Sociological Aspects of S/E Career Participation Yu Xie University of Michigan & Kimberlee A. Shauman University of California-Davis 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 WOMEN IN SCIENCE: Career Processes and Outcomes Yu Xie University of Michigan & Kimberlee A. Shauman University of California-Davis 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. 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 Synthetic cohort life course, outcomes examined and data sources Grades 7 – 12 High school diploma + 6 years Chapter 2: Gender differences in math and science achievement Chapter 4: Gender differences in the attainment of a science/engineering bachelor’s degree Data Sources: NLS-72, HSBSr, HSBSo, LSAY1, LSAY2, NELS Data Source: HSBSo S/E Bachelor’s Degree + 2 years S/E Master’s Degree + 2 years Chapter 6: Gender differences in career paths after attainment of a master’s degree in S/E Data Source: NES Post-M.S. and Post-Ph.D. Career Years Chapter 7: Demographic and labor force profiles of men and women in science and engineering Data Sources: 1960-1990 Census PUMS, SSE Chapter 3: Gender differences in the expectation of an S/E college major among high school seniors Chapter 5: Beyond the science baccalaureate: gender differences in career paths after degree attainment Chapter 8: Geographic mobility of men and women in science and engineering Data Source: NELS Data Sources: NES, B&B 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 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 “Critical Filter” Hypothesis The gender gap in average mathematics achievement is small and has been declining. Standardized mean gender difference of math achievement scores among high school seniors by cohort School Cohort: 1960 1968 1970 1978 1980 Mean Difference (d) -0.25*** -0.22*** -0.15*** -0.13** -0.09*** Data Source NLS-72 HSBSr HSBSo LSAY1 NELS *p<.05 **p<.01 ***p<.001 (two-tailed test), for the hypothesis that there is no mean difference between males and females. “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. Female-to-male ratio of the odds of achieving in the top 5% of the distribution of math achievement test scores among high school seniors by cohort School Cohort: 1960 1968 1970 1978 1980 Achievement ratio 0.45*** 0.47*** 0.48*** 0.25*** 0.60*** Data Source NLS-72 HSBSr HSBSo LSAY1 NELS *p<.05 **p<.01 ***p<.001 (two-tailed test), for the hypothesis that there is no mean difference between males and females. “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. “Critical Filter” Hypothesis Influence of covariates on the estimated female-to-male odds ratio in logit models for the probability of expecting to major in an S/E field Model description Probability of expecting to major in S&E (n=8,918) (0): Sex (1): (0) + Race + high school program (2): (1) + Math and science achievement (3): (2) + Math and science achievement top 5% (4): (3) + Family of origin influences (5): (4) + Own family expectations/attitudes (6): (5) + Math attitudes (7): (6) + High school math course participation and grades Female-to-male odds ratio 0.31*** 0.31*** 0.34*** 0.34*** 0.33*** 0.34*** 0.35*** 0.34*** “Coursework Hypothesis” Girls are as likely as boys to take math and science courses (except for physics). High school math/science course participation by grade 12 Math course taken (% of students) Algebra 1 Geometry Algebra 2 Trigonometry Pre-Calculus Calculus Science course taken (% of students) Earth Science Biology Chemistry Physics Advanced biology Advanced chemistry Females 74.24 70.98 57.27 26.78 18.68 10.38 Males 74.03 67.47 53.42 27.12 19.14 11.26 21.40 95.09 60.12 24.16 22.51 5.19 22.55 93.14 56.91 31.36 18.71 5.79 “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. Mean Grade 12 math/science course grades Course Females Males Math Science 77.89 80.06 75.61 77.94 “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. Participation in S/E Postsecondary Education Representation of women among bachelors degree recipients has increased in almost all S/E fields 50 45 40 Biological Percent women 35 Engineering 30 Mathematical Physical 25 20 15 10 5 0 1965 1970 1975 1980 1985 Year 1990 1995 2000 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 t Sex-specific probabilities for selected pathways to an S/E baccalaureate Educational expectations, Spring 1982 Educational status, Fall 1982 Educational status, 1984 Educational status, 1986-1988 Educational State (k) Not in College or Non-S/E Major in College females: 0.063 males: 0.046 Prob. of exit: females: 0.821 males: 0.541 S/E Major in College: females: 0.075 males: 0.149 S/E Major in College females: 0.207 males: 0.500 Bachelor's Degree in S/E Field by Pathway: Complete Persistence: females: 0.008 males: 0.039 S/E Major in College females: 0.865 males: 0.919 Reentry: females: 0.004 males: 0.004 females: 0.603 males: 0.566 Sex-specific probabilities for selected pathways to an S/E baccalaureate Participation in S/E Postsecondary Education After the transition to college, there are no gender differences in persistence Sex-specific probabilities for selected pathways to an S/E baccalaureate Participation in S/E Postsecondary Education 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 Proportion earning S/E baccalaureates Percent of all S/E baccalaureates Females 0.037 Males 0.078 Females Males Those expecting an S/E major 0.012 0.042 32.43 53.85 Those expecting a Non-S/E major 0.020 0.031 54.05 39.74 All graduating seniors Post-S/E baccalaureate career paths Bachelor’s Degree in S/E Graduate Studies Graduate School in S/E Graduate School in Non-S/E Work Working in S/E Working in Non-S/E No Graduate School, Not Working Post-S/E baccalaureate career paths 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 Post-S/E baccalaureate career paths Female-to-Male Odds Ratios of Career Transitions Bachelor’s Degree in S/E 0.94 1.06 Graduate Studies 0.41*** Graduate School in S/E 2.44*** Work 0.45*** Graduate School in Non-S/E Working in S/E Working in Non-S/E No Graduate School, Not Working 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 Percent women in S/E occupations by field, 1960-1990 50 45 40 Percent women 35 Biological Engineering 30 Mathematical 25 Physical 20 15 10 5 0 1950 1960 1970 1980 Year 1990 2000 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 Achievement in the S/E labor force Women earn significantly less than men Achievement outcome Female Male Earnings (1989 dollars) $39,332 $52,410*** 0.067 0.098*** Promotion Rate Achievement in the S/E labor force Women earn significantly less than men Women are promoted at a significantly lower rate Achievement outcome Female Male Earnings (1989 dollars) $39,332 $52,410*** 0.067 0.098*** Promotion Rate 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 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. Predicted Migration Rate by Gender and Family Structure Migration Rate 0.4 0.3 0.2 0.1 0 No Kids Children Age 0-6 Children Age 7-12 Family Structure Females Males Children Age 13-18 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.” The “Productivity Puzzle” Sex differences in research productivity declined between 1960s and 1990s. Trend in Female-Male Ratio of Publication Rate 1 0.817 0.8 0.695 0.6 0.58 0.632 0.4 0.2 0 1969 1973 1988 1993 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. The “Productivity Puzzle” Estimated Female-to-Male Ratio of Publication Model description 1969 1973 1988 1993 (0): Sex 0.580*** 0.632*** 0.695** 0.817 (1): (0) + Field + Time for Ph.D. + Experience 0.630*** 0.663*** 0.800 0.789* (2):(1)+Institution + Rank +Teaching + Funding + RA 0.952 0.936 0.775 0.931 (3): (2) + Family/Marital Status 0.997 0.971 0.801 0.944 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 – – Does a Family Life Hamper Women Scientists’ Careers? Post-S/E baccalaureate career paths Bachelor's Degree in S/E Graduate Studies Grad in S/E (State 1) Grad in Non-S/E (State 2) Working Working in S/E (State 3) Working in Non-S/E (State 4) No Grad, Not Working (State 5) Does a Family Life Hamper Women Scientists’ Careers? Female-to-male odds ratio of post-baccalaureate career paths by family status Grad school or work Grad school Grad School in S/E Work in S/E 0.90 1.02 0.77 0.78** Married without children 0.28*** 0.67 Married with children 0.05*** Family Status Single 0.72** 0.11** 0.35* 0.39*** Does a Family Life Hamper Women Scientists’ Careers? Female-to-Male Ratio in Labor Force Outcomes by Family Status Odds of employment Earnings rate Odds of promotion Single 2.093*** 0.929*** 1.118 Married without children 0.560*** 0.864*** 0.985 Married with children 0.406*** 0.857*** 0.241*** Family Status 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 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 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 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