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Trading dollars for flextime? Gender differences in earnings trajectories by and the role of compensating differentials

Catherine Doren University of Wisconsin-Madison

Katherine Y. Lin University of Wisconsin-Madison

Abstract (150 words): Despite progress toward earnings equality, gender gaps in earnings trajectories remain marked and expand across the life course. Much of the research tracing this widening gap and explaining its drivers focuses on highly educated men and women. In this paper, we use NLSY79 to ask whether these same patterns are observed across the education spectrum. First, we document gender gaps in earning trajectories across levels of education using individual growth curve models. We find that trajectories of men and women with less than a college degree do not diverge and instead run more parallel. Second, we test the compensating differentials hypothesis, which has been used to explain the widening gap among the most educated. While it may explain gender differences in earnings trajectories among men and women with a college degree, our findings suggest that other explanations are necessary to understand differences in earnings trajectories of the less educated.

Introduction Despite rapid changes in the labor market and in the family, in 2013, women working full-time, year round, still only earned 78 cents for each dollar men received for the same amount of work (Council of Economic Advisers 2015). A wide body of scholarship has attempted to explain why such inequality persists (Blau and Kahn 2016). We make two substantive contributions to this scholarly literature: First, we document gender gaps in earning trajectories, across levels of education. Persistent gender inequalities are the result of age, period, and cohort influences on the difference between men and women’s labor market experiences. Prior research has shown that younger cohorts experience smaller gender gaps compared to older cohorts, but that intra-cohort gender inequality widens across age (Campbell and Pearlman 2013; Goldin 2014; Manning and Swaffield 2008). This makes the examination of earnings trajectories within cohorts crucial to understanding why gender inequality persists. We examine these differences across educational attainment. Much of the existing work on gender gaps in earnings trajectories focuses on the highly-educated, often restricting samples to men and women with professional degrees or from elite undergraduate institutions (e.g., Bertrand, Goldin, and Katz 2010; Goldin and Katz 2008; Goldin 2014). This truncates our knowledge on the extent to which gender gaps in earnings trajectories widen over the life course in the general population. We also have reason to believe that gender gaps in earnings trajectories will differ by education. College-educated women are more likely than those with less education to see more benefits to paid labor (Autor 2014; Blau and Kahn 2016; Goldin 2004), as well as delay fertility until after establishing a (Isen and Stevenson 2010; Martin 2002; Matthews and Hamilton 2009; McLanahan 2004). College-educated men, compared to those with less education, are more likely to be employed in professional occupations that are subject to norms of overwork which can boost men’s relative to women’s (Cha and

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Weeden 2014). Low educated men have also faced a decline in prospects and returns to education (Autor 2014). Thus, it is possible that observed patterns for the highly educated are not consistent with that of less educated men and women. Our second contribution is to document the extent to which certain workplace benefits can explain widening intra-cohort inequality between men and women, across educational levels. A key explanation for the persistence of earnings gender gaps has been the idea that women may trade higher pay for workplace benefits such as flexible scheduling and other amenities that are conducive to balancing family and workplace responsibilities. This idea is referred to as the compensating differentials hypothesis (Felfe 2012; Glauber 2012; Goldin 2014; Lowen and Sicilian 2009; McCrate 2005). Support for this hypothesis has been mixed, with some work demonstrating that models including measures of workplace benefits does little to alter the magnitude of the gender earnings gap (Lowen and Sicilian 2009), and other work demonstrating that women tend to select into occupations that are less likely to offer such benefits (Glauber 2012; McCrate 2005). Given that some of the stronger evidence in favor of this hypothesis has been drawn from samples of advantaged men and women (e.g. Goldin 2014), it may be that this group either sees greater availability or more gendered effects of compensating differentials than less advantaged men and women. Less educated men and women may work in occupations where these tradeoffs or forms of flexibility are not as feasible (Amuedo-Dorantes and Kimmel 2005). It may be that preferences still differ by gender, but that family-friendly schedules or environments are simply unavailable for women with less education to choose. Beyond this, it may be that the gendered effects of the availability of these benefits differ by education. Thus, we contribute to the debate on the mechanisms underpinning gender inequality by examining how various workplace benefits shape gender gaps in earnings trajectories. Finally, prior work documenting divergences between men and women in earnings across age have largely utilized a synthetic cohort approach (Blau and Kahn 2000; Campbell and Pearlman 2013; Manning and Swaffield 2008), which cannot observe to what extent such observed divergences are the result of cohort or age differences. As noted above, some research has examined earnings trajectories of real cohorts of men and women, but has limited this focus to those with a college education (Goldin 2014). Some recent studies have analyzed trajectories of labor market and family experiences among a single cohort of women (Damaske and Frech 2016; Garcia-Manglano 2015), providing useful information about the variation in trajectories among women. However, single-sex studies cannot directly address gender differences, as they do not compare between men and women. We thus contribute to existing research on the labor market experiences of men and women by analyzing a nationally representative longitudinal cohort study of men and women, providing novel evidence on the life course earnings trajectories of a single cohort of men and women across educational categories.

