Schooling, Skills, and Self-rated Health · Schooling, Skills, and Self-rated Health: ATest of ......

Schooling, Skills, and Self-rated Health: A Test ofConventional Wisdom on theRelationship betweenEducational Attainment andHealth

Naomi Duke1 and Ross Macmillan2

Abstract

Education is a key sociological variable in the explanation of health and health disparities. Conventionalwisdom emphasizes a life course–human capital perspective with expectations of causal effects that arequasi-linear, large in magnitude for high levels of educational attainment, and reasonably robust in the faceof measured and unmeasured explanatory factors. We challenge this wisdom by offering an alternative the-oretical account and an empirical investigation organized around the role of measured and unmeasured cog-nitive and noncognitive skills as confounders in the association between educational attainment and health.Based on longitudinal data from the National Longitudinal Survey of Youth-1997 spanning mid-adolescencethrough early adulthood, results indicate that (1) effects of educational attainment are vulnerable to issues ofomitted variable bias, (2) measured indicators of cognitive and noncognitive skills account for a significantproportion of the traditionally observed effect of educational attainment, (3) such skills have effects largerthan that of even the highest levels of educational attainment when appropriate controls for unmeasuredheterogeneity are incorporated, and (4) models that most stringently control for such time-stable abilitiesshow little evidence of a substantive association between educational attainment and health.

Keywords

cognitive ability, noncognitive skills, health, educational attainment, longitudinal

The relationship between education and health has

been a centerpiece of sociological study for over

a century. Theories of the socioeconomics of health

routinely incorporate educational attainment as

a central feature and posit myriad ways in which

people with more education have better health

(e.g., Adler et al. 1994; Cutler, Huang, and

Lleras-Muney 2015; Deaton 2001; Fuchs 2011;

Grossman 1972; Hayward, Hummer, and Sasson

2015; Link and Phelan 1995; Mackenbach et al.

2015; Marmot 2005; Palloni 2006). Research

across time and across the globe routinely finds

that individuals with higher educational attainment

report better health (Ross and Wu 1995), have

lower risk of chronic disease (Winkleby et al.

1992), have lower risk of a variety of cancers

(Jemal et al. 2008), and live longer (Rogers et al.

2010). Equally profound, education was recently

1University of Minnesota, Minneapolis, MN, USA2Universita Bocconi, Milano, Italy

Corresponding Author:

Ross Macmillan, Universita Bocconi, Via Roentgen, 1,

Milano, 20136, Italy.

Email: [email protected]

Sociology of Education2016, 89(3) 171–206

� American Sociological Association 2016DOI: 10.1177/0038040716653168

http://soe.sagepub.com

Research Article

at ASA - American Sociological Association on June 23, 2016soe.sagepub.comDownloaded from

http://soe.sagepub.com/

singled out as one of a limited number of ‘‘social’’

causes for excess mortality in the United States,

where low education was estimated to produce

approximately 245,000 deaths in 2000 alone (Galea

et al. 2011). As such, education is often described

as a ‘‘fundamental cause’’ of health (e.g., Masters,

Link, and Phelan 2015), and important public pol-

icy statements place ‘‘education policy’’ as an

important element of ‘‘health policy’’ (Conti, Heck-

man, and Urzua 2010; Schoeni et al. 2008).

Recent research, however, questions whether

the effects of educational attainment on health

are indeed causal. Referencing long-standing con-

cerns about unobserved heterogeneity and omitted

variable bias, researchers have used natural

experiments, in which changes in law or social

policy effectively randomize exposure to more

education, or have studied twins who share genetic

and family environment factors. Such work pro-

vides a much more equivocal view of the associa-

tion between educational attainment and health.

Studies of changes in compulsory education laws

show both positive effects for health (Lillard and

Molloy 2010; Lleras-Muney 2005; Oreopoulos

2006; Silles 2009; Spasojevic 2003) and null or

inconsistent effects (Albouy and Lequien 2009;

Arendt 2005; Clark and Royer 2010; Fletcher

2015; Gathmann, Jurges, and Reinhold 2015;

Jurges, Kruk, and Reinhold 2013; Mazumder

2008; Powdthavee 2010). Similarly, twin studies

show positive effects of educational attainment

(Behrman, Xiong, and Zhang 2015; Lundborg

2008; Madsen et al. 2010), nonsignificant effects

(Amin, Behrman, and Kohler 2015; Behrman

et al. 2011), and mixed results (Fujiwara and

Kawachi 2009). This work is highly informative

and has good internal validity, but it is largely

empirical, does not actually show the confounding

effect, often requires acceptance of particular

identifying assumptions, and perhaps most impor-

tant, does not offer a compelling theoretical alter-

native to the dominant life course–human capital

explanation of education and health.

