Pathways Linking Education to Midlife Correlates of AD: Using Longitudinal Data

Chandra Muller

Department of Sociology & Population Research Center University of Texas at Austin

Overview

• Explanations for education gradient in health and the role(s) of schools

• What do schools do? • What are high quality schools? • How are schools organized?

• Evidence for school effects • School effects over the life course • Implications of school effects and the changing economy

Supplemental slides

Explanations for the education gradient in health & aging • Selection: Schools sort students according to background

• Selection determines educational achievement & attainment • Education serves as a credential to reproduce social inequality

• School Effects: Schools provide access to opportunities that change people and impact achievement & attainment

• Teach skills—cognitive and non-cognitive • Impart knowledge and information • Provide social networks and peers to reinforce or disrupt learning

• Teach students how to engage in lifelong learning, enabling individuals to

engage with other institutions over the life course

Opportunities to Learn in Formal and Informal Institutions over the Life Course

Pre-school

K-12 Higher Educ

Work & Occupation Lifestyle, Activities, Relationships in Civic, Religious, Community

Cognition (& correlates) at Older Age

NEIGHBORHOOD

FAMILY

Individual

What do effective schools do? Provide academically rich learning opportunities for students

Provide & foster: • Academically rigorous courses • Developmentally appropriate curriculum that builds toward

developing advanced skills and knowledge • Social climate to motivate students to engage in learning • Teachers, administrators and staff who are knowledgeable

and take students’ needs into account

Evidence of School Effects • Pre-school

• Results from experiments (e.g., Perry Pre-school) • Debate over “fade-out”

• Kindergarten through High School (partial list of evidence) • Summer set back • Catholic schools and “academic press” • Academic achievement (test scores, grades, coursework) • Class size (e.g., Tennessee Star) • Compulsory school laws • Debate over heterogeneous effects

• Higher Education • Four year college degree (bachelor’s degree or more) • Vocational college

How are schools organized?

• Age graded—as students age: • Schools may get larger, less

neighborhood based • Greater complexity of curriculum and

social dynamics • More within school stratification • More structured evaluation of

success and failure • Compulsory through high school • High school to college is crucial

transition for lifelong stratification • High school courses, esp math,

determine success in higher ed

Pre-school Kinder-12

Higher

Education

Early Childhood Childhood & Adolescence

Early Adulthood

High School Academic Organization: Courses

Algebra II Advanced Science

Foreign Language

Honors English

Honors Social

Studies

General Math

General Science

Foreign Language

Basic English

Basic Social

Studies -100%

-80%

-60%

-40%

-20%

0%

20%

40%

60%

80%

100%

1982 1990 1994 1998 2000 2005 2009

Bel

ow S

tand

ard

- At o

r Abo

ve S

tand

ard

High School Graduation Year

Below Standard Standard Midlevel RigorousSource: J. M. Carroll & C. Muller. Forthcoming. “Curricular Differentiation and Its Impact on Different Status Groups Including Immigrants and Students with Disabilities.” in Handbook of Sociology of Education.

Trends in high school curriculum levels taken by high school graduates from 1982 to 2009

High School Mathematics

Highest Level Mathematics Course Taken by the End of High School (%), HS&B, Class of 1982

28.7

20 14

22.7

14.7

05

101520253035

Percentage of Students Taking No Math Course

0%

10%

20%

30%

40%

50%

60%

1982 1992 2004 2013

HSB NELS ELS HSLS

Did NotTake MathSenior Year

Didn't TakeMath Junioror SeniorYear

High School Math Coursework and Early Adult Labor Force Outcomes

• A natural experiment in Denmark, Joensen and Nielsen (2009). Advanced math coursework ->

30% higher incomes in early adulthood. The most likely mechanism is development of knowledge and skills and it operates partially (but not entirely) through higher education.

• HS&B, Rose and Betts (2001, 2004) Advanced math coursework -> higher wages at age 27. An important threshold in determining higher wages was whether or not students completed Algebra 1 and geometry by the end of high school. Math coursework explained the wage gap by parents’ SES.

• NLSY97, Levine and Zimmerman (1995) Advanced math coursework -> technology occupations and higher wages.

• 1958 British Cohort Study, Dolton and Vignoles (2002) and 1970 British Cohort Study, Adkins and Noyes (2016) Advanced math coursework -> higher earnings at age 33. A threshold is Algebra.

