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Life Satisfaction of Career Women and Housewives

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Abstract

Profound changes in gender roles have taken place over the past several decades in the United States. Women’s roles have changed most: women are marrying later in life and at lower rates, having fewer children, and working more outside of the household. “Career women” are the new normal and housewifery has gone out of fashion. At the same time, women have become less happy. We use the US General Social Surveys from 1972 to 2014 to explore these latest trends. We find that, until recently, women were happier to be housewives or to work part-time than full-time, especially, women who are older, married, with children, in middle or upper class, and living in suburbs or smaller places. The effect size of housewifery on subjective wellbeing (SWB) is mild to moderate, at about a fourth to a third of the effect of being unemployed. Therefore, we argue that one possible reason for the decline in average happiness for women was increased labor force participation. Yet, the happiness advantage of housewifery is declining among younger cohorts and career women may become happier than housewives in the future.

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Notes

  1. Traditionally, economists treat labor as disutility or unhappiness at the time of labor (e.g., Bryson and MacKerron 2016), but overall, they argue that the more income or consumption, the more utility or happiness, at least with diminishing returns (Okulicz-Kozaryn 2012).

  2. Of course, many feminist advocates have also argued for wage equity and against other conditions that make employment difficult for women (sexual harassment, discrimination, occupational segregation, etc).

  3. There are many studies on marital satisfaction and some on job satisfaction, but in this brief review we only limit ourselves to those that analyze overall happiness or SWB, not domain-specific satisfactions. Further, we limit ourselves to studies that specifically address the dichotomy of labor force participation and housewifery. For instance, Della Giusta et al. (2011) is omitted because it only considers household work in conjunction with labor force participation.

  4. Panel data analysis would be a useful addition to cross-sectional results. Such data containing all necessary variables for replicating the present analysis are now being collected for the US by the Panel Study of Income Dynamics (PSID). The limitation of PSID is, however, that it covers a much shorter span–it only started SWB questions a few years ago.

  5. Having children may affect self-reported health. For robustness, we have rerun our models without the variable children and the results were substantially the same; and we have also checked the variance inflation factor which was about 1.2 for these variables in the full models. Additional robustness checks are also provided by subsetting the sample by the variable children in Table 3.

  6. Note that the sample sizes in a3 and a4 are substantially different–the health variable is missing for many cases. We have rerun model a3 for the sample used in model a4 (results are not shown) and the coefficient on housewifery was .04 only–the doubling of the coefficient from a3 to a4 is not due to the sample used, but to the inclusion of the health variable. There is also a potential issue of endogeneity with respect to the health variable. There is some disagreement about whether health predicts happiness or happiness predicts health (Diener 2015). Recent research seems to indicate that health causes happiness (Liu et al. 2016), and we treat it this way here, and also postpone health to later stages in our model elaboration.

  7. Model 4a uses the same sample as model 4, but without dummies and the health variable to examine whether the addition would reduce significance in model 4. The results are still insignificant in 4a even without these controls, hence we conclude that it is rather due to a smaller sample. Model 4b uses the sample from model 2 and the results are significant, and of the same magnitude even when controlling for work status and region dummies. In short, the addition of the occupation dummies removes statistical significance due to missing data and due to a reduced sample size in model 4, but not due to their attenuating effect, because even without these controls in model 4A and using the same sample, the results are still insignificant.

  8. Of course, women are often discriminated against and are paid less than a man for the very same job. Discrimination can arise as a result of discriminatory biases of employer (Becker 2010). A study by Wood et al. (1993) analyzed lawyers who graduated from the University of Michigan, and even after controlling for hours worked, and a list of worker qualifications and other covariates, including family status, race, location, grades while in law school, and detailed work history data, such as years practiced law, months of part-time work, and type and size of employer, they found that male lawyers earned 13 percent more. Even in academia, a MIT report found differential treatment of female professors, with women receiving less despite having equal professional accomplishments as their male colleagues (MIT 1999). Many other studies, analyzing men and women in the same profession have found significant gender pay gap by occupation. See for example, (Hegewisch et al. 2012; Corbett and Hill 2012). Women are also less likely to negotiate salary and succeed in negotiations (e.g., Zarya 2016, Babcock and Laschever 2007, Bowles 2016).

