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Heterogeneity in Human Capital Investments: High School Curriculum, College Major, and Careers

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Motivated by the large differences in labor market outcomes across college majors, we survey the literature on the demand for and return to high school and postsecondary education by field of study. We combine elements from several papers to provide a dynamic model of education and occupation choice that stresses the roles of the specificity of human capital and uncertainty about preferences, ability, education outcomes, and labor market returns. The model implies an important distinction between the ex ante and ex post returns to education decisions. We also discuss some of the econometric difficulties in estimating the causal effects of field of study on wages in the context of a sequential choice model with learning. Finally, we review the empirical literature on the choice of curriculum and the effects of high school courses and college major on labor market outcomes.
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NBER WORKING PAPER SERIES
HETEROGENEITY IN HUMAN CAPITAL INVESTMENTS:
HIGH SCHOOL CURRICULUM, COLLEGE MAJOR, AND CAREERS
Joseph G. Altonji
Erica Blom
Costas Meghir
Working Paper 17985
http://www.nber.org/papers/w17985
NATIONAL BUREAU OF ECONOMIC RESEARCH
1050 Massachusetts Avenue
Cambridge, MA 02138
April 2012
Forthcoming at the Annual Review of Economics doi: 10.1146/annurev-economics-080511-110908.
We thank Sarah Amanda Levis for excellent research assistance and Peter Arcidiacono, Richard Murnane,
Robert Triest, Seth Zimmerman and participants in a conference at the Federal Reserve Bank of Atlanta
(September 2011) for helpful comments. Part of this research was conducted while Altonji was visiting
the LEAP Center and the Department of Economics at Harvard University. We also received research
support from Department of Economics, the Economic Growth Center, and the Cowles Foundation,
Yale University. We are solely responsible for errors and omissions. The views expressed herein are
those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
NBER working papers are circulated for discussion and comment purposes. They have not been peer-
reviewed or been subject to the review by the NBER Board of Directors that accompanies official
NBER publications.
© 2012 by Joseph G. Altonji, Erica Blom, and Costas Meghir. All rights reserved. Short sections of
text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit,
including © notice, is given to the source.
Heterogeneity in Human Capital Investments: High School Curriculum, College Major, and
Careers
Joseph G. Altonji, Erica Blom, and Costas Meghir
NBER Working Paper No. 17985
April 2012
JEL No. I21,J24
ABSTRACT
Motivated by the large differences in labor market outcomes across college majors, we survey the
literature on the demand for and return to high school and post-secondary education by field of study.
We combine elements from several papers to provide a dynamic model of education and occupation
choice that stresses the roles of specificity of human capital and uncertainty about preferences, ability,
education outcomes, and labor market returns. The model implies an important distinction between
the ex ante and ex post returns to education decisions. We also discuss some of the econometric difficulties
in estimating the causal effects of field of study on wages in the context of a sequential choice model
with learning. Finally, we review the empirical literature on choice of curriculum and the effects of
high school courses and college major on labor market outcomes.
Joseph G. Altonji
Department of Economics
Yale University
Box 208264
New Haven, CT 06520-8264
and NBER
joseph.altonji@yale.edu
Erica Blom
Department of Economics
Yale University
Box 208264
New Haven, CT 06520-8264
erica.blom@yale.edu
Costas Meghir
Department of Economics
Yale University
New Haven, CT 06520-8264
c.meghir@yale.edu
An online appendix is available at:
http://www.nber.org/data-appendix/w17985
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Table 1: College major characteristics
Major Share % Math Science SAT SAT Mean p10 p90 p90 % % %
female credits credits M V wages wages wages wagesaMA PhD PDb
Mathematics 1.5 0.4 27.7 13.7 592 538 42.71 15.91 73.53 63.73 33.9 9.1 4.4
Mech. Eng. 1.5 0.1 12.3 66.1 613 566 44.12 21.03 66.18 58.82 31.4 4.1 1.9
Elec. Eng. 1.9 0.1 12.3 66.5 606 571 46.45 21.79 70.08 63.24 33.8 5.2 2.3
Chemistry 1.2 0.4 12.0 57.2 604 597 45.37 13.73 87.83 52.29 22.1 20.8 16.0
Comp. & IT 2.9 0.3 11.4 11.1 582 556 37.99 17.16 58.82 56.37 22.1 1.7 1.2
Bio. Sci. 4.5 0.5 5.8 56.1 577 575 41.27 12.75 85.29 46.08 20.2 11.3 22.0
Economics 2.1 0.3 5.6 6.3 597 573 50.60 15.25 111.76 78.43 26.6 3.4 9.0
Finance 1.9 0.3 4.9 5.1 563 534 42.34 15.69 77.30 65.36 20.4 0.7 3.7
Accounting 4.2 0.5 3.9 3.7 571 534 39.46 15.69 67.23 60.33 18.6 0.6 3.9
Marketing 2.3 0.5 4.0 5.1 526 514 34.12 13.94 58.82 57.29 11.8 0.2 1.3
Educ. (other)b12.3 0.8 3.1 7.1 488 496 24.54 12.75 38.64 32.68 39.2 2.1 2.6
Bus. Mgmt & Admin. 6.8 0.4 3.7 4.3 522 510 33.42 13.73 55.56 51.96 16.5 0.6 2.1
Agr. & Agr. Sci. 1.1 0.3 3.6 21.1 546 549 29.04 11.13 49.02 45.75 15.8 5.5 5.2
Psychology 4.8 0.7 2.7 8.2 530 540 29.15 12.25 49.02 41.18 30.8 6.9 6.0
Music & Drama 1.3 0.6 2.3 6.5 539 575 25.56 10.59 42.58 37.91 27.7 4.1 3.2
Fit. & Nutr. 1.0 0.6 2.9 19.9 520 518 25.20 11.27 40.96 38.15 19.4 2.5 3.3
Comm. 2.9 0.6 1.6 5.8 512 537 29.20 12.50 49.41 49.02 14.2 1.1 2.8
Soc. Sci. (oth.) 3.2 0.6 2.0 7.2 514 526 28.79 12.66 47.06 41.67 25.7 3.7 4.9
Letters 5.2 0.6 2.3 6.1 540 592 30.63 12.35 51.93 46.35 25.8 3.9 7.4
Poli. Sci. 2.4 0.4 2.2 6.2 542 571 42.09 14.71 75.41 57.25 21.1 3.6 21.6
History 2.3 0.4 2.2 6.0 558 595 36.12 12.65 64.17 49.02 27.9 4.9 13.5
Art & Art Hist. 1.5 0.7 2.0 6.9 555 592 27.09 11.09 44.71 43.57 21.2 1.2 2.5
Social Work & HR 1.5 0.8 1.5 5.2 460 487 25.41 12.50 40.20 38.24 34.0 0.9 2.1
Phil. & Rel. 1.3 0.3 1.8 5.7 567 595 27.52 10.23 47.06 39.22 28.3 8.4 9.1
Nursing 3.6 0.9 1.0 13.0 488 497 33.51 18.82 49.02 46.08 18.2 1.2 3.3
Source: NCES B&B 1993/2003 for course counts and SAT scores. Majors with a share less than 1%, or missing
SAT scores, are not shown. Shares and hourly wage data are from the 2009 ACS, top- and bottom-coded at 5 and
400 USD per hour, respectively.
Sample selection: Wage observations are included if the individual has at least a bachelor’s degree, is working >34
hours per week and >40 weeks per year, and is 23-59 years old.
aExcluding advanced degrees.
bProfessional degree.
cIncludes library science.