Against this backdrop, this study articulates

two ideas about the meaning of education and

reexamines its association with health. The con-

ventional view is a life course–human capital

model, in which educational attainment increases

access to resources and knowledge that translate

into better capability for management of health

dynamics in later life. The key feature of this

model is the role of educational attainment in cog-

nitive transformation that limits engagement in

risk behaviors and promotes factors—such as fam-

ily and peer relationships, work and income, and

placement in general social contexts—that are

conducive to better health (see Link and Phelan

1995; Miech et al. 2011; Mirowsky and Ross

2003; Ross and Wu 1995). An alternative view

builds on other key themes in life course sociology

and emphasizes the cumulative nature of social

life and the importance of age-graded social and

psychological development as a necessary basis

for producing subsequent experiences and

achievements in the unfolding life course (Elder

1994). In the realm of educational attainment,

this perspective highlights early life as a critical

period for the development of cognitive and non-

cognitive skills that are reasonably stable after

childhood, and it describes how these coalesce

into stocks of abilities that are the engines of edu-

cational achievement and attainment over the life

course (e.g., Alexander, Entwisle, and Horsey

1997; Cunha et al. 2006; DiPrete and Eirich

2006; Entwisle, Alexander, and Olson 2005; Far-

kas et al. 1990; Heckman 2006). Such skills are

almost never measured or modeled in health

research, and their absence may distort conclu-

sions about the significance of educational attain-

ment for health, given widely accepted principles

of omitted variable bias. Research that statistically

accounts for such factors, directly or indirectly,

would empirically assess the contributions of cog-

nitive and noncognitive abilities, evaluate causal

claims around education and health in innovative

ways, and advance theoretical understanding in

the sociology of health by adjudicating between

the different perspectives.

We pursue these objectives using data from the

National Longitudinal Survey of Youth-1997.

These data are unique in that the multipanel struc-

ture includes annual measures of educational

attainment and health, which allows estimation

of models that assess the effects of change in edu-

cation on change in health that are the basis of

random- and fixed-effects estimation. Such mod-

els control, in different ways, for time-stable,

unobserved characteristics of individuals that

will confound identification of causal effects on

health. These data also include credible measures

of cognitive and noncognitive abilities that are

critically important in evaluating the meaning of

the education-health relationship. They also allow

us to study a number of time-varying factors that

are widely regarded as proximal determinants of

health (e.g., body mass, smoking, and substance

172 Sociology of Education 89(3)



use) and that provide points of comparison in

assessing the significance, statistical and substan-

tive, of the skill and educational effects, with the

different specifications. Finally, for all analyses,

we assess robustness for six race-sex groups.

EDUCATION AS A CAUSE OFHEALTH: A LIFE COURSE–HUMANCAPITAL PERSPECTIVE

Hegemonic accounts of education and health

stress a causal relationship, in which increases in

educational attainment produce better health out-

comes (Cutler and Lleras-Muney 2008). Here,

education is typically viewed as an engine of

human capital acquisition that has transformative

consequences for a diverse set of psychological

and social behaviors and experiences (for a general

discussion, see Becker 2009). From this perspec-

tive, education increases the stock of competen-

cies, general and specific knowledge, and a variety

of personal and social attributes that increase one’s

ability to function more successfully within mar-

ket and nonmarket environments across the life

course.

A life course–human capital perspective propo-

ses several mechanisms in the causal relationship

between education and health. To start, educa-

tional attainment increases intellectual capability,

which facilitates rational problem solving, critical

thinking skills, and decision making that assist in

medication adherence, reading and understanding

of medical information, and evaluation of com-

plex, new health treatments and medical science

and technology (Cutler and Lleras-Muney 2008).

This has obvious implications for the better navi-

gation of health care systems, identification of

more knowledgeable and skilled medical pro-

viders, and retention of medical information dur-

ing health care encounters (Hummer and Lariscy

2011). In other words, if and when one does get

sick, one is better able to take care of oneself.

Education is also believed to help people avoid

or limit exposure to risky situations, and it imparts

knowledge, motivation, and discipline to adopt

healthy practices (Mirowsky and Ross 1998).