  9. The GSS started to ask questions on sexual orientation only in their last four survey in 2008, 2010, 2012 and 2014.

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Appendix

Appendix

Figure 4 shows the variable distributions. If a variable has more than 10 categories, it is classified into 5 bins.

Fig. 4
figure 4

Variables’ distribution

Table 5 shows cross-tabulation of marital status and work status for females.

Table 5 Cross-tabulation of marital status and work status for females

Marriage

Marital status is critical to this study–more and more women postpone or drop marriage altogether. Marriage is also closely correlated (over past several decades) with labor force participation–see Fig. 5–and there is clearly a tradeoff for women when considering to have a family and/or a career (Williams 2001).

Fig. 5
figure 5

Marital status and labor force participation of women, ages 25-54 (Cohen 2013)

Figure 6 shows interesting patterns. Being a housewife, provided one has a husband/spouse, is the best a woman can do for her happiness. Category ’other’ has a slightly bigger happiness advantage, but standard errors are much bigger, and it is unclear who is included in this generic category.

Fig. 6
figure 6

Predicted values from specification mar6 in Table 6

There is a very interesting drop for married women who are unemployed, quite counter-intuitive because the dip is much larger for married women than unmarried, and one would expect unemployment to have a more dramatic effect on happiness of women who are unmarried. One explanation could be relative deprivation (relative to husband/spouse), or perhaps lost of face or honor. Exploring further this result is beyond the scope of this study, but is an interesting area for future research (Table 6).

Table 6 OLS of women SWB on work status categories interacted with marital status

Results Using Hours for both Sexes as Opposed to Work Status Categories for Women Only

This section presents results using hours worked for both sexes as opposed to work status categories for women only. Only persons who work > 0 hours are included, i.e. only persons working part-time or full-time.

As shown in the first panel in Fig. 7, men are happier to work longer hours and women are happier to work shorter hours, controlling for other predictors of SWB. The second panel in Fig. 7 shows a more nuanced relationship: interestingly, women are equally happy to work either 40 or < 16 hours per week and less happy to work in other brackets (Tables 7 and 8).

Fig. 7
figure 7

Predicted SWB with 95% confidence intervals. Estimates are based on full models (#6) from Tables 7 and 8

Table 7 OLS of SWB on interaction of working hours with gender
Table 8 OLS of SWB on interaction of working hours categories with gender

Booth and Van Ours (2008, 2009) also find that women are happier working part time. Rätzel (2009) found that overemployment is worse for women and underemploymet is worse for men.

Results Showing Interactions with Year, Age, and Cohort

Here only women who are either working full-time, part-time, or housewives are retained. Table 9 shows the interactions with year: part-timers and housewives are happier than full-timers, but housewives are becoming less happy over time. Results become insignificant in yr4 when controlling for health.

Table 9 OLS of female SWB on interaction of year and collapsed 3-step work status categories

In Table 10 the part-time coefficient and interaction with cohort loose significance in the more elaborated specifications, but housewifery remains strongly significant.

Table 10 OLS of female SWB on interaction of cohort and collapsed 3-step work status categories

Finally, in Table 11 the interaction of part-time dummy with age is weakly significant or mostly insignificant, whereas the interaction of housewife dummy with age is insignificant in base model (age0) but gains significance in more elaborate models.

Table 11 OLS of female SWB on interaction of age and collapsed 3-step work status categories

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Okulicz-Kozaryn, A., da Rocha Valente, R. Life Satisfaction of Career Women and Housewives. Applied Research Quality Life 13, 603–632 (2018). https://doi.org/10.1007/s11482-017-9547-2

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