Data, variables, and methods We use the National Longitudinal Study of Youth 1979 cohort (NLSY) and individual growth curve models to estimate men’s and women’s earnings trajectories across age. The NLSY 1979 cohort is uniquely suited to addressing our research questions as earnings information across the bulk of adulthood was ascertained from respondents. Men and women were aged 14 to 22 at their first interview in 1979 and 47 to 55 at their most recent interview in 2012. NLSY conducted interviews every year from 1979 through 1994 and every other year since, so the data on our respondents’ and earnings histories are consistent and comprehensive across their lives as working adults. We do not include data collected on the military and supplemental

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** For submission to PAA 2017. Please do not quote or cite without the author’s permission. ** samples because follow-ups for these groups ended in 1985 and 1991, respectively. This remaining sample of 6,111 men and women was designed to be representative of the noninstitutionalized United States population ages 14-22 in 1979. Our analytic sample consists of those who contributed at least one wave of earnings information and provide information on all covariates of interest (N=62,772 observations from 5,797 respondents). For the PAA meetings we will explore how missing data impacts our results, and multiply impute missing data. We restrict our analysis to men and women aged 22 to 47 because the oldest respondents in the cohort were 22 at the time of their first interview and the youngest were 47 at the time of the most recent wave. Because respondents were different ages in different years, we structure the data to be organized by age as opposed to survey year. This makes it possible for us to compare respondents when they are at the same age, regardless of the survey wave. To model men and women’s earnings trajectories we estimate individual growth curve models of annual earnings for men and women. To assess differences by education in the gender gap in earnings trajectories, we allow the intercepts and slopes of the growth models to vary by gender, education, and an interaction between gender and education. After modeling the gender gap in earnings trajectories across education level, we add time-varying measures of the availability of specific workplace benefits into the model. We then compare gender-by-education coefficients from models with and without occupational characteristics to assess the impact of compensating differentials in explaining gender-by-education differences in trajectories. Our main outcome of interest is a time-varying measure of annual earnings. This is measured by the respondent’s income from wages and in the past year. We account for inflation using the CPI inflation adjustment to put all earnings in 2012 dollars. Education is assessed at age 25 based on self-reports of years of completed schooling. We divide respondents into three groups. Reports of 12 or fewer years are grouped as high school or less, reports of more than 12 but fewer than 16 years are grouped as some college, and reports of 16 or more years are grouped as college or more. For the PAA meetings, we will test the sensitivity of our results to differences in education cut-offs. To test the compensating differentials hypothesis we incorporate time-varying measures of a respondent’s job, specifically the availability of health , , , , and flexible work hours. These measures were collected from 1989 through the most recent wave in 2012. For these analyses we include all men and women who contribute information on our measures of interest. This means that we include men and women who are not in the labor force at a particular wave of observation, recording their earnings as zero. This is an important substantive decision as we are interested in the earnings gap across the life course between men and women. We know that men and women respond differently to important life course transitions, such as parenthood and marriage, and this differentially impacts labor market behavior, including entering and exiting the labor force. Excluding these transitions from the analyses would provide a truncated view of the gender earnings trajectory gap. To account for differences in family status, we include a time-varying indicator for parenthood and marital status. We also include a time-varying indicator for when a respondent was not in the labor force. For the PAA meetings we will more fully explore the role that parenthood and marital status play in the gender earnings trajectory gap by education, including stratifying our sample by marriage and parenthood status.