Adoption of a healthy lifestyle, including drinking

in moderation, avoidance of tobacco or smoking

cessation, avoiding recreational drugs, and mainte-

nance of a healthy weight through diet and physi-

cal activity, defends against a broad range of dis-

eases and impairments. Education may also

foster positive and supportive relationships in later

life (Mirowsky and Ross 2003). These practices

assist in coping and dampen the impact of events

and experiences in which self-esteem and percep-

tions of control are threatened (Pearlin and

Schooler 1978). Beyond support, more highly edu-

cated people are also more likely to have highly

educated friends and partners who may in turn

place greater value on health and act more health-

fully (Cutler and Lleras-Muney 2008). This may

result in greater personal accountability and dis-

couragement of negative health behaviors, which

could prevent one from getting sick in the first

place.

As a final mechanism, education facilitates the

acquisition of work that is of personal and practi-

cal value in promoting health (Mirowsky and Ross

2008). More education results in greater likelihood

of work that provides the means to obtain basic

human needs as well as social and psychological

needs, such as belongingness, competence,

achievement, and esteem. Greater educational

attainment further improves labor market experi-

ences, including acquisition of more secure,

more stable, and higher-status jobs that offer better

pay, health insurance, and retirement benefits (J.

R. Reynolds and Ross 1998). Moreover, given

the life course structure of work, educational

attainment can produce a cumulating path toward

better and more advantageous work (Warren,

Sheridan, and Hauser 2002). Educational attain-

ment can thus increase income, assets, and wealth

that facilitate the ‘‘purchase’’ of a variety of

health-enhancing commodities over the life

course.

EDUCATION AS ENDOGENOUS:CRITICAL PERIODS, SKILLS, ANDLIFE COURSE CONTINGENCY INPROCESSES OF EDUCATIONALATTAINMENT

An alternative model of educational attainment,

with contrasting implications for health dynamics,

emphasizes temporality in the life course and the

importance of contingent relationships among

life course experiences and attainments at differ-

ent points in time (Elder 1994). A central concept

in life course social science is the idea of social

trajectories, that is, the institutional pathways indi-

viduals follow—through education, work, and

Duke and Macmillan 173



family—that characterize the sum of their experi-

ences (Elder 1998). A focus on trajectories high-

lights two important elements: life course contin-

gency and critical periods. With life course

contingency, each step in the path to a particular

outcome is dependent on a combination of assets

and vulnerabilities related to experiences, struc-

tural constraints, individual traits, and chance at

earlier points in time (McLeod and Almazan

2003). Within this framework, actions that propel

people over the age span often require personal,

social, and material resources. In short, this per-

spective emphasizes the idea that certain social

and psychological phenomena are necessary or

probabilistic precursors to subsequent experiences

and attainments. This is particularly clear for edu-

cational careers.

Educational institutions are hierarchical and

select on prior achievements to determine

advancement (DiPrete and Eirich 2006). Even in

the earliest years, students need to demonstrate

some level of mastery with respect to cognitive,

psychological, and social skills to warrant (or acti-

vate) promotion (Alexander, Entwisle, and Dauber

1993). Teachers evaluate and identify students’

abilities from the earliest days, and best evidence

suggests this has important implications for a range

of achievements deep into the life course

(Entwisle et al. 1997, 2005). Moreover, with

advancing stages, curricula become more defined

and the requirements for promotion more stan-

dardized and universal, typically with a basis in

internal or external testing. The institutionalization

of education has an added feature of general

stages—primary, secondary, postsecondary, grad-

uate, and postgraduate—that have even more

explicit benchmarking of prior achievements and

additional, targeted testing to determine entrance

into subsequent stages.

In understanding movement through and across

educational institutions, there is increased atten-

tion to the notion of ‘‘skills’’ as the fundamental

driver of educational transitions. Skills can be dif-

ferentiated into cognitive and noncognitive (Car-

neiro, Crawford, and Goodman 2007; Farkas

2003; Heckman 2006). Cognitive skills are typi-

cally conceptualized as generalized abilities to

process information and problem solve; this

includes comprehension, computation, and strate-

gic and goal-oriented applications of rules (e.g.,

algebra, geometry, and calculus). In contrast, non-

cognitive skills embody issues of effort, organiza-

tion, and discipline as well as leadership,

sociability, socioemotional regulation, and ability

to defer gratification.

When one combines life course contingency

and a focus on skills, the importance of critical

periods emerges. Here, the likelihood of given

life stage attainments is heavily dependent on

attainments at earlier stages. In education and

social mobility research, recent work emphasizes

the importance of early skill acquisition and the

necessity of such skills for the progression of edu-

cational careers; childhood and early adolescence

are thus critical periods for skills formation (see

reviews in Heckman 2006; Scott 1962). In a num-

ber of important papers, Alexander and Entwisle

demonstrate the significance of beginning school

transitions for long-term educational stratification.

