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Income, personality, and subjective financial well-being: the role of gender in their genetic and environmental relationships.

Zyphur MJ, Li WD, Zhang Z, Arvey RD, Barsky AP - Front Psychol (2015)

Bottom Line: Using a twins sample from the Survey of Midlife Development in the U.This relationship was due to 'unshared environmental' factors rather than genes, suggesting that the effect of income on SFWB is driven by unique experiences among men.Further, for women and men, we found that CSE influenced income and SFWB, and that both genetic and environmental factors explained this relationship.

View Article: PubMed Central - PubMed

Affiliation: Department of Management and Marketing, The University of Melbourne Melbourne, VIC, Australia.

ABSTRACT
Increasing levels of financial inequality prompt questions about the relationship between income and well-being. Using a twins sample from the Survey of Midlife Development in the U. S. and controlling for personality as core self-evaluations (CSE), we found that men, but not women, had higher subjective financial well-being (SFWB) when they had higher incomes. This relationship was due to 'unshared environmental' factors rather than genes, suggesting that the effect of income on SFWB is driven by unique experiences among men. Further, for women and men, we found that CSE influenced income and SFWB, and that both genetic and environmental factors explained this relationship. Given the relatively small and male-specific relationship between income and SFWB, and the determination of both income and SFWB by personality, we propose that policy makers focus on malleable factors beyond merely income in order to increase SFWB, including financial education and building self-regulatory capacity.

No MeSH data available.


Related in: MedlinePlus

Results of multivariate analysis for males and females. Unstandardized path coefficients with their standard errors (in parentheses) are reported. For simplicity purposes, control variables are not shown. CSE and SFWB are measured as latent variables with multiple indicators. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001.
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Figure 2: Results of multivariate analysis for males and females. Unstandardized path coefficients with their standard errors (in parentheses) are reported. For simplicity purposes, control variables are not shown. CSE and SFWB are measured as latent variables with multiple indicators. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001.

Mentions: Including all control variables as predictors, we first estimated a model for females and a model for males separately, before testing a combined model for both gender groups. Table 4 shows the fit of the separate and combined models. For males, the AE model for the three study variables adequately fit the data (Model 1 in Table 4). Results indicate that two paths, x21 (the effect of A1 on income) and x32 (the influence of A2 on SFWB), were not significant. Accordingly, as recommended in Plomin et al. (2013), we constrained these two paths to zero (Model 2, Table 4), which did not cause meaningful decrements in fit compared to Model 1. For females, the model with both A and E factors for all of the three study variables also showed adequate fit (Model 3, Table 4) and four paths were not significant: x21 (the effect of A1 on income), x32 (the influence of A2 on SFWB), z21 (the effect of E1 on income), and z32 (the impact of E2 on SFWB). Therefore, the four paths were constrained to zero in a nested model (Model 4, Table 4), which also did not cause meaningful decrements in fit compared to Model 3. Consequently, the best multivariate models were obtained for both males (Model 2) and females (Model 4). We then combined the two best fitting models into a single model (Model 5). Results show that the combined model did not achieve exceptional fit, but the fit was adequate to examine the effects contained in the combined model. We tested our hypotheses based on the path coefficients in this combined model, as presented in Figure 2.


Income, personality, and subjective financial well-being: the role of gender in their genetic and environmental relationships.

Zyphur MJ, Li WD, Zhang Z, Arvey RD, Barsky AP - Front Psychol (2015)

Results of multivariate analysis for males and females. Unstandardized path coefficients with their standard errors (in parentheses) are reported. For simplicity purposes, control variables are not shown. CSE and SFWB are measured as latent variables with multiple indicators. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4587091&req=5

Figure 2: Results of multivariate analysis for males and females. Unstandardized path coefficients with their standard errors (in parentheses) are reported. For simplicity purposes, control variables are not shown. CSE and SFWB are measured as latent variables with multiple indicators. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001.
Mentions: Including all control variables as predictors, we first estimated a model for females and a model for males separately, before testing a combined model for both gender groups. Table 4 shows the fit of the separate and combined models. For males, the AE model for the three study variables adequately fit the data (Model 1 in Table 4). Results indicate that two paths, x21 (the effect of A1 on income) and x32 (the influence of A2 on SFWB), were not significant. Accordingly, as recommended in Plomin et al. (2013), we constrained these two paths to zero (Model 2, Table 4), which did not cause meaningful decrements in fit compared to Model 1. For females, the model with both A and E factors for all of the three study variables also showed adequate fit (Model 3, Table 4) and four paths were not significant: x21 (the effect of A1 on income), x32 (the influence of A2 on SFWB), z21 (the effect of E1 on income), and z32 (the impact of E2 on SFWB). Therefore, the four paths were constrained to zero in a nested model (Model 4, Table 4), which also did not cause meaningful decrements in fit compared to Model 3. Consequently, the best multivariate models were obtained for both males (Model 2) and females (Model 4). We then combined the two best fitting models into a single model (Model 5). Results show that the combined model did not achieve exceptional fit, but the fit was adequate to examine the effects contained in the combined model. We tested our hypotheses based on the path coefficients in this combined model, as presented in Figure 2.

Bottom Line: Using a twins sample from the Survey of Midlife Development in the U.This relationship was due to 'unshared environmental' factors rather than genes, suggesting that the effect of income on SFWB is driven by unique experiences among men.Further, for women and men, we found that CSE influenced income and SFWB, and that both genetic and environmental factors explained this relationship.

View Article: PubMed Central - PubMed

Affiliation: Department of Management and Marketing, The University of Melbourne Melbourne, VIC, Australia.

ABSTRACT
Increasing levels of financial inequality prompt questions about the relationship between income and well-being. Using a twins sample from the Survey of Midlife Development in the U. S. and controlling for personality as core self-evaluations (CSE), we found that men, but not women, had higher subjective financial well-being (SFWB) when they had higher incomes. This relationship was due to 'unshared environmental' factors rather than genes, suggesting that the effect of income on SFWB is driven by unique experiences among men. Further, for women and men, we found that CSE influenced income and SFWB, and that both genetic and environmental factors explained this relationship. Given the relatively small and male-specific relationship between income and SFWB, and the determination of both income and SFWB by personality, we propose that policy makers focus on malleable factors beyond merely income in order to increase SFWB, including financial education and building self-regulatory capacity.

No MeSH data available.


Related in: MedlinePlus