Preliminary Results

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Table 1 describes the proportion of person-waves a respondent had access to specific workplace benefits, separated by gender and by education. There is a clear education gradient in the availability of workplace benefits. For example, while the proportion of person-years a respondent had access to flexible scheduling was under a third for those with a high school degree or less, nearly 40% of women, and more than half of men with at least a bachelors degree had access to these benefits. was the amenity that the majority of respondents had access to ranging from 42% of person-years for women with high school degree or less to 73% for men with at least a bachelor’s degree. Across education, men are more likely to have access to these workplace benefits, with one notable exception: in all three education categories, women are more likely to have access to parental leave compared to their male counterparts. Finally, women are more likely than men to not be in the labor force. The difference is the most pronounced among those with a high school degree or less, with a gap of 15%. For those with at least a bachelor’s degree, the gap is only 9%. Figure 1 presents predicted annual earnings from an individual growth curve model of annual earnings that allows slopes and intercepts to vary by gender, education, and a gender-by- education interaction term, and controls for time-varying measures of marital status and parental status. We subtracted 22 from the age variable to allow for a meaningful constant. From this figure we can see that there are clear educational differences in the gender gap in earnings trajectories. While across education categories women earn less than men, we can see the divergence in earnings trajectories is the widest for those with a college degree or more. In fact, the gap remains fairly stable for those with a high school degree or less. In the models, the coefficient for the interaction between gender, education, and age is significant at the p<0.001 level. Table 2 compares model estimates with and without the inclusion of measures of various workplace benefits. The first column presents coefficients from the model that generated Figure 1. The second column displays coefficients from a model that includes measures of workplace benefits. From this table we can see that the inclusion of the compensating differentials variables attenuates some, but not all, of the gender-by-education coefficient. We see that the gender-by- education coefficient for those with some college moves from B=-993.89 to B=-793.18, while the coefficient for college degree or above changes from B=-3087.28 to B=-2777.88. The decrease in BIC from model 1 to model 2 implies that the second model better explains variation in the data. Interestingly, the coefficients on the benefits measures themselves are positive and significant, demonstrating that while there is some evidence of a compensating differentials hypothesis, it is the case that many that offer benefits are also more high-paying. This speaks to the idea that compensating differentials, as an explanation of persistent gender inequality in the labor market, may only apply to well-compensated jobs, often only accessible by those with a college degree. As expected, being a parent is associated with a drop in earnings, while being married is associated with an increase in earnings.

Preliminary Conclusions and Next Steps Overall, these findings draw attention to the importance of examining the gender gap in earnings while noting not only its size at the aggregate level, but how it develops across the life course. Unlike past work, this paper highlights the necessity of identifying and understanding how these trends in men and women’s trajectories vary by level of educational attainment. The distinct trends that emerge for men and women with and without college degrees suggest that the dynamics of the gender gap in earnings across the life course are more complex than may be evident at first glance. Moreover, these findings draw attention to how different mechanisms

