1. WHY ARE WOMEN’S AND MEN’S WORK LIVES CONVERGING? DEMOGRAPHY, HUMAN CAPITAL INVESTMENTS, AND LIFETIME EARNINGS Joyce Jacobsen (Wesleyan University) Melanie Khamis (Wesleyan University and IZA) Mutlu Yuksel (Dalhousie University and IZA) 24th IAFFE Annual Conference, 16-18July 2015, Berlin

2. Motivation Changes in women’s and men’s work lives since the mid 20 th century in the U.S.: increasing female labor force participation, narrowing of the gender wage gap, decreasing male labor force participation. Workforce experiences have increasingly diverged for different levels of human capital. Inequality within gender and within the labor force research has not focused on economy-wide patterns and lifetime dimension of labor force participation decisions, human capital investments and lifetime earnings.

3. Over time studies have found: 1. Underlying differences in human capital between men and women have decreased and so has the portion of the gender wage gap that accounts for the differences, for the period 1970 to 2010 (Goldin 2014). 2. Convergence in education and experience; 1976 to 1990 gender wage gap declined (O’Neill and Polachek 1993). 3. Convergence of earnings for 70s to 90s; slowing convergence in 1990s in the gender pay gap cannot be explained by changes in human capital (Blau and Kahn 2000, 2006). 4. Over time women’s selection into labor force participation changed from negative selection in the 1970s to positive selection in the 1990s (Mulligan and Rubenstein 2008).

4. Our own research on these trends has found: 1. Selection bias is increasing over time for women  for real hourly earnings change from negative to positive selection bias for women (consistent with Mulligan and Rubenstein 2008), men negative selection bias 2. Returns to potential experience for men and women converge for both Heckman and OLS results 3. Returns to education diverge for men and women in the Heckman selection corrected results but converge in the OLS 4. Lifetime labor attachment and earnings patterns: Convergence in lifetime years of work and lifetime hours worked, but less convergence in lifetime earnings

5. This current paper: Investigates observed trends in demographic patterns:  life expectancy and  in birth rates alongside  returns to human capital investments,  in real earnings and expected lifetime earnings for men and women as potential explanations for the increased in convergence in women’s and men’s work lives Using CPS and demographic data from Vital Statistics over 50 years, we estimate long-run relationships between actual life expectancy or actual birth rates, human capital investments and actual and expected labor market outcomes for men and women.

6. Our main contribution: We employ 50 years of individual level data from CPS and Vital Statistics on actual demography Other papers employing the time-series approach to studying gender differences in work patterns have used aggregated annual data series and have not accounted for marginal returns to human capital investment (McNown and Rajbhandry 2003) Other papers have not included life expectancy and only focused on fertility trends, thereby missing an important trend in human demography (Salamaliki et al. 2013; McNown and Rajbhandary 2003; Hondryiannis and Papapetrou 2002)

7. Data US Census Annual Demographic Files (March CPS) for 1964- 2013 Individuals 25-65 years of age; gender, marital status, race, marital status, years of education, educational attainment, urban-rural location and regions Vital Statistics from 1964 to 2013 Actual life expectancy at birth for women and men separately; Birth rate: the number of births per 1,000 people in the population

8. Macro: Time Series Analysis Variables: Demographic variables: actual life expectancy at birth, birth rates Results from the micro-level wage regression (OLS and Heckman selection corrected  we focus here on the Heckman results) generated returns to 15 years of experience and returns to college graduation over time Real hourly earnings Expected lifetime earnings  Separately for female and males

9. Is there a long-run relationship? Descriptive Graphs (visual inspection of trends) and summary statistics Test data series for whether they are stationary or non-stationary Test for co-integration (Johansen 1988, 1991, 1995) of four different models, by gender (OLS and Heckman) If co-integrating relationship is found, vector error correction models are estimated to determine long-run relationship and adjustment process Impulse response functions: Shocks of innovation to our demographic and economic variables and their effect on expected lifetime earnings

11. Demographic Variables Life expectancy, female Life expectancy, male

12. Birth rate, female and male

13. Returns to 15 years experience (Heckman) Female Male

14. Returns to College (Heckman) Female Male

15. Real Hourly earnings FemaleMale

16. Expected lifetime earnings FemaleMale In thousands

17. Summary Statistics

18. Unit Root Tests, female

19. Unit Root Test, male

20. For each gender separately four models are tested for cointegration: Model 1: life expectancy at birth, returns to 15 years of experience, real hourly earnings and expected lifetime earnings. Model 2: birth rate, returns to 15 years of experience, real hourly earnings and expected lifetime earnings. Model 3 : life expectancy at birth, returns to college, real hourly earnings and expected lifetime earnings. Model 4 : the birth rate, returns to college, real hourly earnings and expected lifetime earnings.

21. Selection-order criteria, AIC

22. Johansen test for cointegration For Females: Four models cointegration, For Males: Only Model 3 in Heckman

23. Vector error-correction models

24. Impulse Response Functions, Model 1, female Model 1: life expectancy at birth, returns to 15 years of experience, real hourly earnings and expected lifetime earnings.

25. Impulse Response Functions, Model 2, female Model 2: birth rate, returns to 15 years of experience, real hourly earnings and expected lifetime earnings.

26. Impulse Response Functions, Model 3, female Model 3 : life expectancy at birth, returns to college, real hourly earnings and expected lifetime earnings

27. Impulse Response Functions, Model 4, female Model 4 : the birth rate, returns to college, real hourly earnings and expected lifetime earnings

28. Impulse Response Functions, Model 3, male Model 3 : life expectancy at birth, returns to college, real hourly earnings and expected lifetime earnings

29. Conclusion We estimate long-run relationships between actual life expectancy or actual birth rates, human capital investments and actual and expected labor market outcomes for men and women Findings suggest a long-run relationship for all these variables for women while for men changes in birth rates do not matter The long-run relationships are overall more convincing for women than men:  Shocks to demography, human capital investments and real hourly earnings have permanent effects on women’s lifetime earnings  For men only one type of shock, a change in the return to college, has a long-run impact on lifetime earnings