1
Table 2: Summary of selected empirical studies on the returns to college major
Study Data & Method. Controls Outcome variable Majors Outcomes
Men Women
Daymont &
Andrisani
(1984)
NLS72; OLS work experience; preferences;
father’s occupation; marital
status; highest degree; type
and size of community; race;
region
log hourly
earnings
Business
Engineering
Math & sci.
Soci. sci.
Humanities
.16
.33
.18
.06
.04
.17
.19
.25
.00
-.05
1 yr exp. 12 yrs exp.
Berger (1988) NLSM;
conditional logit
and selection
correction in the
wage equation
log of yrs of exp, grad
yr1900, and their
interaction; IQ score and
Knowledge of World of Work
score; US male unemp. rate,
race, health status, married,
smsa, South, enrolled in
school, log of annual wks
worked; selection correction
log hourly wages
for 1974 male
college graduates
in 1986 USD,
corrected for
selection bias
Business
Engineering
Science
Lib. Arts
.3473
.4130
.1237
.1045
.1332
.3637
.2226
-.0719
(1)a(2)b
James et al
(1989)
NLS72 (men
only); WLS
family background, SAT, hs
rank, acad. track, math
credits, Catholic hs, various
college-level variables, labor
market variables; occupation
and industry dummies in (2)
1985 log annual
earnings
Business
Engineering
Math & sci.
Soc. sci.
Humanities
.262
.47
.1966
.2378
.059
.1452
.451
.1198
.1829
-.026
Men Women
Altonji (1993) NLS72; OLS SAT, hs grades,
self-assessment of college
ability; various education
interactions; exp and
exp-squared; gender, race,
family background; hs
curriculum; post-grad deg.
log of real hourly
wage; coefficients
on terminal
majors presented
not all presented)
Bus.
Engineering
Phys. sci.
Math, CS
Life sci.
Soc. sci.
Humanities
.1796
.4119
.2432
.3887
.1231
.0975
.0646
.2422
.2836
.0743
.2325
.2057
.0117
-.0048
aWithout occupation or industry dummies.
bWith occupation and industry dummies.
2
Study Data & Method. Controls Outcome variable Majors Outcomes
Men Women
Rumberger
and Thomas
(1993)
Survey of Recent
College graduates
(1987);
hierarchical linear
modeling, OLS
family background, race,
GPA, private college, college
selectivity, labor market
variables
1987 log annual
earnings
Business
Engineering
Sci. & math
Health
Soc. sci.
.1839
.3913
.2552
.2984
.0765
.2521
.5135
.3036
.4433
.1255
Whites Blacks
Loury &
Garman
(1995)
NLS72; OLS college selectivity, years of
education, parental income,
GPA, SAT, weeks worked,
rural dummy
ln weekly
earnings, 1979 or
1986
Business
Eng. & Sci.
Soc. sci.
Humanities
.212
.374
.094
-.164
.262
.549
-.097
.030
Men Women
Grogger &
Eide (1995)
NLS72, HSB;
GLS
std. tests, hs grades; family
income; experience; race;
educ. attainment (not shown:
with occupation controls),
full-time workers only
log hourly wage
1977-79, 86
Business
Engineering
Science
Soc. sci.
.155
.2797
.0607
.0215
.1057
.0659
.2152
.0168
Men Women
Finnie &
Frenette
(2003)
National
Graduate Survey
(Stats Can); OLS
pre-programme educ. level,
age, post-grad exp.,
self-employment status,
marriage/children, region,
language; adv. degree-holders
excluded
log annual
earnings 1995
Commerce
Eng & CS
Math & Sci.
Health
Soc. sci.
Arts & Hum.
.06
.22
.31
.06
-.12
-.15
.04
.16
.09
.19
-.04
-.06
Hamermesh &
Donald (2008)
graduates of
UT-Austin,
1980-2000c;
double selection
correction (into
employment and
survey
non-response)
high-school background,
college achievement,
demographic, post-grad deg.,
hours worked, quadratic in
propensity scores for working
& survey response
log earnings
(selected majors
only presented)
Bus. (hard)
Bus. (soft)
Engineering
Nat. sci.
Soc. sci.
Humanities
.489***
.378***
.316***
.265***
.279***
.086
*** p<0.01, ** p<0.05, * p<0.10
cSelected years.
3
Table 3: Effects of college major on log wages by gender, with and without occupation
controls
Major Major dummies only With occupation controls
Female Male Female Male
Communications 0.202*** 0.207*** 0.063*** 0.058**
Computer Science 0.441*** 0.531*** 0.161*** 0.242***
Elementary Education -0.024* -0.009 -0.015 0.009
Electrical Engineering 0.556*** 0.561*** 0.258*** 0.293***
Mechanical Engineering 0.554*** 0.524*** 0.265*** 0.264***
English Language And Literature 0.107*** 0.152*** 0.026* 0.063***
Liberal Arts 0.073*** 0.154*** 0.021 0.055*
Biology 0.196*** 0.302*** 0.068*** 0.114***
Mathematics 0.288*** 0.426*** 0.143*** 0.224***
Chemistry 0.250*** 0.366*** 0.101*** 0.193***
Criminal Justice And Fire Protection 0.076*** 0.226*** -0.013 0.076***
Economics 0.400*** 0.517*** 0.224*** 0.275***
Anthropology And Archeology 0.069** 0.135*** -0.001 0.053
Political Science And Government 0.246*** 0.327*** 0.112*** 0.158***
Sociology 0.077*** 0.165*** 0.012 0.075***
Fine Arts -0.021 0.017 -0.067** -0.035
Nursing 0.391*** 0.408*** 0.172*** 0.243***
General Business 0.218*** 0.339*** 0.077*** 0.142***
Accounting 0.310*** 0.431*** 0.143*** 0.199***
Business Management And Administration 0.199*** 0.292*** 0.054*** 0.104***
Marketing And Marketing Research 0.256*** 0.356*** 0.089*** 0.150***
Finance 0.342*** 0.518*** 0.151*** 0.243***
History 0.105*** 0.167*** 0.033* 0.064***
R20.200 0.217 0.330 0.337
SD of major coefficients 0.146 0.177 0.074 0.098
N 125794 140706 124858 139493
Notes: *** p<0.01, ** p<0.05, * p<0.10
All specifications include dummy variables for highest level of education attained, a
cubic in potential experience, and race dummies. Bachelor’s degrees are 4-digit; only a
selected sample of the 171 are shown. Wages are top- and bottom-coded at 5 and 400
USD per hour, respectively. General Education is the excluded category. Occupation
controls are 5-digit. SD is calculated over all majors using ACS weights.
Sample selection: Observations are included if the individual has at least a bachelor’s
degree, is working >34 hours per week and >40 weeks per year, and is 23-59 years old.
4
Figure 1: Relative fraction female, by major
.4 .6 .8 1 1.2
1970 1980 1990 2000 2010
Graduation year
General business Accounting
Business mgmt and admin Marketing
Finance
0 .5 1 1.5
1970 1980 1990 2000 2010
Graduation year
Mechanical engineering Electrical engineering
Computer science Communications
.2 .4 .6 .8 1 1.2
1970 1980 1990 2000 2010
Graduation year
Biology Mathematics
Chemistry Physics
.4 .6 .8 1 1.2 1.4
1970 1980 1990 2000 2010
Graduation year
Poli sci & govt Economics
History Sociology
Psychology
1 1.2 1.4 1.6 1.8
1970 1980 1990 2000 2010
Graduation year
Liberal arts English
Elementary ed General ed
Note: Relative fraction female is calculated by dividing the fraction female in a partic-
ular major in a particular year by the fraction of female college graduates that year,
then smoothed using a three-year moving average. Data are from the ACS.
