1. Sociological Aspects of S/E Career Participation Yu Xie University of Michigan & Kimberlee A. Shauman University of California-Davis

2. Presentation Outline  Design of study  Participation in the S/E Education  Participation in the S/E Labor force  Summary of evidence regarding common explanations for women’s underrepresentation

3. Yu Xie University of Michigan & Kimberlee A. Shauman University of California-Davis WOMEN IN SCIENCE: Career Processes and Outcomes

4. Main Features of the Study  We take a life course approach.  We study the entirety of a career trajectory.  We analyzed seventeen large, nationally representative datasets.

5. The Life Course Approach  Interactive effects across multiple levels.  Interactive effects across multiple domains: education, family, and work.  Individual-level variation in career tracks  The cumulative nature of the life course

6. Chapter 4: Gender differences in the attainment of a science/engineering bachelor’s degree Data Source: HSBSo Chapter 5: Beyond the science baccalaureate: gender differences in career paths after degree attainment Data Sources: NES, B&B Chapter 6: Gender differences in career paths after attainment of a master’s degree in S/E Data Source: NES Chapter 7: Demographic and labor force profiles of men and women in science and engineering Data Sources: 1960-1990 Census PUMS, SSE Chapter 8: Geographic mobility of men and women in science and engineering Data Source: 1990 Census PUMS Chapter 9: The research productivity puzzle revisited Data Sources: Carnegie-1969, ACE-1973, NSPF-1988, NSPF-1993 Chapter 10: Immigrant women scientists/ engineers Data Sources: 1990 Census PUMS, SSE Chapter 2: Gender differences in math and science achievement Data Sources: NLS-72, HSBSr, HSBSo, LSAY1, LSAY2, NELS Chapter 3: Gender differences in the expectation of an S/E college major among high school seniors Data Source: NELS High school diploma + 6 years S/E Bachelor’s Degree + 2 years S/E Master’s Degree + 2 years Post-M.S. and Post-Ph.D. Career Years Grades 7 – 12 Synthetic cohort life course, outcomes examined and data sources

7. Participation in S/E Secondary Education  “Critical Filter” Hypothesis – Women are handicapped by deficits in high school mathematics training  Coursework Hypothesis – Girls fail to participate in the math and science college preparatory courses during high school

8. “Critical Filter” Hypothesis  The gender gap in average mathematics achievement is small and has been declining.

9. “Critical Filter” Hypothesis  The gender gap in average mathematics achievement is small and has been declining.  The gender gap in representation among top achievers remains significant.

10. “Critical Filter” Hypothesis  The gender gap in average mathematics achievement is small and has been declining.  The gender gap in representation among top achievers remains significant.  Gender differences in neither average nor high achievement in mathematics explain gender differences in the likelihood of majoring in S/E fields.

11. “Critical Filter” Hypothesis

12. “Coursework Hypothesis”  Girls are as likely as boys to take math and science courses (except for physics).

13. “Coursework Hypothesis”  Girls are as likely as boys to take math and science courses (except for physics).  Girls attain significantly better grades in high school coursework.

14. “Coursework Hypothesis”  Girls are as likely as boys to take math and science courses (except for physics).  Girls attain significantly better grades in high school coursework.  Course participation does not explain gender differences in math and science achievement scores.

15. Participation in S/E Postsecondary Education  Representation of women among bachelors degree recipients has increased in almost all S/E fields

16. Participation in S/E Postsecondary Education  Representation of women among bachelors degree recipients has increased in almost all S/E fields  Participation gaps are greatest at the transition from high school to college: – Women are less likely to expect a S/E major – Attrition from the S/E educational trajectory is greater for women than men at the transition from high school to college

17. Sex-specific probabilities for selected pathways to an S/E baccalaureate

19.  After the transition to college, there are no gender differences in persistence Participation in S/E Postsecondary Education

20. Sex-specific probabilities for selected pathways to an S/E baccalaureate

21.  After the transition to college, there are no gender differences in persistence  Most female S/E baccalaureates had expected to pursue non-S/E majors but shifted to S/E after entering college Participation in S/E Postsecondary Education

22. Post-S/E baccalaureate career paths

23.  Women are more likely than men to “drop out” of education and labor force participation  Among those who do not “drop out” of education and the labor force: – Women and men are equally likely to make the transition to either graduate education or work – But within either trajectory, women are significantly less likely to pursue the S/E path

