Sociological Aspects of S/E Career Participation

Sociological Aspects of S/E Career Participation

Yu XieUniversity of Michigan

&

Kimberlee A. ShaumanUniversity 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

Yu XieUniversity of Michigan

&

Kimberlee A. ShaumanUniversity of California-Davis

WOMEN IN SCIENCE: Career Processes and Outcomes

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

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 andengineering

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 revisitedData 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 YearsGrades 7 – 12

Synthetic cohort life course, outcomes examined and data sources

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:

Mean Difference (d)

Data Source

1960 -0.25*** NLS-72

1968 -0.22*** HSBSr

1970 -0.15*** HSBSo

1978 -0.13** LSAY1

1980 -0.09*** NELS *p<.05 **p<.01 ***p<.001 (two-tailed test), for the hypothesis that there is no mean difference between males and females.



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: Achievement ratio Data Source 1960 0.45*** NLS-72

1968 0.47*** HSBSr

1970 0.48*** HSBSo

1978 0.25*** LSAY1

1980 0.60*** NELS

*p<.05 **p<.01 ***p<.001 (two-tailed test), for the hypothesis that there is no mean difference between males and females.



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 Female-to-male

odds ratio

Probability of expecting to major in S&E (n=8,918) (0): Sex 0.31*** (1): (0) + Race + high school program 0.31*** (2): (1) + Math and science achievement 0.34*** (3): (2) + Math and science achievement top 5% 0.34*** (4): (3) + Family of origin influences 0.33*** (5): (4) + Own family expectations/attitudes 0.34*** (6): (5) + Math attitudes 0.35*** (7): (6) + High school math course participation and grades 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) Females Males Algebra 1 74.24 74.03 Geometry 70.98 67.47 Algebra 2 57.27 53.42 Trigonometry 26.78 27.12 Pre-Calculus 18.68 19.14 Calculus 10.38 11.26

Science course taken (% of students) Earth Science 21.40 22.55 Biology 95.09 93.14 Chemistry 60.12 56.91 Physics 24.16 31.36 Advanced biology 22.51 18.71 Advanced chemistry 5.19 5.79


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 77.89 75.61 Science 80.06 77.94


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.

0

5

10

15

20

25

30

35

40

45

50

1965 1970 1975 1980 1985 1990 1995 2000

Year

Per

cen

t wo

men

Biological

Engineering

Mathematical

Physical

Participation in S/E Postsecondary Education Representation of women among bachelors degree

recipients has increased in almost all S/E fields

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

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

Not in Collegeor

Non-S/E Major in College

Bachelor's Degreein S/E Fieldby Pathway:

Reentry:females: 0.004males: 0.004

S/E Majorin College:

females: 0.075males: 0.149

S/E Majorin College

S/E Majorin College

CompletePersistence:


Edu

catio

nal

Sta

te (k)

t




females: 0.063males: 0.046Prob. of exit:






Not in Collegeor






S/E Majorin College

S/E Majorin College



Edu

catio

nal

Sta

te (k)

t







After the transition to college, there are no gender differences in persistence

Participation in S/E Postsecondary Education





Not in Collegeor






S/E Majorin College

S/E Majorin College



Edu

catio

nal

Sta

te (k)

t







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

Proportion earning S/E baccalaureates

Percent of all S/E baccalaureates

Females Males Females Males

All graduating seniors 0.037 0.078

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

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


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

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


2.44***1.060.94

0.41*** 0.45***

Female-to-Male Odds Ratios of Career Transitions

0

5

10

15

20

25

30

35

40

45

50

1950 1960 1970 1980 1990 2000

Year

Per

cen

t w

om

en

Biological

Engineering

Mathematical

Physical

Percent women in S/E occupations by field, 1960-1990

0

5

10

15

20

25

30

35

40

45

50

1950 1960 1970 1980 1990 2000

Year

Per

cen

t w

om

en

Biological

Engineering

Mathematical

Physical

Percent women in S/E occupations by field, 1960-1990

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

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***

Promotion Rate 0.067 0.098***

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***

Promotion Rate 0.067 0.098***

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.

0

0.1

0.2

0.3

0.4

No Kids Children Age 0-6 Children Age 7-12 Children Age 13-18

Family Structure

Migration Rate

Females Males

Predicted Migration Rate by Gender and Family Structure

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.

0.580.632

0.695

0.817

0

0.2

0.4

0.6

0.8

1

1969 1973 1988 1993

Trend in Female-Male Ratio of Publication Rate

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”

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

Estimated Female-to-Male Ratio of Publication

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


Bachelor'sDegree in S/E

Graduate Studies Working

No Grad,Not Working

(State 5)

Grad in S/E(State 1)

Grad inNon-S/E(State 2)

Working in S/E(State 3)

Working inNon-S/E(State 4)


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

Single 0.90 1.02 0.77 0.78**

Married without children

0.28*** 0.67 0.11**

0.72**

Married with children

0.05*** 0.35* 0.39***


Female-to-Male Ratio in Labor Force Outcomes by Family Status

Family StatusOdds 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***


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

Sociological Aspects of S/E Career Participation

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