5
Figure 2: Average of major coefficients by age
.1 .15 .2 .25 .3 .35
1970 1980 1990 2000 2010
Graduation year
Men Male weights with female coefficients
Women Female weights with male coefficients
6
Figure 3: Occupational dispersion by age
0 1 2 3 4
Density
.2 .4 .6 .8 1
Proportion of people in top ten occupations by major
Age 25 to 34
Age 35 to 49
Age 50 to 59
Cell size ! 350
7
WEB SUPPLEMENTAL APPENDIX
Empirical evidence on choice of high school curriculum
There is relatively little empirical work in economics on how high school students choose curricula, partic-
ularly in comparison to research on choice of college major. The main margins of choice for a typical high
school student are among vocational, general, and academic curricula, and, within the latter, between a
focus in social sciences and humanities and a focus in mathematics and science. The level and number of
courses (subject to promotion and graduation requirements) are also choice variables. Contrary to popular
perception, the number and level of courses in academic subjects taken by high school students in America
have risen over the past thirty years. Data on course taking by high school seniors reported in Ingels et al
(2008) for the years 1982, 1992, and 2004 shows an increase from 1982, particularly in science. Supplemen-
tary Table 1 compares course-taking trends from 1990 to 2009 for high school graduates, as reported by Nord
et al (2011). Course-taking overall is up and in core academic subjects (mathematics, science, social science,
and English). Furthermore, the percentage of students taking more rigorous programs of study has increased
as well. Of course, one would expect curriculum choice to change over time as the occupational mix of labor
demand changes. As we discuss in Section 4, part of the trend is due to changes in state level graduation
requirements. It would be interesting to decompose trends in college attendance and major choice over the
past 40 years into the contribution of changes in high school curriculum and the contribution of changes in
the link between high school curriculum and major choice.
We are not aware of any study that has estimated a s t r u c t u r a l m o del of high sch o ol curriculum choice
along the lines sketched in the previous section. Zietz and Joshi (2005) use the NLSY (1997) to estimate
a two-period model of leisure maximization, subject to minimum consumption constraints. They find that
“academic aptitude, pre-high school academic performance, and lifetime consumption goals as driven by
peer pressure and family background are by far the most important determinants of program choice.” Meer
(2007), using the NELS88 data, finds that the principle of comparative advantage is at play when students
choose between academic and vocational high school curricula. In the remainder of this section, we briefly
summarize some of the descriptive evidence on the determinants of curriculum.
There is a substantial literature on the role of gender, race/ethnicity, and socio-economic status in
determining high school curriculum, which we touch on briefly here. Historically, girls tended to take less
math and science than boys, despite equal (or greater) opportunities or prior achievement (Oakes (1990),
Catsambis (1994), Ayalon (1995)). Girls tend to have less positive attitudes toward or fewer aspirations
for careers in math or science; they are less interested in math and less confident about their mathematics
1
abilities (Dick & Rallis (1991)). However, Goldin et al (2006) report that among graduating seniors the
male/female ratio of mean number of high school courses in math, science courses, and chemistry declined
from between 1.3 and 1.4 in 1957 to between 0.9 and 1.0 in 2000. In physics, the male/female ratio declined
from 3.1 to 1.21.
Students from high SES backgrounds tend to be streamed into more academic tracks; this can be explained
by a variety of factors including higher intrinsic ability (cognitive or non-cognitive), choice of scho ol and
neighborhood, better preparation in primary school, peer eects, or parental lobbying (Vanfossen, Jones &
Spade (1987)). Interestingly, African-American and Latino students have positive attitudes toward math,
despite low achievement (Catsambis (1994)). That said, minorities enroll in math-intensive courses at lower
rates than whites; however, this is mostly explained by SES and prior achievement (as measured, for example,
by GPA) (Ferguson (2009)).
Recent reports discussing student course taking and achievement with emphasis science, engineering,
technology and math, include President’s Council on Science and Technology (2011).
School-level influences
School policies, particularly course requirements, scope of oerings, and tracking guidelines, are an important
influence on curriculum choice. These policies are in turn shaped in part by state and school district
regulations, as we document below. A substantial fraction of the variance in curriculum choice is across high
schools (see note 9).
To the extent t h a t s c h o o l behav io r c a n be taken as independent of the unobserved ch a r a c t e r i s t i c s o f th e
students that attend the school, such variation can be (and has been) used to identify the eects of particular
high school curricula. In practice, however, school choice and cross school competition and specialization
(attracting particular parts of the student market) will undermine the credibility of such a strategy. In
Section 3, we discuss three studies that use curriculum reforms as a source of exogenous variation.
Peers, teachers and facilities (e.g., availability of science labs or a theater) may also influence curriculum
choice, but attempts to identify the causal eect of these factors are subject to the same endogeneity problem.
References
[1] Ayalon H. 1995. Math as a gatekeeper: ethnic and gender inequality in course taking of the
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2
[2] Catsambis S. 1994. The path to math: gender and racial-ethnic dierences in mathematics
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Educ. 60(2):104–22
[10] Zietz J, Joshi P. 2005. Academic choice behavior of high school students: economic rationale
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3
Supplementary Tables and Figures
1
Supplementary Table 1: Trends in high school course-taking
1990 2005
Credits earned, total 23.6 27.2
Core academic, total 13.7 16.0
Core science 2.8 3.5
Core math 3.2 3.9
Core social science 3.5 4.2
Core English 4.1 4.4
Percentage taking curricula that are:
Rigorous 5 13
Midlevel 26 46
Standard 9 16
Below standard 60 25
Percentage taking STEM courses:
Algebra II 53 76
Calculus 7 17
Advanced biology 28 45
Chemistry 45 70
Physics 24 39
From 2011 NAEP report. All differences are significant
at 5%.
2
Supplementary Table 2: College major characteristics, no advanced degrees
Major Share % Math Science SAT SAT Mean p10 p90
female credits credits M V wages wages wages
Mathematics 1.2 0.4 27.7 13.7 592 538 37.76 14.01 63.73
Mech. Eng. 1.4 0.1 12.3 66.1 613 566 40.43 19.61 58.82
Elec. Eng. 1.7 0.1 12.3 66.5 606 571 41.61 20.10 63.24
Comp. & IT 3.2 0.3 11.4 11.1 582 556 35.83 16.18 56.37
Bio. Sci. 3.2 0.6 5.8 56.1 577 575 27.26 10.98 46.08
Economics 2.0 0.3 5.6 6.3 597 573 43.15 13.90 78.43
Finance 2.2 0.4 4.9 5.1 563 534 38.21 14.71 65.36
Accounting 4.9 0.5 3.9 3.7 571 534 36.88 14.71 60.33
Marketing 3.0 0.5 4.0 5.1 526 514 32.90 13.73 57.29
Educ. (other)b10.5 0.8 3.1 7.1 488 496 21.17 11.27 32.68
Bus. Mgmt & Admin. 8.3 0.4 3.7 4.3 522 510 31.56 13.24 51.96
Agr. & Agr. Sci. 1.2 0.3 3.6 21.1 546 549 26.51 10.46 45.75
Comm. Arts 1.3 0.7 3.0 6.7 529 527 25.00 11.40 41.18
Psychology 4.1 0.7 2.7 8.2 530 540 24.61 11.03 41.18
Music & Drama 1.3 0.6 2.3 6.5 539 575 22.57 9.80 37.91
Fit. & Nutr. 1.2 0.6 2.9 19.9 520 518 22.91 10.62 38.15
Comm. 3.6 0.6 1.6 5.8 512 537 28.17 12.25 49.02
Soc. Sci. (oth.) 3.1 0.6 2.0 7.2 514 526 25.56 11.76 41.67
Letters 4.9 0.6 2.3 6.1 540 592 27.14 11.62 46.35
Poli. Sci. 2.0 0.4 2.2 6.2 542 571 33.32 12.60 57.25
History 1.8 0.4 2.2 6.0 558 595 29.52 11.13 49.02
Art & Art Hist. 1.7 0.7 2.0 6.9 555 592 25.57 10.37 43.57
Social Work & HR 1.4 0.8 1.5 5.2 460 487 23.19 11.76 38.24
Phil. & Rel. 1.1 0.4 1.8 5.7 567 595 23.77 9.80 39.22
Journ. 1.2 0.6 1.1 5.5 496 533 29.94 12.20 50.98
Nursing 4.2 0.9 1.0 13.0 488 497 31.12 17.71 46.08
Source: NCES B&B 1993/2003 for course counts and SAT scores. Not all majors are shown; only those which
appear in Table 1 are presented. Shares and hourly wage data are from the 2009 ACS, top- and bottom-coded at
5 and 400 USD per hour, respectively.