In summarizing much of their work, they write,

The transition into full-time schooling—

entry into first grade—constitutes a ‘‘critical’’

period for children’s academic development.

First, the early schooling period coincides

with important cognitive changes as children

shift from preoperational to operational

modes of thought. Second, the elementary

years, and especially first grade, constitute

a special time for acquiring the basic skills

of literacy and numeracy, and failing to

acquire skills during this time leads to an

almost insurmountable handicap. (Entwisle

and Alexander 1993:404)

Here, timing is a key issue, and the best evidence

indicates there are critical or sensitive periods

early in children’s development when certain

skills are more readily acquired. Moreover, if

these skills are not developed, subsequent acquisi-

tion becomes difficult. Evidence of this comes

from three bodies of research. First, extensive

work shows significant social differences in cogni-

tive and noncognitive traits at the point of school

entry and the first years of education (Alexander

et al. 1988; Cuhna et al. 2006; Fryer and Levitt

2004; M. R. Gottfredson and Hirschi 1990; Janus

and Duku 2007; Lee and Burkham 2002). Second,

a wealth of evidence suggests substantive stability

in cognitive and noncognitive skills after adoles-

cence. In the former case, studies repeatedly find

test-retest correlations in excess of .6 for IQ

scores, even when measured decades apart (Deary

et al. 2000; Hopkins and Bracht 1975; Kolb and

Whishaw 2008; Ramsden et al. 2011; Schuerger

and Witt 1989). Evidence of stability in




noncognitive skills is less definitive, although

comprehensive reviews suggest reasonable stabil-

ity for self-control (M. R. Gottfredson and Hirschi

1990), personality type (Roberts and DelVecchio

2000), positive and negative affect (Watson and

Walker 1996), and attentiveness and hyperactivity

(Ingram, Hechtman, and Morgenstern 1999)

across the life span. These literatures do not imply

that change does not and cannot happen—associa-

tions are never perfect (Almlund et al. 2011);

rather, change is much more apparent in the earlier

part of the life course than in subsequent periods,

and evidence of stability outweighs that of change.

Finally, a critical period interpretation is supported

by the unfortunate lack of effectiveness of inter-

ventions and changes of environment in adoles-

cence and adulthood. Rutter, Kreppner, and

O’Connor (2001), for example, studied transna-

tional adoptees and found that children who expe-

rienced deprivation into early childhood had much

poorer psychosocial development in later child-

hood and their preteen years, regardless of the

enriched environments to which they relocated

(see also Beckett et al. [2006] on cognitive abili-

ties; M. R. Gottfredson and Hirschi [1990] on

self-control; Newport [2002] on language; and

Spitz [1986] on intelligence). In the realm of inter-

vention, Heckman (2000, 2006) reviews a wide

range of programs delivered at different stages

of the life course and concludes that later interven-

tions show much less evidence of effectiveness.

Taken together, the weight of the evidence sug-

gests that early life is a critical period for the

development of cognitive and noncognitive skills,

and skills acquisition after this period is both dif-

ficult and not the norm.

The combination of critical periods for skills

acquisition and life course contingency suggests

considerable endogeneity in school achievements

and educational attainment. From this perspective,

educational achievements, including test scores

and overall attainment, are largely a function of

cognitive and noncognitive abilities established

early in life. Empirical evidence on this is strong.

McCoy and Reynolds (1999), for example, found

that early school performance, test scores, and

grades were the strongest predictors of grade

retention, with further, independent associations

with poorer school performance and subsequent

attainment (see also A. J. Reynolds 1992). Simi-

larly, Farkas and colleagues’ (1990) study of

seventh- and eighth-grade students found that

both cognitive and noncognitive skills, the latter

they call ‘‘cultural resources,’’ had extremely large

effects on course work mastery and grades; these

effects were larger than the effects of sex, race,

and family income combined. Moreover, Carneiro

and colleagues’ (2007) analysis of data from the

UK National Child Development Survey showed

large effects of cognitive and noncognitive traits

at age 11 on educational attainment at age 42. Per-

haps most impressive, Entwisle and colleagues’

(2005) study of students in Baltimore found that

reading and math grades and a composite indicator

of temperament in first grade were significant pre-

dictors of overall educational attainment in early

adulthood. In general, numerous studies indicate

the overwhelming importance of cognitive (see

Beckett et al. 2006; Doherty 1997; Fryer and Lev-

itt 2004; Janus and Duku 2007; Lee and Burkam

2002) and noncognitive (see Evans et al. 1997;

M. R. Gottfredson and Hirschi 1990; A. L. Harris

and Robinson 2007; Tach and Farkas 2006) skills

for educational achievements. Figure 1 shows the

temporal aspects of these associations. The key

elements are the reinforcing processes of cognitive

and noncognitive skills from infancy to early

adulthood, the nonrecursive associations between

skills and schooling in early and late childhood,

and the recursive effects of skills on schooling in

the teenage and early adult years.

