Turnover and Career Outcomes of Female and Male Scientists and Engineers
*Names in bold indicate Presenter
According to the social role theory (Eagly, 1987), women have the primary responsibility for caring for the family. Compared to men, women’s career trajectories are more strongly affected by marital and family circumstances (Han and Moen, 1999). Compared to women, men do not need to take so many family responsibilities. Considering voluntary turnover of female and male scientists and engineers, I propose: Female scientists and engineers are more likely to have voluntary turnover due to family-related reasons, while male scientists and engineers are more likely to have voluntary turnover due to career-related reasons. Moreover, compared to men, women gain less benefits from voluntary turnover, mainly because of limited scope of job search and employer’s bias. Women’s family-related turnovers might lead to lower returns towards career outcomes. Women might need to spend more time and energy in taking care of family members, especially children. Previous studies have shown that having children is associated with lower income for women (Budig and England 2001; Waldfogel 1998). This kind of family burden can cause career interruptions for women (Budig and England 2001; Williams 2000). After women change jobs, the new employer might have bias towards them. If employers perceive women as less dedicated to their careers because of real or presumed family responsibilities, their past mobility is more likely to be interpreted as evidence of lack of commitment rather than career-oriented job shopping.
To test the above theory and hypotheses, the paper adopts the coming from the National Survey of College Graduates (NSCG), which has been conducted since the 1970s by the U.S. National Science Foundation. Since the sampling frame of the NSCG has changed since 2010, and due to data access limitation, this study only adopts 2010, 2013 and 2015 surveys for panel data analysis. The paper will mainly use pooled OLS and the fixed effect model to run the analysis. Preliminary analysis has shown to support the hypotheses.