Sample selection: Wage observations are included if the individual has a bachelor’s degree, is working >34 hours
per week and >40 weeks per year, and is 23-59 years old. Individuals with an advanced degree are excluded.
aIncludes library science.
3
Supplementary Table 3: Effects of college major on log wages by gender, with and without
occupation controls – all control variables and major coefficients reported
Major Major dummies only With occupation controls
Female Male Female Male
PhD 0.290*** 0.220*** 0.296*** 0.264***
Masters 0.204*** 0.173*** 0.181*** 0.139***
Professional degree 0.441*** 0.497*** 0.254*** 0.259***
Potential experience 0.083*** 0.097*** 0.066*** 0.083***
Potential experience2-0.003*** -0.003*** -0.002*** -0.002***
Potential experience30.000*** 0.000*** 0.000*** 0.000***
Black, non-hispanic -0.084*** -0.215*** -0.052*** -0.137***
Native American, non-hispanic -0.163*** -0.171*** -0.132*** -0.139***
Asian, non-hispanic -0.022** -0.099*** -0.000 -0.076***
Pacific Islander, non-hispanic -0.220** -0.159 -0.114 -0.089
Mixed raced, non-hispanic -0.044** -0.085*** -0.038** -0.059***
Any race hispanic -0.088*** -0.205*** -0.037*** -0.130***
General Agriculture -0.043 -0.059 -0.094* -0.068
Agriculture Production And Management 0.011 0.047 -0.101* 0.034
Agricultural Economics 0.074 0.150** -0.010 0.027
Animal Sciences -0.079* -0.078* -0.074* -0.060
Food Science 0.255*** 0.236* 0.110 0.120
Plant Science And Agronomy -0.027 0.026 -0.041 0.029
Soil Science -0.292 0.127 -0.280 0.103
Miscellaneous Agriculture 0.053 0.154 -0.042 0.079
Environmental Science 0.095** 0.162*** -0.032 0.036
Forestry 0.250*** 0.157*** 0.162*** 0.048
Natural Resources Management 0.047 0.112*** -0.038 0.000
Architecture 0.238*** 0.272*** 0.067* 0.086***
Area Ethnic And Civilization Studies 0.155*** 0.247*** 0.063* 0.122**
Communications 0.202*** 0.207*** 0.063*** 0.058**
Journalism 0.174*** 0.183*** 0.029 0.033
Mass Media 0.112*** 0.102*** 0.013 -0.011
Advertising And Public Relations 0.181*** 0.228*** 0.018 0.057
Communication Technologies 0.188*** 0.165*** 0.039 0.042
Computer And Information Systems 0.295*** 0.421*** 0.072** 0.160***
Computer Programming And Data Processi -0.052 0.229*** -0.091 0.043
Computer Science 0.441*** 0.531*** 0.161*** 0.242***
Information Sciences 0.410*** 0.421*** 0.167*** 0.164***
Computer Administration Management And 0.290*** 0.357*** 0.076 0.134**
Computer Networking And Telecommunicat 0.174** 0.258*** 0.036 0.074
4
Major Major dummies only With occupation controls
Female Male Female Male
Cosmetology Services And Culinary Arts -0.163* -0.024 -0.003 0.165*
Educational Administration And Supervi 0.106** 0.069 0.072 -0.011
School Student Counseling -0.006 0.065 0.018 0.122
Elementary Education -0.024* -0.009 -0.015 0.009
Mathematics Teacher Education 0.051* 0.006 0.020 0.015
Physical And Health Education Teaching 0.059** -0.002 0.046* -0.002
Early Childhood Education -0.057*** -0.230 -0.015 -0.164
Science And Computer Teacher Education -0.017 0.018 -0.015 0.053
Secondary Teacher Education 0.031 0.010 0.010 0.013
Special Needs Education 0.081*** 0.105** 0.067*** 0.102**
Social Science Or History Teacher Educ 0.013 -0.014 -0.001 -0.016
Teacher Education: Multiple Levels -0.012 0.003 -0.014 0.021
Language And Drama Education 0.031 0.013 0.016 0.035
Art And Music Education -0.004 -0.040 -0.007 0.000
Miscellaneous Education 0.043* 0.052 0.015 -0.011
General Engineering 0.417*** 0.392*** 0.170*** 0.169***
Aerospace Engineering 0.616*** 0.548*** 0.274*** 0.272***
Biological Engineering 0.224** 0.135* 0.053 0.025
Architectural Engineering 0.542*** 0.357*** 0.262** 0.171**
Biomedical Engineering 0.468*** 0.472*** 0.203** 0.171**
Chemical Engineering 0.526*** 0.614*** 0.252*** 0.346***
Civil Engineering 0.406*** 0.482*** 0.138*** 0.240***
Computer Engineering 0.562*** 0.606*** 0.227*** 0.293***
Electrical Engineering 0.556*** 0.561*** 0.258*** 0.293***
Engineering Mechanics Physics And Scie 0.715*** 0.429*** 0.379** 0.235***
Environmental Engineering 0.400*** 0.530*** 0.166* 0.267***
Geological And Geophysical Engineering 0.342 0.639*** 0.063 0.385***
Industrial And Manufacturing Engineeri 0.483*** 0.469*** 0.221*** 0.227***
Materials Engineering And Materials Sc 0.341** 0.429*** 0.064 0.194***
Mechanical Engineering 0.554*** 0.524*** 0.265*** 0.264***
Metallurgical Engineering 0.374* 0.452*** 0.155 0.209***
Mining And Mineral Engineering 0.771* 0.412*** 0.590 0.215**
Naval Architecture And Marine Engineer 0.530** 0.360*** 0.246* 0.147
Nuclear Engineering 0.600** 0.651*** 0.364 0.406***
Petroleum Engineering 0.682*** 0.869*** 0.332* 0.590***
Miscellaneous Engineering 0.260*** 0.394*** 0.129 0.206***
Engineering Technologies 0.141 0.345*** 0.010 0.143***
Engineering And Industrial Management 0.298** 0.374*** 0.111 0.135**
Electrical Engineering Technology 0.150 0.315*** -0.075 0.140***
Industrial Production Technologies 0.282*** 0.261*** 0.146* 0.087**
Mechanical Engineering Related Technol 0.481*** 0.262*** 0.186* 0.107**
5
Major Major dummies only With occupation controls
Female Male Female Male
Miscellaneous Engineering Technologies 0.176* 0.327*** 0.041 0.121***
Linguistics And Comparative Language A 0.080* 0.172* 0.001 0.047
French German Latin And Other Common F 0.128*** 0.164*** 0.066*** 0.070*
Other Foreign Languages 0.081 0.117* 0.017 0.047
Family And Consumer Sciences 0.020 0.203*** -0.002 0.102*
Court Reporting 0.289* -0.013 0.067 -0.014
Pre-Law And Legal Studies 0.139*** 0.234*** 0.017 0.117*
English Language And Literature 0.107*** 0.152*** 0.026* 0.063***
Composition And Speech 0.090* 0.141** 0.002 0.090*
Liberal Arts 0.073*** 0.154*** 0.021 0.055*
Humanities 0.113* 0.113 0.044 0.004