A critical period–life course contingency

perspective has a number of implications for

education-health dynamics. First, cognitive and

noncognitive skills should have a strong associa-

tion with health by virtue of their links to psycho-

logical affect, risk behaviors, and socioeconomic

status, which are the theoretical underpinnings of

health dynamics over the life course. Stronger

cognitive ability allows for better and faster pro-

cessing of health-related information and better

problem solving. Similarly, the conceptualization

of noncognitive abilities (e.g., self-control) maps

closely onto various orientations toward risk and

risk aversion that have clear relevance for health

(Caspi et al. 1997). Second, educational attain-

ment as conventionally measured (i.e., total years,

highest grade completed, and credentials) may

proxy time-stable (at least at the point of measure-

ment) unmeasured cognitive and noncognitive

abilities. In theory, people with more years of edu-

cation or higher degree attainment have accumu-

lated and demonstrated possession of the stock

of skills necessary for promotion into that particu-

lar rank, and their presence in that rank is a signal

of their cumulative stock of cognitive and




noncognitive abilities. Third, the time-stable stock

of cognitive and noncognitive abilities are poten-

tial confounders of the education-health relation-

ship in research that examines health at any point

past mid-adolescence. Here, one does not need to

accept that key skill formation occurs in early life

and prior to school entry (cf. Heckman 2006); one

could simply assume that key skill formation sol-

idifies at any time before the initial point of educa-

tional stratification, typically high school comple-

tion. In summary, educational attainment may be

of questionable relevance for health with appropri-

ate controls, either direct or indirect, for typically

unmeasured time-stable attributes, such as cogni-

tive and noncognitive skills, that are exogenous

to educational attainment and important determi-

nants of health. A soft version of this thesis would

deem a significant portion of the educational

attainment effect to be spurious with controls for

cognitive and noncognitive abilities. A harder ver-

sion of the thesis would view the effects of educa-

tion to be noncausal and substantively and statisti-

cally insignificant with appropriate controls for

such abilities. Given these expectations, fruitful

research would use statistical models that control

for such time-stable attributes of individuals and

directly or indirectly model the association

between cognitive and noncognitive traits, educa-

tional attainment, and health.

SKILLS AND HEALTH: A REVIEWOF THE EVIDENCE

Evidence on the role of cognitive and noncogni-

tive skills in health dynamics is simultaneously

unequivocal, complicated, and difficult to inter-

pret. For cognitive skills, L. Gottfredson

(2004:189) argues that intelligence is the true

‘‘fundamental cause’’ of health, stressing that

‘‘health self-management is inherently complex

and thus puts a premium on the ability to learn,

reason, and solve problems.’’ Link and colleagues

(2008) review several studies and conclude that

most show a cross-sectional or longitudinal rela-

tionship between cognitive ability and health.

But further consideration of educational attain-

ment complicates things. In some studies, socio-

economic status, including educational attainment,

significantly attenuates the effects of cognitive

ability, suggesting that educational attainment

may explain the effects of cognitive ability. Other

studies show that the effects of socioeconomic sta-

tus are attenuated by cognitive ability. Link and

Figure 1. Conceptual model of skills and schooling in early life.




colleagues’ (2008) own research is more defini-

tive: The effects of cognitive ability are substan-

tially reduced with controls for educational attain-

ment and income, but the effects of educational

attainment and income are substantively

unchanged conditional on cognitive ability. In

contrast, Conti and colleagues (2010) analyze lon-

gitudinal data from the 1970 British Cohort Study

and conclude that selection due to cognitive and

noncognitive skills measured in early childhood

account for over half of the observed educational

differences in poor health, depression, and obesity

at age 30. This research shows cognitive ability is

clearly implicated in health dynamics, but its con-

ditional relationship with educational attainment is

much less definitive.

