) | (1.368) | ||
Exp. ≥ 20 | 2.507*** | 3.313* | 2.241*** | 1.960 | 1.955*** | 1.134 | 1.900*** | 2.194 | |
(0.504) | (1.397) | (0.517) | (1.447) | (0.554) | (1.554) | (0.554) | (1.522) | ||
Hours worked per week | 0.038** | − 0.012 | 0.043** | − 0.013 | 0.045** | − 0.045 | 0.045** | − 0.045 | |
(0.014) | (0.030) | (0.014) | (0.032) | (0.016) | (0.040) | (0.016) | (0.038) | ||
Family characteristics | |||||||||
Marital status (ref. single) | |||||||||
Married or domestic partner | 1.839* | 3.369† | – | – | – | – | – | – | |
(0.776) | (1.765) | ||||||||
Other | 1.772 | 3.266 | – | – | – | – | – | – | |
(1.128) | (2.009) | ||||||||
Children status | − 0.236 | 1.542 | − 0.301 | 1.248 | – | – | – | – | |
(0.492) | (1.150) | (0.499) | (1.244) | ||||||
Took time out of workforce to care for children | − 0.718 | − 0.383 | − 0.710 | − 0.667 | − 0.680 | − 0.746 | − 0.629 | − 0.664 | |
(0.496) | (0.960) | (0.496) | (1.001) | (0.506) | (0.999) | (0.508) | (0.932) | ||
Spousal work status (ref. stay-at-home) | |||||||||
Spouse works part time | – | – | – | – | – | – | 0.734 | 1.481 | |
(0.489) | (1.800) | ||||||||
Spouse works full time | – | – | – | – | – | – | − 0.124 | − 1.010 | |
(0.394) | (1.374) | ||||||||
N | 430 | 78 | 390 | 62 | 341 | 51 | 340 | 49 | |
Adjusted R2 | 0.106 | 0.218 | 0.081 | − 0.049 | 0.066 | − 0.071 | 0.070 | 0.019 | |
F (d.f.) | 5.63 (11)*** | 2.95 (11)** | 4.81 (9)*** | 0.69 (9) | 3.99 (8)*** | 0.58 (8) | 3.55 (10)*** | 1.09 (10) | |
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Tharp, D.T., Parks-Stamm, E.J., Lurtz, M. et al. Exploring Gender Differences in Marital and Parental Income Premiums Among Financial Advisors. J Fam Econ Iss 43, 15–35 (2022). https://doi.org/10.1007/s10834-021-09766-4
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DOI: https://doi.org/10.1007/s10834-021-09766-4
Keywords
- Marriage premium
- Fatherhood premium
- Parental leave effect
- Stay-at-home spouse premium
- Gender differences
- Gender pay gap