Library Science -0.046 0.110 -0.036 0.073
Biology 0.196*** 0.302*** 0.068*** 0.114***
Biochemical Sciences 0.262*** 0.308*** 0.096** 0.111**
Botany -0.012 0.008 -0.062 -0.007
Molecular Biology 0.196*** 0.260*** 0.080 0.112*
Ecology 0.050 0.068 -0.008 0.012
Genetics 0.196** 0.178 0.080 0.025
Microbiology 0.185*** 0.228*** 0.063 0.071
Pharmacology 0.387** 0.168 0.135 0.022
Physiology 0.157*** 0.193*** 0.016 0.077
Zoology 0.088 0.328*** -0.005 0.153***
Miscellaneous Biology 0.154** 0.189*** 0.075 0.057
Mathematics 0.288*** 0.426*** 0.143*** 0.224***
Applied Mathematics 0.537*** 0.641*** 0.286*** 0.375***
Statistics And Decision Science 0.473*** 0.523*** 0.228*** 0.206***
Military Technologies 0.670*** 0.280 0.761*** 0.099
Intercultural And International Studie 0.119** 0.192*** 0.019 0.061
Nutrition Sciences 0.144*** 0.402*** 0.081* 0.232*
Neuroscience 0.088 0.160 -0.094 -0.032
Mathematics And Computer Science 0.722** 0.638*** 0.444* 0.335***
Cognitive Science And Biopsychology 0.164 0.367** 0.022 0.137
Interdisciplinary Social Sciences 0.082* 0.195*** 0.019 0.081
Multi-Disciplinary Or General Science 0.116*** 0.282*** 0.015 0.110***
Physical Fitness Parks Recreation And 0.018 0.045* -0.034 0.005
Philosophy And Religious Studies 0.086* -0.003 0.028 -0.004
Theology And Religious Vocations -0.242*** -0.304*** -0.172*** -0.142***
Physical Sciences -0.085 0.130 -0.163* 0.016
Astronomy And Astrophysics 0.438** 0.339** 0.378* 0.212**
Atmospheric Sciences And Meteorology 0.196 0.335*** 0.097 0.152**
Chemistry 0.250*** 0.366*** 0.101*** 0.193***
6
Major Major dummies only With occupation controls
Female Male Female Male
Geology And Earth Science 0.162*** 0.261*** 0.018 0.117***
Geosciences 0.332* 0.422*** 0.095 0.213**
Oceanography 0.025 0.244*** -0.037 0.086
Physic 0.292*** 0.383*** 0.113*** 0.187***
Nuclear, Industrial Radiology, And Bio 0.155 0.276*** 0.000 0.112
Psychology 0.076*** 0.157*** 0.019 0.051**
Educational Psychology -0.015 -0.098 -0.026 -0.115
Clinical Psychology 0.149** 0.119 0.109 0.087
Counseling Psychology -0.095** -0.102 -0.100** -0.111
Industrial And Organizational Psycholo 0.176** 0.466*** 0.046 0.247***
Social Psychology 0.054 0.129 -0.007 -0.028
Miscellaneous Psychology 0.060 0.199** -0.004 0.149*
Criminal Justice And Fire Protection 0.076*** 0.226*** -0.013 0.076***
Public Administration 0.240*** 0.292*** 0.051 0.098**
Public Policy 0.204** 0.346*** 0.028 0.170*
Human Services And Community Organizat -0.077** -0.016 -0.098*** -0.052
Social Work -0.027 0.009 -0.034* 0.017
General Social Sciences 0.055* 0.166*** 0.017 0.099**
Economics 0.400*** 0.517*** 0.224*** 0.275***
Anthropology And Archeology 0.069** 0.135*** -0.001 0.053
Criminology 0.123** 0.191*** 0.052 0.064
Geography 0.154*** 0.212*** 0.004 0.085***
International Relations 0.242*** 0.398*** 0.093** 0.229***
Political Science And Government 0.246*** 0.327*** 0.112*** 0.158***
Sociology 0.077*** 0.165*** 0.012 0.075***
Miscellaneous Social Sciences 0.340*** 0.364*** 0.164** 0.213**
Construction Services 0.298* 0.430*** 0.121 0.225***
Electrical And Mechanic Repairs And Te -0.550* 0.145* -0.332 0.108
Precision Production And Industrial Ar 0.122* -0.003 0.025 0.013
Transportation Sciences And Technologi 0.292*** 0.259*** 0.111 0.081**
Fine Arts -0.021 0.017 -0.067** -0.035
Drama And Theater Arts -0.025 -0.089* -0.065* -0.135***
Music -0.109*** -0.034 -0.109*** -0.038
Visual And Performing Arts -0.097 0.122 -0.084 0.028
Commercial Art And Graphic Design 0.093*** 0.127*** -0.017 0.009
Film Video And Photographic Arts 0.014 0.082 -0.012 0.007
Art History And Criticism 0.132*** 0.263** 0.064* 0.149
Studio Arts -0.020 -0.147* -0.031 -0.178**
General Medical And Health Services 0.163*** 0.183*** 0.032 0.105*
Communication Disorders Sciences And S 0.144*** 0.294*** 0.049* 0.138*
Health And Medical Administrative Serv 0.197*** 0.242*** 0.058 0.058
7
Major Major dummies only With occupation controls
Female Male Female Male
Medical Assisting Services 0.338*** 0.315*** 0.177*** 0.152*
Medical Technologies Technicians 0.252*** 0.360*** 0.137*** 0.266***
Health And Medical Preparatory Program 0.481*** 0.496*** 0.306*** 0.190**
Nursing 0.391*** 0.408*** 0.172*** 0.243***
Pharmacy Pharmaceutical Sciences And A 0.641*** 0.626*** 0.253*** 0.406***
Treatment Therapy Professions 0.208*** 0.220*** 0.101*** 0.095**
Community And Public Health 0.151*** 0.226*** 0.030 0.076
Miscellaneous Health Medical Professio -0.042 0.083 -0.035 0.078
General Business 0.218*** 0.339*** 0.077*** 0.142***
Accounting 0.310*** 0.431*** 0.143*** 0.199***
Actuarial Science 0.632*** 0.764*** 0.160 0.337***
Business Management And Administration 0.199*** 0.292*** 0.054*** 0.104***
Operations Logistics And E-Commerce 0.350*** 0.403*** 0.169*** 0.181***
Business Economics 0.432*** 0.458*** 0.273*** 0.204***
Marketing And Marketing Research 0.256*** 0.356*** 0.089*** 0.150***
Finance 0.342*** 0.518*** 0.151*** 0.243***
Human Resources And Personnel Manageme 0.203*** 0.258*** 0.037 0.064*
International Business 0.284*** 0.398*** 0.090** 0.149***
Hospitality Management 0.093** 0.095** 0.049 0.060
Management Information Systems And Sta 0.406*** 0.485*** 0.152*** 0.223***
Miscellaneous Business & Medical Admin 0.063 0.363*** -0.037 0.192***
History 0.105*** 0.167*** 0.033* 0.064***
United States History 0.090 0.127 -0.061 -0.031
Constant 2.217*** 2.214*** 2.292*** 2.235***
R20.200 0.217 0.330 0.337
N 125794 140706 124858 139493
Notes: *** p<0.01, ** p<0.05, * p<0.10
Bachelor’s degrees are 4-digit. Wages are top- and bottom-coded at 5 and 400 USD per
hour, respectively. General Education is the excluded category. Occupation controls
are 5-digit; coefficients not shown.