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** For submission to PAA 2017. Please do not quote or cite without the author’s permission. ** underpinning the gender gap in earnings can vary by educational attainment. Our preliminary evidence suggests mixed support for the compensating differentials hypothesis. While adding such measures to a model of annual earnings does serve to attenuate some of the gender-by- education gap, the level of attenuation is minimal. Moreover, the availability of benefits is positively associated with higher pay. This suggests that if compensating differentials play any role in the gender gap, it does so only among the highly educated, who have access to such benefits. We may need to look elsewhere for the mechanisms underlying the gap at lower levels of education. These preliminary findings provide a foundation for the analyses to be presented at the PAA meetings. In future analyses, we will also include an interaction of the compensating differentials variables with gender and education. This will allow us to directly test whether men and women’s pay is differentially affected by the availability of these benefits. If women see larger tradeoffs associated with these benefits, it may be that they play a larger role in driving apart men and women’s earnings in high-paying jobs than we observe in our current models.

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Works Cited Amuedo-Dorantes, Catalina and Jean Kimmel. 2005. “The Motherhood Gap for Women in the United States: The Importance of College and Fertility Delay.” Review of Economics of the Household 3(1):17–48. Autor, David H. 2014. “Skills, Education, and the Rise of Earnings Inequality among The ‘other 99 Percent.’” Science 344(6186):843–51. Bertrand, Marianne, Claudia Goldin, and Lawrence F. Katz. 2010. “Dynamic of the Gender Gap for Young Professionals in the Corporate and Financial Sectors.” American Economic Journal: Applied Economics 2(3):228–55. Retrieved (http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:No+Title#0). Blau, Francine D. and Lawrence M. Kahn. 2000. “Gender Differences in Pay.” Journal of Economic Perspectives 14(4):75–100. Blau, Francine D. and Lawrence M. Kahn. 2016. The Gender Wage Gap: Extent, Trends, and Explanations. Campbell, Colin and Jessica Pearlman. 2013. “Period Effects, Cohort Effects, and the Narrowing Gender Wage Gap.” Social Science Research 42(6):1693–1711. Retrieved (http://dx.doi.org/10.1016/j.ssresearch.2013.07.014). Cha, Y. and K. a. Weeden. 2014. “Overwork and the Slow Convergence in the Gender Gap in Wages.” American Sociological Review 79(3):457–84. Council of Economic Advisers. 2015. : Recent Trends and Explanations. Retrieved (https://www.whitehouse.gov/sites/default/files/docs/equal_pay_issue_brief_final.pdf). Damaske, Sarah and Adrianne Frech. 2016. “Women’s Work Pathways Across the Life Course.” Demography 53(2):365–91. Retrieved (http://link.springer.com/10.1007/s13524-016-0464- z). Felfe, Christina. 2012. “The Motherhood Wage Gap: What about Job Amenities?” 19(1):59–67. Retrieved (http://dx.doi.org/10.1016/j.labeco.2011.06.016). Garcia-Manglano, Javier. 2015. “Opting Out and Leaning In: The Life Course Employment Profiles of Early Baby Boom Women in the United States.” Demography 52(6):1961–93. Glauber, R. 2012. “Women’s Work and Working Conditions: Are Mothers Compensated for Lost Wages?” Work and Occupations 39(2):115–38. Goldin, C. 2004. “The Long Road to the Fast Track: Career and Family.” The ANNALS of the American Academy of Political and Social Science 596(1):20–35. Goldin, C. and L. F. Katz. 2008. “Transitions: Career and Family Life Cycles of the Educational Elite.” American Economic Review 98(2):363–369. Retrieved (http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:gender+differences+in+ ,+education,+and+games#0).