24. Bachelor’s Degree in S/E Graduate School in Non-S/E No Graduate School, Not Working Working in Non-S/E Graduate School in S/E Working in S/E Graduate Studies Work Post-S/E baccalaureate career paths 2.44***1.060.94 0.41***0.45*** Female-to-Male Odds Ratios of Career Transitions

25. Participation in the S/E labor force  The representation of women in the S/E labor force has increased for all fields, but gaps persist

26. Participation in the S/E labor force  The representation of women in the S/E labor force has increased for all fields, but gaps persist  Women scientists and engineers are less likely to be employed full time. – Percent employed full time, 1990: Women scientists: 90.9 Men scientists: 96.5

27. Achievement in the S/E labor force  Women earn significantly less than men

28. Achievement in the S/E labor force  Women earn significantly less than men  Women are promoted at a significantly lower rate

29. Explanations for gaps in participation and achievement in the S/E labor force  Women are not as geographically mobile as men  Women publish at slower rates  Women’s family roles hamper their career progress

30. Are Women’s Rates of Geographic Mobility Limited?  This may be true because women are more likely than men to be in dual-career families.  However, we find – Scientists in dual-career families do not have lower mobility rates. – There are no overall gender differences across types of families. – Only married women with children have lower mobility rates.

31. Predicted Migration Rate by Gender and Family Structure

32. The “Productivity Puzzle”  Cole and Zuckerman (1984) stated: “women published slightly more than half (57%) as many papers as men.”  Long (1992 ) reaffirms: “none of these explanations has been very successful.”

33. The “Productivity Puzzle”  Sex differences in research productivity declined between 1960s and 1990s. Trend in Female-Male Ratio of Publication Rate

34. The “Productivity Puzzle”  Sex differences in research productivity declined between 1960s and 1990s.  Most of the observed sex differences in research productivity can be attributed to sex differences in background characteristics, employment positions and resources, and marital status.

35. The “Productivity Puzzle” Model description 1969197319881993 (0): Sex0.580***0.632***0.695**0.817 (1): (0) + Field + Time for Ph.D. + Experience 0.630***0.663***0.8000.789* (2):(1)+Institution + Rank +Teaching + Funding + RA 0.9520.9360.7750.931 (3): (2) + Family/Marital Status 0.9970.9710.8010.944 Estimated Female-to-Male Ratio of Publication

36. Does a Family Life Hamper Women Scientists’ Careers?  Marriage per se does not seem to matter much.  Married women are disadvantaged only if they have children: – less likely to pursue careers in science and engineering after the completion of S/E education – less likely to be in the labor force or employed – less likely to be promoted – and less likely to be geographically mobile

37. Post-S/E baccalaureate career paths Bachelor's Degree in S/E Graduate StudiesWorking No Grad, Not Working (State 5) Grad in S/E (State 1) Grad in Non-S/E (State 2) Working in S/E (State 3) Working in Non-S/E (State 4) Does a Family Life Hamper Women Scientists’ Careers?

38. Female-to-male odds ratio of post-baccalaureate career paths by family status Family Status Grad school or work Grad school Grad School in S/E Work in S/E Single0.901.020.770.78** Married without children 0.28***0.67 0.11** 0.72** Married with children 0.05***0.35*0.39*** Does a Family Life Hamper Women Scientists’ Careers?

39. Female-to-Male Ratio in Labor Force Outcomes by Family Status Family Status Odds of employment Earnings rate Odds of promotion Single2.093***0.929***1.118 Married without children 0.560***0.864***0.985 Married with children 0.406***0.857***0.241*** Does a Family Life Hamper Women Scientists’ Careers?

40. Summary: What are the causes of the persistent inequities in science?  Common explanations not supported – “Critical Filter” Hypothesis – Coursework Hypothesis  Explanations supported – Supply problem – Segregation – Familial gender roles

41. Supply problem  Interest in science is relatively low among girls and young women – Expectation of an S/E college major – Participation in S/E during college  Women are significantly less likely to utilize S/E human capital – Achievement – Post-baccalaureate pursuit of S/E – Transition to the S/E labor force

42. Segregation  Women and men are segregated within science by field and by employment setting – Women are most likely to be in the biological sciences; Men are most likely to be in engineering Gender gaps in transition to the S/E labor force and earnings – Women employed in teaching colleges; Men more likely employed in research universities Gender gaps in publication productivity and earnings

43. Familial gender roles  Marriage per se does not seem to matter much.  Married women are disadvantaged only when they have children: – less likely to pursue S/E careers after the completion of S/E education – less likely to be in the labor force or employed full time – less likely to be promoted – and less likely to be geographically mobile