Sample selection: Observations are included if the individual has at least a bachelor’s
degree, is working >34 hours per week and >40 weeks per year, and is 23-59 years old.
8
Supplementary Table 4: Proportion of people in top three occupations by major
Major Age 25-29 Age 40-44 Age 55-59
Top 3 occupations %age Top 3 occupations %age Top 3 occupations %age
Comm.
Marketing and Sales Managers
11.0
Elem. & Middle School Teachers
15.0
Miscellaneous Managers
14.5Secretaries and Admin. Assts Marketing and Sales Managers Writers and Editors
Human Resources, Training, and
Labor Relations Specialists
Miscellaneous Managers Chief Executives and Legislators
Comp. Sci.
Computer Software Engineers
51.7
Computer Software Engineers
37.4
Computer Software Engineers
42.8Computer Programmers Computer Programmers Computer Programmers
Computer Scientists and Systems
Analysts
Computer Scientists and Systems
Analysts
Computer Scientists and Systems
Analysts
Elem. Ed.
Elem. & Middle School Teachers
77.3
Elem. & Middle School Teachers
65.0
Elem. & Middle School Teachers
56.0Preschool and Kindergarten Teachers Preschool and Kindergarten Teachers Education Administrators
Special Education Teachers Education Administrators Preschool and Kindergarten Teachers
Elec. Eng.
Electrical and Electronics Engineers
39.3
Computer Software Engineers
29.5
Electrical and Electronics Engineers
29.7Computer Software Engineers Electrical and Electronics Engineers Petroleum, Mining, Geol.Engineers
Petroleum, Mining, Geol. Engineers Petroleum, Mining, Geol. Engineers Miscellaneous Managers
Biology
Physicians and Surgeons
19.4
Physicians and Surgeons
29.2
Physicians and Surgeons
26.5Postsecondary Teachers Dentists Postsecondary Teachers
Clinical Laboratory Techs Postsecondary Teachers Miscellaneous Managers
Chem.
Postsecondary Teachers
37.8
Physicians and Surgeons
31.1
Physicians and Surgeons
23.4Chemists and Materials Scientists Miscellaneous Managers Chemists and Materials Scientists
Physicians and Surgeons Chemists and Materials Scientists Postsecondary Teachers
Psych.
Social Workers
20.5
Counselors
17.2
Psychologists
15.8Counselors Social Workers Counselors
Elem. & Middle School Teachers Elem. & Middle School Teachers Social Workers
Econ.
Accountants and Auditors
18.7
Miscellaneous Managers
19.0
Lawyers, Judges, Magistrates, etc.
21.7Financial Analysts and Advisors Financial Managers Miscellaneous Managers
Lawyers, Judges, Magistrates, etc. Lawyers, Judges, Magistrates, etc. Accountants and Auditors
Poli. Sci. &
Govt.
Lawyers, Judges, Magistrates, etc.
20.6
Lawyers, Judges, Magistrates, etc.
25.5
Lawyers, Judges, Magistrates, etc.
28.8Elem. & Middle School Teachers Miscellaneous Managers Miscellaneous Managers
Miscellaneous Legal Support Workers Elem. & Middle School Teachers Chief Executives and Legislators
Fine Arts
Designers
21.9
Designers
25.6
Elem. & Middle School Teachers
19.2Waiters and Waitresses Elem. & Middle School Teachers Artists and Related Workers
Retail Salespersons Artists and Related Workers Designers
Nursing
Registered Nurses
89.8
Registered Nurses
83.4
Registered Nurses
79.4Nursing, Psych, Home Health Aides Med. & Health Services Managers Med. & Health Services Managers
Licensed Practical and Licensed
Vocational Nurses
Nursing, Psych, Home Health Aides Postsecondary Teachers
Acct.
Accountants and Auditors
70.2
Accountants and Auditors
52.9
Accountants and Auditors
50.9Financial Managers Financial Managers Financial Managers
Bookkeeping, Accounting, and
Auditing Clerks
Miscellaneous Managers Chief Executives and Legislators
Business
Mgmt. &
Admin.
Accountants and Auditors
18.9
Accountants and Auditors
17.5
Miscellaneous Managers
17.0Supervisors/Managers, Sales Workers Supervisors/Managers, Sales Workers Accountants and Auditors
Miscellaneous Managers Miscellaneous Managers Supervisors/Managers, Sales Workers
History
Elem. & Middle School Teachers
20.2
Lawyers, Judges, Magistrates, etc.
23.1
Lawyers, Judges, Magistrates, etc.
22.8Secondary School Teachers Elem. & Middle School Teachers Elem. & Middle School Teachers
Lawyers, Judges, Magistrates, etc. Miscellaneous Managers Postsecondary Teachers
Note: Occupations are 5-digit; only a selected sample shown.
9
Supplementary Table 5: Proportion of people in top three majors by occupation
Major Age 25-29 Age 40-44 Age 55-59
Top 3 majors %age Top 3 majors %age Top 3 majors %age
Chief Executives and
Legislators
Business Mgmt. & Admin.
27.8
General Business
27.0
Business Mgmt. & Admin.
24.9General Business Business Mgmt. & Admin. General Business
Economics Accounting Accounting
Miscellaneous
Managers, Including
Postmasters and Mail
Business Mgmt. & Admin.
21.5
Business Mgmt. & Admin.
19.7
Business Mgmt. & Admin.
19.0General Business General Business General Business
Marketing And Marketing Research Electrical Engineering Biology
Lawyers, and Judges,
Magistrates, and
Other Judicial
Workers
Political Science And Government
36.4
Political Science And Government
38.8
Political Science And Government
40.3English Language And Literature History History
History English Language And Literature English Language And Literature
Marketing and Sales
Managers
Marketing And Marketing Research
41.1
Marketing And Marketing Research
32.1
Business Mgmt. & Admin.
28.5Business Mgmt. & Admin. Business Mgmt. & Admin. Marketing And Marketing Research
Communications General Business General Business
Financial Managers
Finance
41.5
Accounting
46.1
Accounting
45.4Business Mgmt. & Admin. Business Mgmt. & Admin. Business Mgmt. & Admin.
General Business Finance General Business
Education
Administrators
Psychology
20.2
General Education
24.3
General Education
28.9General Education Elementary Education Elementary Education
English Language And Literature Psychology English Language And Literature
Accountants and
Auditors
Accounting
69.1
Accounting
72.1
Accounting
65.8Finance Business Mgmt. & Admin. Business Mgmt. & Admin.