Evidence of the role of noncognitive skills in

health dynamics is either quite rare or routine,

depending on conceptualization. M. R. Gottfred-

son and Hirschi (1990), for example, describe

‘‘low self-control’’ as characterized by impulsivity,

a preference for simple tasks, risk-seeking, physi-

cality, self-centeredness, and being temperamen-

tal. From this perspective, the vast literature that

relates a range of risk behaviors—including dan-

gerous driving, substance use, involvement in

crime and violence, smoking, poor diet and lack

of exercise, noncompliance with medical advice,

and poor preventative care practices—to poor

health can all be seen as evidence of the impor-

tance of noncognitive traits for health over the

life course (see also Moffitt et al. 2011). At the

same time, personality measures of the type that

Conti and colleagues (2010) use are quite rare in

socio-health research but still suggest important

effects. Summarizing across a number of studies,

the evidence that links cognitive and noncognitive

skills to health dynamics is provocative but under-

developed, interpretation is complicated, and con-

clusions are ultimately equivocal.

DATA AND MEASURES

Our study uses data from the National Longitudi-

nal Survey of Youth-1997 (hereafter NLSY97).

The NLSY97 consists of an initial sample of

8,984 respondents who were between the ages of

12 and 16 in 1997. When possible, respondents

were reinterviewed annually, and data were col-

lected on a range of topics on the transition to

adulthood. As of 2016, there are 16 waves of

data covering an age range of 12 to 32 years. In

addition to non-Hispanic whites, the NLSY97

oversampled blacks and Hispanics such that there

are relatively large samples of six race-sex groups.

Compared to other national surveys, panel reten-

tion is excellent, with 79 percent of the sample

retained at wave 16. The NLSY97 survey is spon-

sored and directed by the U.S. Bureau of Labor

Statistics and conducted by the National Opinion

Research Center at the University of Chicago,

with assistance from the Center for Human

Resource Research at The Ohio State University

(Bureau of Labor Statistics 2013).

For our study of skills, educational attainment,

and health, we capitalize on the record structure of

the NLSY97 data and its position in the history of

population health in the United States. For the for-

mer, the multipanel record structure provides

annual, repeated measures of education and

health, coupled with reasonable measures of cog-

nitive and noncognitive traits in adolescence.

This allows for use of statistical approaches that

better control for unobserved heterogeneity than

do traditionally used ordinary least squares

(OLS) approaches. In the latter case, the obesity

epidemic in the United States has had profound

effects on the age structure of health liabilities.

As K. Harris (2010) notes, much data, including

studies such as the National Longitudinal Study

of Adolescent Health, show that obesity is a har-

binger of short-term and longer-term chronic

health problems, and a range of serious health

problems (e.g., type 2 diabetes and hypertension)

are increasingly visible through the early adult

years. Given this, heterogeneity in health liabili-

ties, including self-perceived liabilities, is increas-

ingly measurable in the early adult years among

contemporary cohorts (Bauldry et al. 2012).

Health

In these analyses, we measure health as self-rated

health (hereafter SRH). This widely used measure

is one of the most validated survey instruments,

with strong and robust associations shown for

a wide range of morbidities and follow-up mortal-

ity (Ferraro and Farmer 1999; Idler and Benyamini

1997), and it has been validated for samples of

youth and young adults (Bauldry et al. 2012; Fosse

and Haas 2009; Vingilis, Wade, and Seeley 2002).

SRH asks respondents to describe their health on

a scale from excellent, coded 1, through very

good, good, fair, to poor, coded 5. Although we




recognize range limitations, we treat SRH as a con-

tinuous variable to ensure maximum sample repre-

sentation over the 16 waves of data. Change in

health status across panels varies between 46 per-

cent and 52 percent of respondents depending on

the panels being compared.

Educational Attainment

We treat educational attainment as a set of

dummy variables indexing high school/GED,

some college, a two-year degree, or a four-year

college degree or greater with less than a high

school degree as the reference category. This

allows us to capture a range of meaningful con-

trasts in education as they relate to health, and

we can assess linearity or consider nonlinearities

if apparent. Panel-specific change in educational

attainment varies from 8 percent to 27 percent of

respondents depending on the panels being

compared.

Cognitive Skills

We operationalize cognitive skills using the

Armed Services Vocational Aptitude Battery

(ASVAB). In the NLSY97, the ASVAB is a sum-

mary percentile score variable based on four key

subsets of the larger ASVAB battery. The subsets

covered include mathematical knowledge, arith-

metic reasoning, word knowledge, and paragraph

comprehension; they were calculated for each

three-month age grouping and assigned to percen-

tiles based on theta scores. Carroll’s (1992:267)

comprehensive review of measures of cognitive

abilities describes the ASVAB as ‘‘one of the

most widely administered group tests of cognitive

abilities . . . that feature dimensions of intelligence

such as verbal, reasoning, spatial, quantitative, and

memory abilities that have been recognized, for at

least 50 years, as being partly independent traits,

along with a more global trait of general

intelligence.’’ Specific comparisons of ASVAB

scores with those from the Wechsler Adult Intelli-

gence Scale (WAIS), the Wechsler Intelligence

Scale for Children (WISC-III), and the traditional

Stanford Binet IQ test indicate correlations in

excess of .95 (Herrnstein and Murray 2010:580–

85). To facilitate comparison of the magnitude

of effects, we transformed the continuous distribu-

tion into equal quintiles.