High School Coursework and Midlife Outcomes (early results from High School & Beyond Midlife Follow-up)

• Labor market • Advanced math and science predict

• Occupational mean wage (one decile higher) • Likelihood of holding STEM occupation (even without a college degree) • Who successfully adapts to labor market changes (advancing in an occupation between early

adulthood and midlife, net of early occupation) • Low level math predicts:

• Unemployment after holding a bad job • Holding a precarious occupation (low wage (women) or occupation with high unemployment

(men))

• Health status & physical functioning • Elite high school coursework & selective college predicts not being obese • Advanced math predicts being in very good or excellent health • Low level math predicts:

• Lower physical functioning • Disability • Mortality

Workforce Polarization: Smoothed Changes in Employment Shares for 318 Occupations (excludes agriculture), 1980-2008, by 1980 Percentile Rank of Occupation Mean Wage

Source: “On the Origins of STEM: High School Knowledge and Skills and Occupational Determination in an Era of Growing Inequality.” By S.E. Black, C. Muller, A. Spitz-Oener, Z. He, K. Hung, J.R. Warren

Curve is from Acemoglu and Autor (2011) Red circles (radius) represent number of STEM and STEM-related occupations for each wage percentile

Decline in jobs

Job growth

Low skill High skill

Implications of Workforce Polarization for Cognitive Aging • To the extent that jobs offer individuals opportunities to learn and

continue to grow intellectually, the greater polarization of the workforce means that a very large share of the mid-adult workforce will be in low-skill jobs that offer fewer opportunities for cognitive growth.

• Job polarization is related to resources, safety nets, opportunity through

• Access to other societal institutions • Spatial stratification

Conclusions

• A substantial body of research has established effects of schools on • Early adult outcomes

• More limited research on long run effects on • Ability to adapt to societal changes • Individual’s interactions with formal institutions (e.g., work/employment,

health care)

• Society is becoming increasingly stratified (economically polarized) and segregated, which could exacerbate long run effects of education on correlates of AD

Collaborators Co-Investigators • Sandy Black, Economics, UT

Austin • Eric Grodsky, Sociology, UW

Madison • Rob Warren, Sociology, UMN

Twin Cities

Others • Amanda Bosky, UT (sociology) • Jamie Carroll, UT (sociology) • Michelle Frisco, PhD, Penn State • Ziwei He, UT (economics) • Koit Hung, UT (sociology) • Eve Pattison, UT (sociology) • Robert Reynolds, PhD, UT • April Sutton, PhD, UCSD

Funding

• We are grateful for the generous support of the High School and Beyond Midlife Follow-up project. This material is based upon work supported by the Alfred P. Sloan Foundation under grant number 2012-10-27, the National Science Foundation under grant numbers HRD 1348527, HRD 1348557, DRL 1420691, DRL 1420330, & DRL 1420572, & the Institute for Education Sciences of the U.S. Department of Education under grant number R305U140001. This research was also supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development under grants numbers 5 R24 HD042849 & 5 T32 HD007081 (Training Program in Population Studies). Thank you to the Russell Sage Foundation for the visiting scholar fellowship.

• Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or other funders.

Supplementary Slides

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from the High School and Beyond Cohort.” University of Minnesota, Minneapolis, MN

High School and Beyond (sophomores): The Class of 1982

1980 1982 1984 1992 1986 2014

Unemployment=7.8% (1990) Unemployment=6.3%

(2001)

Unemployment=10% (2008)

? Computerization

of Workplace

*Class of 1980 (seniors) interviewed 1980-86, 2015

Unemployment=10.8% (July 1981)

Migration: Where do they live then and now?

Then: 1980 Now: 2014

10

20

30

40

50 60

70

80

90

100

No sample

Decile

Labor Force: 2013 Occupation Wage Percentile OLS regression coefficients All models include locus of control, number of mathematics credits, science credits, and foreign language credits & background characteristics (race/ethnicity, gender, age, mother’s education, and number of siblings), 1992 educational attainment

Adaptation Models Highest math course (reference=below Alg 1)

Algebra 1 3.301* 1.450 1.236 2.585 Geometry 5.567*** 4.185* 3.883* 4.666** Algebra 2 5.609*** 4.214* 3.710 4.952** Adv math/Calculus 8.589*** 7.809*** 6.608** 7.861***

Highest science course (reference=below Bio) Biology 2.111 2.387 2.857 2.153 Chemistry 1.611 3.007 3.442 1.678 Physics 3.980* 2.949 2.622 2.321 Advanced science 2.664 1.392 1.362 2.321

Math test score 3.223*** 3.810*** 3.641*** 3.165*** STEM occup 1991 11.40*** HS Fixed effect No Yes Yes No STEM 1991=0 No No No Yes R2 0.229 0.380 0.391 0.205 N 7,240 6,870 6,870 6,330