Business Mgmt. & Admin. Finance General Business
Computer Software
Engineers
Computer Science
63.7
Computer Science
45.4
Computer Science
41.7Computer Engineering Electrical Engineering Mathematics
Electrical Engineering Computer Engineering Electrical Engineering
Social Workers
Psychology
59.6
Social Work
59.3
Social Work
52.9Social Work Psychology Psychology
Sociology Sociology Sociology
Postsecondary
Teachers
Biology
20.4
English Language And Literature
20.9
English Language And Literature
19.2Psychology Biology Nursing
English Language And Literature Psychology Biology
Elementary and
Middle School
Teachers
Elementary Education
44.5
Elementary Education
45.2
Elementary Education
50.7General Education General Education General Education
Early Childhood Education English Language And Literature Special Needs Education
Secondary School
Teachers
General Education
26.8
General Education
24.4
General Education
26.2English Language And Literature English Language And Literature Secondary Teacher Education
Art And Music Education Physical And Health Education
Teaching
Elementary Education
Physicians and
Surgeons
Biology
53.2
Biology
53.0
Biology
53.3Biochemical Sciences Chemistry Chemistry
Psychology Biochemical Sciences Health & Med. Prep. Progs.
Registered Nurses
Nursing
86.2
Nursing
82.9
Nursing
81.9Biology Psychology Psychology
Psychology Multi-Disciplinary Or General Science Multi-Disciplinary Or General Science
First-Line
Supervisors/Managers,
Sales Workers
Business Mgmt. & Admin.
32.3
Business Mgmt. & Admin.
30.7
Business Mgmt. & Admin.
26.1Marketing And Marketing Research General Business General Business
General Business Marketing And Marketing Research General Education
Sales Representatives,
Wholesale and
Manufacturing
Marketing And Marketing Research
35.4
Business Mgmt. & Admin.
37.4
Business Mgmt. & Admin.
30.7Business Mgmt. & Admin. General Business General Business
General Business Marketing And Marketing Research Marketing And Marketing Research
Note: Occupations are 5-digit; only a selected sample shown.
10
Supplementary Figure 1
5 10 15 20 25 30
Proportion of majors
1970 1980 1990 2000 2010
College graduation year
Education Engineering
Liberal Arts Science
Business and economics
Major trends, men
0 10 20 30 40
Proportion of majors
1970 1980 1990 2000 2010
College graduation year
Education Engineering
Liberal Arts Science
Business and economics
Major trends, women
Selected majors only. Data from ACS.
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Supplementary Figure 2
0 10 20 30
Proportion of majors
1985 1990 1995 2000 2005 2010
College graduation year
Education Engineering
Liberal Arts Science
Business and economics
Major trends, men
0 5 10 15 20 25
Proportion of majors
1985 1990 1995 2000 2005 2010
College graduation year
Education Engineering
Liberal Arts Science
Business and economics
Major trends, women
Selected majors only. Data from IPEDS.
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... Given the clear sequence that students are required to follow [3], the high school math curriculum is arguably the most studied academic trajectory. Analyzing state or national longitudinal transcript data, researchers have tackled the positive role of a specific or the first fundamental math course (e.g., Algebra II) [4,21,22], advanced-level math and science courses [23,24], the highest-level math and science courses [18,25,26], and the ways students follow the course sequence of math and science that later impact their college attendance, college graduation, and economic returns to education [27,28]. Those who start from a more advanced position eventually complete more advanced courses than their counterparts [29,30]. ...
... Whereas existing studies imply that math and science are the most critical subjects in students' future success beyond high school in the US, an examination including other subjects, such as CS, is critical for understanding high school pathways to postsecondary academic outcomes [3,22,23,31]. Situated at the intersection of math, science, and engineering [32], computing-or CS-related courses have been perceived as difficult, complex, and ancillary courses reserved for students with high academic disposition until CS courses become mandatory. Conversely, students' prior programming experience is related to their sense of belonging in computing [33], which may, in turn, motivate students to eventually major in CS in college because students are able to see the relevance of CS to their future careers [34]. ...
... These phenomena imply that CS courses are perceived as extra as opposed to an integral part of student's coursework when such courses are not required for high school graduation. Without the external motivator, teachers, parents, and students themselves may put more emphasis on math and science than CS courses because of the direct linkage to college readiness [4,6,18,[21][22][23] or the clear sequences during advising [27][28][29]. This low participation and return rate of CS courses may hinder students' interest in CS fields in college since the accumulation of learning experience is key to a greater aspiration to a particular field [34,43]. ...
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Whereas researchers regard high school math and science coursework as the best indicator of college readiness for students in the United States, computer science coursework and its relationship to college attendance, particularly for minoritized students, have not received due attention despite its root in the mathematical and scientific reasoning ability. We examined students’ high school course completion patterns across subjects and grade levels with a special focus on elective computer science courses and whether the coursework pattern transitions worked differently for minoritized students in Texas, USA. Latent profile analysis and latent transition analysis revealed multiple patterns of coursework, including Regular, Trailing, and Computer Science-Intensive. However, high school students seemed to attempt computer science courses with an experimental attitude. High school girls, low-income, and Latinx and African American students were less likely to complete computer science courses, despite demonstrating a similar coursework pattern in the previous year. Similarly, students with limited English proficiency, those eligible for free- or reduced-price lunch programs, and Native American students systematically have a lower chance to attend college, despite sufficient academic preparation in high school. Findings highlight the challenges minoritized students face and how students approach elective computer science courses in high school.
... Research suggests that many students base their decision on college majors on assumptions rather than facts [9,10]. Students' inability to successfully select what and where to study may greatly impact returns of higher education [10], and the labor market [11]. Thus, understanding how students choose their majors and what influences them will allow policymakers to define appropriate measures and incentives for labor supply adjustments based on market needs and other strategic goals [12]. ...
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This study aims to understand and analyze what influences female students to choose a college major in the United Arab Emirates (UAE). To accomplish our target, we conducted a survey with mostly female first-year undergraduate students (N = 496) at Zayed University to understand the personal, social, and financial factors influencing students’ major choices. Further, this study also asked students to specify their actions before deciding on their major and assessed the information that could be helpful for future students to decide on their majors. Last, the study investigated how Science, Technology, Engineering, and Mathematics (STEM) students differ from other students in their major decision. The results show that financial factors such as income and business opportunities related to the major are crucial. Further, gender suitability for the job and passion are influential. Students conduct internet searches, use social media, and read brochures in the process of major decisions. Moreover, students think job alignment with the UAE vision and information related to job availability, income, and skills are critical for future students to decide on their major. Finally, STEM students are more influenced by business opportunities, prestige, and career advancement than others.
... I expect the presence of colleges in the county of residence to primarily influence education by encouraging students to attend a nearby college through a cost reduction (both pecuniary and psychic) mechanism. If this sort of cost reduction is more important for students inclined toward particular types of college education, such as majors (Altonji, Blom, and Meghir, 2012) or quality (Black and Smith, 2006), then this IV will identify the effects of such types of education, and may violate uniform treatment responses if individuals prefer nearby colleges of an otherwise non-preferred types to distant colleges of more-preferred types. ...
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Many treatment variables used in empirical applications nest multiple unobserved versions of a treatment. I show that instrumental variable (IV) estimands for the effect of a composite treatment are IV-specific weighted averages of effects of unobserved component treatments. Differences between IVs in unobserved component compliance produce differences in IV estimands even without treatment effect heterogeneity. I describe a monotonicity condition under which IV estimands are positively-weighted averages of unobserved component treatment effects. Next, I develop a method that allows instruments that violate this condition to contribute to estimation of treatment effects by allowing them to place nonconvex, outcome-invariant weights on unobserved component treatments across multiple outcomes. Finally, I apply the method to estimate returns to college, finding wage returns that range from 7\% to 30\% over the life cycle. My findings emphasize the importance of leveraging instrumental variables that do not shift individuals between versions of treatment, as well as the importance of policies that encourage students to attend "high-return college" in addition to those that encourage "high-return students" to attend college.