Noncognitive Skills

We use four measures of noncognitive skills that

capture different types of aptitudes and orientations.

The first measure comes from the 1997 wave of

data and indexes quintiles of commitment to school-

ing, when respondents were 12 to 16 years old,

based on the number of unexcused absences from

school in the previous year. The second measure

is also from 1997 and captures self-control. Draw-

ing on M. R. Gottfredson and Hirschi (1990), this

measure is a variety index of the total number of

different delinquent acts that respondents reported

committing during the previous 12-month period.

The third measure comes from the 2002 data

wave, when respondents were 17 to 21, and indexes

task-orientation. This is the degree (on a scale of

one to five) that respondents were ‘‘organized’’

(as opposed to ‘‘disorganized’’), ‘‘conscientious’’

(as opposed to ‘‘not conscientious’’), ‘‘dependable’’

(as opposed to undependable), and ‘‘thorough’’ (as

opposed to ‘‘careless’’). The final measure, also

from the 2002 wave, indexes sociability as the

degree that respondents were ‘‘agreeable’’ (as

opposed to ‘‘quarrelsome’’), ‘‘difficult’’ (as opposed

to ‘‘cooperative’’), ‘‘stubborn’’ (as opposed to

‘‘flexible’’), and ‘‘trustful’’ (as opposed to ‘‘dis-

trustful’’). For the latter two measures, we created

a cumulative score and then differentiated the sam-

ple into quintiles, again for ease of comparison. As

a set, these measures capture behavioral and psy-

chological aspects of noncognitive skills that may

have differing validity (Almlund et al. 2011).

They also include measures that are clearly tempo-

rally exogenous to our initial stratification point for

educational attainment, although the psychological

indicators were measured subsequent to the teen

years.1 Given recent work (e.g., Almlund et al.

2011), it is useful to think of the measures as bear-

ing some, albeit imperfect, affinity with the ‘‘Big

Five’’ personality constructs; here, we would argue

that sociability seems quite consistent with

‘‘agreeableness,’’ and task-orientedness and com-

mitment seem consistent with ‘‘conscientiousness.’’

Self-control does not seem to fit well, as has been

noted in earlier theoretical statements on its struc-

ture and dimensionality (M. R. Gottfredson and

Hirschi 1990).

Risk Behaviors/States

In certain models, we incorporate a number of

measures of behavioral risks and states that all




have resonance in sociological research on health.

The first risk measure is marital status, which has

a long history in health research (Waite and Gal-

lagher 2002); it differentiates between respondents

who are married, separated or divorced, or never

married (reference category). The second measure

captures variation in vocational activities and

identifies respondents enrolled in school. The

third set of measures is based on body mass index

(BMI) scores calculated from self-reported height

and weight and differentiates respondents who are

underweight (\18.5), overweight (25 to 29.9),

obese (30 to 34.9), and severely obese (35 or

greater). The reference category is respondents

whose BMI falls in the optimal range (18.5 to

24.9). In these data, a BMI of 30 or greater is

the standard indicator of obesity in adults; it corre-

sponds almost identically to the 95th percentile for

respondents at ages 12 through 14 and is above the

90th percentile for ages 15 through 17 in the

NLSY97 data.

We also include various types of substance use.

With respect to smoking, we differentiate respond-

ents who do not smoke from light (1 to 72 ciga-

rettes per month), moderate (73 to 297 cigarettes

per month), or heavy (more than 300 cigarettes

per month) smokers. We adopt a similar strategy

for alcohol consumption, with abstainers distin-

guished from light (1 to 4 drinks per month), mod-

erate (5 to 20 drinks per month), and heavy (more

than 20 drinks per month) drinkers. Finally, we

include two indicators of drug use that index using

marijuana or other drugs in each wave of data

during the previous 12 months.

Controls and Stratification

Our models also include measures associated with

both health and educational selection. To capture

socioeconomic influences, we include a measure

indexing whether respondents reported living in

hard times and variables measuring parental edu-

cational attainment (we use similar categories as

for respondents, for both mother and father,

regardless of residence). To capture early health

disadvantage, we include two dummy variables

indexing whether respondents had a history of

chronic disease or limb disability. We drew all

these measures from the 1997 data collection.