Source: “On the Origins of STEM: High School Knowledge and Skills and Occupational Determination in an Era of Growing Inequality.” By S.E. Black, C. Muller, A. Spitz-Oener, Z. He, K. Hung, J.R. Warren

Labor Force: 2013 Low-wage & High Unemployment Occupation--AMEs

Low Wage Occupation

High Unemployment Rate Occupation High/Aver $20/hr+

Low <$20/hr Unemployed

Low/Aver unemp

High unemp Unemployed

Highest math course (reference <Algebra 1)

Alg 1 0.068** -0.013 -0.027* 0.080*** -0.024 -0.028* Geometry 0.086** -0.024 -0.027 0.099*** -0.037 -0.027 Alg 2 0.091*** -0.048 -0.012 0.100*** -0.059** -0.011 Advanced Math + 0.106*** -0.067* -0.016 0.094** -0.051* -0.018

Math test score 0.055*** -0.027* -0.010 0.034** -0.005 -0.010

N=8,520 Outcome categories not shown are disability & out of labor force/homemaker All models include background characteristics (race/ethnicity, gender, age, parents’ education, and family structure, disability in adolescence), locus of control, science and foreign language coursework, GPA, public/private high school, urbanicity, 1992 educational attainment, 1991 unemployment

Source: “Adapting in a Changing Economy: High School Preparation and Long-Run Labor Force Outcomes” By A. Bosky & C. Muller

Health: 2013 Health Status & Physical Functioning

Health Status Physical Functioning Course-taking Pattern (reference all low-level courses)

Mixed Low-Level Courses .105* .084~ .135 .105 All Medium-Level Courses .141* .088 .263** .193* Mixed High-Level Courses .191*** .137* .137 .065 All High-Level Courses .221** .141* .208* .127

Locus of Control .084** .175*** .079 .200** Test Scores -.002 -.006** .003 -.001 GPA .078** .084** .110* .089~ Degree Attainment No Yes No Yes R2 .100 .116 .104 .126 N 6850 2990

*** p<.001, ** p<.01, * p<.05, ~ p<.1

OLS regression coefficients All models include background characteristics (race/ethnicity, gender, age, parents’ education, family income, father’s occupation, home ownership, number of siblings and family structure), adolescent health, high school drop out status, school characteristics. Models with degree attainment also include change in locus of control, test scores and grades from sophomore to senior year.

Source: “Tracking Health Inequalities from High School to Midlife.” By J. Carroll, C. Muller, E. Grodsky, and J.R. Warren.

Health: Mortality

Math Coursework (reference is <Algebra I) Algebra I 0.59** 0.65* Geometry 0.61* 0.77 Algebra II 0.41** 0.58* >Algebra II 0.41** 0.68

Educational Attainment (ref= HS degree, no college) Did Not Complete High School 1.25 Some College, No Degree 0.93 Bachelor's Degree or More 0.49*

N=13,040

Odds Ratios Both models include background characteristics (race/ethnicity, gender, age, parents’ education, father’s occupation, parents’ home ownership, number of siblings and family structure), adolescent health. Model with educational attainment also includes science courses, locus of control, self-concept, test scores, grades, school effort, and peer characteristics.

Source: “Which Aspects of Education Matter for Early Adult Mortality? Evidence from the High School & Beyond Cohort.” By J.R. Warren, C. Muller, R.A. Hummer, E. Grodsky, and M. Humphries.

School Processes –Degree

Preparation: Coursework Achievement Test Scores Grades Non-Cognitive Skills

Academic Degree

Health

Childhood & Adolescence Early Adulthood Midlife & Older Adulthood

Health

Selection Effects Go Beyond Family Background

Course-taking by Adolescent Weight Status Source: High School and Beyond

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

General Algebra 1 Geometry Algebra 2 Advanced

Healthy Weight Overweight

Course-taking by Adolescent Depressive Symptoms Source: High School and Beyond

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

General Algebra 1 Geometry Algebra 2 Advanced

Never Once More than once

Course-taking by Adolescent Disability Status Source: High School and Beyond

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

General Algebra 1 Geometry Algebra 2 Advanced

No Disability Physical Disability Mental Disability

College Preparatory Coursework End of High School for Students with a Learning Disability

Source: Education Longitudinal Study of 2002

0.00

0.20

0.40

0.60

0.80

1.00

Geometry orhigher

2 of 3 mainsciences

1 cr. foreignlanguage

All CollegePreparatory

Non-LD LD