... With improving access to education to a large section of society, the years of education can't determine the labour market outcomes effectively, rather specialisation becomes an important factor (Altonji, 2015). Studies indicate that the earning gap across colleges major is notably high (Altonji et al., 2012) and increasing (Altonji et al., 2014;Gemici & Wiswall, 2014) over time. ...
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This study examines the access of students from diverse backgrounds to medical education in India. It shows how inequalities existing in society may entail significant social injustices with regard to access to a career in medicine. The study is based on data from secondary sources. The major part of the analysis is from the Periodic Labour Force Survey, 2019–20; All India Survey on Higher Education, 2019–20; and National Sample Survey data on Social Consumption, Education 2017–18. It is observed that the availability of health professionals is very low overall but it is even lower among underprivileged groups. There are indications of a better share of salaried health professionals among underprivileged caste/ethnic groups probably due to the presence of affirmative action but inequality prevails in self-employment and high quality occupations, thus reflecting the inequality prevalent in society. However, the pattern among Muslims is different from the caste/ethnic groups as the share of regular salaried workers is lower and self-employed is higher among Muslims. The study shows that access to medical courses is linked to family background depicted by caste/ethnicity and religious identities. The availability of medical education in general is very low. The situation is further aggravated for students from underprivileged backgrounds. The high cost of medical courses combined with the dominance of self-financed courses and private unaided institutions may make it inaccessible to students from weaker sections of society. In fact, the probability of attending a medical course is relatively lower for Scheduled Castes/Scheduled Tribes (SCs/STs) and Muslims than Hindu High Castes (HHCs). The low average expenditure of medical courses confirms the low quality of education accessed by the student from underprivileged backgrounds at every level. It is important to note that education of the head of the family emerges as the most important predictor for access to medicine education. Similarly low household size also improves the probability of attendance. It is thus important to improve the access to medical education through establishing new educational institutions with affordable costs. The challenge is to ensure equal access for students from underprivileged groups so that the existing inequality in the availability of health professionals may be addressed. For this, affirmative action for the students from poor families and first generation learners may be worthwhile to address the problem of inequality of access to medical education. Such policies would also improve the availability of health professionals from the underprivileged socio-religious background which in turn would play an instrumental role in ensuring better access to healthcare services for patients from underprivileged communities.
... 3 Throughout the paper, we use the term bias to describe the difference between two population quantities, namely the IV estimand and the parameter of interest, that is the positively weighted average of treatment effects for some complier group. 4 There is a growing body of work on the payoffs to field of study or college major, reviewed in Altonji et al. (2012Altonji et al. ( , 2016, and Kirkeboen et al. (2016). The latter study also reports IV estimates of the payoffs to fields of study from Norway. ...
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We study how high school majors affect adult earnings using a regression discontinuity design. In Sweden students are admitted to majors in tenth grade based on their preference rankings and ninth grade GPA. We find engineering, natural science, and business majors yield higher earnings than social science and humanities, with major-specific returns also varying based on next-best alternatives. There is either a zero or a negative return to completing an academic program for students with a second-best nonacademic major. Most of the differences in adult earnings can be attributed to differences in occupation, and to a lesser extent, college major. (JEL I21, I26, J24, J31)
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Women continue to be disproportionately underrepresented in science and engineering fields. A model for career choice is proposed that includes both the direct and indirect effects that socializers can play in determining career choices. A sample of 2213 high school seniors from nine schools in Rhode Island were surveyed about their academic and career choices and the perceived influences on those choices. Parents and teachers were perceived to be influences on career choice more often for students (both men and women) choosing careers in engineering and science than for those not choosing such careers. Pay was a more important factor in career choice for men in general, and genuine interest was a more important factor for women not choosing careers in engineering or science. However, these gender differences do not appear among students with extremely strong mathematics and science coursework backgrounds, even though there remains a marked disparity in the proportion of men to women planning careers in engineering or science. Teachers may play a particularly important role in influencing the career choice of some of these women. Equity of access and encouragement in mathematics and science is certainly a necessary, but insufficient, condition for improving the representation of women in science and engineering.
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This paper studies the labor market experiences of white-male college graduates as a function of economic conditions at time of college graduation. I use the National Longitudinal Survey of Youth whose respondents graduated from college between 1979 and 1989. I estimate the effects of both national and state economic conditions at time of college graduation on labor market outcomes for the first two decades of a career. Because timing and location of college graduation could potentially be affected by economic conditions, I also instrument for the college unemployment rate using year of birth (state of residence at an early age for the state analysis). I find large, negative wage effects of graduating in a worse economy which persist for the entire period studied. I also find that cohorts who graduate in worse national economies are in lower-level occupations, have slightly higher tenure and higher educational attainment, while labor supply is unaffected. Taken as a whole, the results suggest that the labor market consequences of graduating from college in a bad economy are large, negative and persistent.
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This paper provides structural estimates of a dynamic model of schooling, work, and occupational choice decisions based on 11 years of observations on a sample of young men from the 1979 youth cohort of the National Longitudinal Surveys of Labor Market Experience (NLSY). The structural estimation framework that we adopt fully imposes the restrictions of the theory and permits an investigation of whether such a theoretically restricted model can succeed in quantitatively fitting the observed data patterns. We find that a suitably extended human capital investment model can in fact do an excellent job of fitting observed data on school attendance, work, occupational choices, and wages in the NLSY data on young men and also produces reasonable forecasts of future work decisions and wage patterns.
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Within the arts, sciences, and engineering fields, differences between men and women in choice of college major have not lessened in the past two decades. In this paper, detailed data on choice of major and individual scores on the Scholastic Aptitude Test (SAT) are used to examine the extent to which observed differences between men and women reflect the effects of pre-collegiate preparation (as reflected in SAT scores), as contrasted with a panoply of other forces. One conclusion is that there is a widening divide between the life sciences and math/physical science fields in their relative attractiveness to men and women. Differences in SAT scores account for only part of the observed gap, and an array of residual forces - including differences in preferences, labor market expectations, and gender-specific effects of the college experience - account for the main part of today's gender gaps in choice of academic major.
Chapter
The literature on the determinants of earnings suggest an earnings function for individual i which depends on age ai, year t, “vintage” or “cohort” schooling level si, and experience ei. Adopting a linear function to facilitate exposition we may write $${Y_i}(t,{a_i},{c_i},{e_i},{s_i}) = {\alpha _0} + {\alpha _1}{a_i} + {\alpha _2}t + {\alpha _3}{e_i} + {\alpha _4}{s_i} + {\alpha _5}{c_i}$$ (1) where ei is experience, usually defined for males as age minus schooling, (ei = ai – si),1 and Yi may be any monotone transformation of earnings.
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This study traced the development of gender differences in learning opportunities, achievement, and choice in mathematics among White, African American, and Latino students using data from a nationally representative sample of eighth-grade students who were resurveyed in the 10th grade. It found that in this age group, female students do not lag behind male students in test scores and grades and that White female students are exposed to more learning opportunities in mathematics than are male students. However, all female students tend to have less interest in mathematics and less confidence in their mathematics abilities. Gender differences are the largest among Latinos and the smallest among African Americans. Furthermore, the major barriers to mathematics achievement for White female students are attitudes and career choices and for minority students of both sexes, they are limited learning opportunities and low levels of achievement.