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