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Goldin, Claudia. 2014. “A Grand Gender Convergence: Its Last Chapter.” American Economic Review 104(4):1091–1119. Retrieved (http://pubs.aeaweb.org/doi/abs/10.1257/aer.104.4.1091). Isen, Adam and Betsey Stevenson. 2010. “Women’s Education and Family Behavior: Trends in Marriage, Divorce and Fertility.” NBER Working Paper Series. Retrieved (http://www.nber.org/papers/w15725.pdf). Lowen, Aaron and Paul Sicilian. 2009. “‘Family-Friendly’ Fringe Benefits and the Gender Wage Gap.” Journal of Labor Research 30(2):101–19. Manning, Alan and Joanna Swaffield. 2008. “The Gender Gap in Early-Career Wage Growth.” The Economic Journal 118(530):983–1024. Martin, S. 2002. “Delayed Marriage and Childbearing: Implications and Measurement of Diverging Trends in Family Timing.” Social Inequality (October). Retrieved (http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Delayed+Marriage+and +Childbearing:+Implications+and+Measurement+of+Diverging+Trends+in+Family+Timin g#0). Matthews, T. J. and Brady E. Hamilton. 2009. “Delayed Childbearing: More Women Are Having Their First Child Later in Life.” NCHS data brief (21):1–8. McCrate, Elaine. 2005. “Flexible Hours, Workplace Authority, and Compensating Wage Differentials in the US.” Feminist Economics 11(1):11–39. McLanahan, Sara. 2004. “Diverging Destinies: How Children Are Faring Under the Second Demographic Transition.” Demography 41(4):607–27. Retrieved (http://link.springer.com/10.1353/dem.2004.0033).

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Table 1. Proportion of person-years respondent had access to workplace benefits, by education and gender Women Men HS or less at 25 Health insurance 42% 54% Life insurance 36% 45% Parental leave 41% 37% Sick leave 35% 37% Flextime 32% 31% Not asked (not working enough) 42% 27% N 19,420 17,168 Some college at 25 Health insurance 54% 62% Life insurance 49% 53% Parental leave 54% 46% Sick leave 50% 52% Flextime 40% 40% Not asked (not working enough) 33% 25% N 8,507 6,588 College or more at 25 Health insurance 59% 73% Life insurance 54% 66% Parental leave 58% 53% Sick leave 57% 66% Flextime 37% 53% Not asked (not working enough) 31% 20% N 5,906 5,183

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Table 2. Individual growth curve estimates for annual earnings, coefficients and standard errors

Model 1 Model 2

Intercept 17777.21 *** 19104.64 *** (753.912) (725.993)

Female -13400.00 *** -10200.00 *** (920.07) (799.777) Education HS or Less (ref) 0.00 0.00 . . Some College -2395.13 + -2266.87 * (1260.739) (1094.148) BA+ -6151.46 *** -7354.13 *** (1377.205) (1196.255) FemaleXEducation Female X HS or Less 0.00 0.00 . . Female X Some College 8788.81 *** 5626.31 *** (1707.158) (1478.407) Female X BA+ 18906.08 *** 15820.20 *** (1900.513) (1645.772)

Age 2160.91 *** 1021.68 *** (89.878) (88.409) Female X Age -307.03 *** -429.77 *** (91.661) (87.125) Education X Age HS or Less X Age 0.00 0.00 . . Some College X Age 1296.69 *** 1178.08 *** (125.116) (118.826) BA+ X Age 3918.98 *** 3741.54 *** (136.85) (129.968) Female X Education X Age Female X HS or less X Age 0.00 0.00 . . Female X Some College X Age -993.89 *** -793.18 *** (170.242) (161.732) Female X BA+ X Age -3087.28 *** -2777.88 *** (189.938) (180.46)

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Age2 -44.05 *** -8.38 *** (2.217) (2.248)

Time varying indicators Parent -3696.33 *** -2286.20 *** (407.111) (374.351) Married 2086.28 *** 1910.04 *** (288.6) (273.823) Not in the labor force -5993.88 *** (337.303) Time varying indicators of workplace benefits Health insurance 5393.51 *** (415.761) Life insurance 3700.48 *** (363.474) Parental Leave 1546.45 *** (303.428) Sick Leave 3142.28 *** (309.505) FlexTime 319.90 (240.371) N 62772 62772 BIC 1443687 1440000 ***p<.001, **p<.01, *p<.05, +p<.1

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Figure 1.

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