Our models also include a key control for aging,

captured through the panel structure of the data.

We measure aging in terms of a linear

specification and as a dummy variable specifica-

tion, but we report only the former because the

results are consistent. This measure accounts for

well-recognized declines in health with advancing

age as well as the strong determinism of educa-

tional attainment with aging.2 To stratify by race

and sex, we distinguish white males, white

females, black males, black females, Hispanic

males, and Hispanic females. Table 1 shows

descriptive statistics.

ANALYTIC METHODS

Our analytic strategy involves estimation of three

types of models. First, we estimate between-effect

models that mimic traditional OLS and general-

ized linear models (GLMs), which characterize

the vast majority of work on educational attain-

ment and health (for discussion of the use of con-

ventional regression models, see Cutler and

Lleras-Muney 2008). The between-effect estima-

tor regresses the mean response for health on the

mean responses of all the predictor variables,

with information on the regression coefficients

from within-respondent variability eliminated.

Second, we estimate maximum likelihood,

random-effects (ML-RE) models where the model

includes a random intercept, typically denoted zj,

that is independent across subjects, and a residual,

Eij, that is independent across subjects and time

periods. Both are normally distributed with means

equal to zero and that are independent of one

another. A random-effects approach provides

a more stringent control for unmeasured heteroge-

neity: It parameterizes across person variation

while still accommodating time-stable and time-

varying variables, particularly our indicators of

cognitive and noncognitive skills that are mea-

sured at only one point in time. To account for

the possibility of serial correlation, we estimate

the ML-RE models with an autoregressive compo-

nent with a lag of 1 (AR1). Finally, the third spec-

ification is a maximum likelihood, fixed-effects

(ML-FE) model. With this specification, there is

a parameter, ai, which is a unique intercept for

each respondent, and we model deviations off

the person-specific intercept. In contrast to the

conventional OLS (and to a lesser extent ML-RE

approaches), a fixed-effects approach provides

much more stringent control for unobserved het-

erogeneity bias, at least under most circumstances

(see general discussions in Allison 2009; Halaby




Table 1. Descriptive Statistics, National Longitudinal Survey of Youth-1997 (NLSY97).

Mean or

Percentage SD Minimum Maximum

Self-rated health (time-varying) 2.139 .945 1 5

Sociodemographics

Race-sex group (1997) 2.781 1.657 1 6

White males 28.29

White females 27.05

Black males 12.03

Black females 13.17

Hispanic males 9.87

Hispanic females 9.60

Birth cohort (1997) 1981.903 1.389 1982 1984

Socioeconomic and health selection (1997)

Lived in ‘‘hard times’’ .052 .222 0 1

Limb deformity .015 .121 0 1

Has chronic condition .109 .312 0 1

Parental education 2.674 1.027 1 4

Less than high school 14.13

High school graduate 31.92

Some college 26.37

Four-year degree or more 27.59

Respondent’s educational attainment (time-varying) 2.660 1.255 1 5

Less than high school 20.92

High school graduate 24.31

Some college 37.06

Two-year degree 3.28

Four-year degree or more 14.43

Respondent’s enrollment (time-varying) .326 .469 0 1

Cognitive and noncognitive skills

Cognitive ability, categories (1997) 3.591 1.662 1 6

Educational commitment, categories (1997) 2.774 1.469 1 5

Self-control, categories (1997) .777 .879 0 3

Task-orientedness, categories (2002) 4.247 1.905 1 6

Sociability, categories (2002) 4.210 1.931 1 6

Proximal risk factors (time-varying)

Marital status .267 .515 0 2

Never married 76.77

Married 19.76

Separated/divorced/widowed 3.47

BMI 2.794 1.003 1 5

Underweight 1.63

Normal range 48.49

Overweight 28.07

Obese 12.47

Severely obese 9.33

Smoking .696 1.097 0 3

Nonsmoker 66.41

Light smoker 11.20

Moderate smoker 8.79

Heavy smoker 13.59

Drinking 1.325 1.205 0 3

Abstainer 38.14

Light drinker 14.67

Moderate drinker 23.73

Heavy drinker 23.46

Marijuana use .215 .407 0 1

Other drug use .050 .218 0 1

Respondents 7,177

Observations 80,282




2004). The disadvantage is that they require time-

varying covariates and hence cannot include

measures of adolescent-based skills and traits

that are fixed attributes. Still, such models will

capture the broad but undefined stock of time-

invariant attributes, of which cognitive and non-

cognitive traits established early in the life course

would be a key component. These models should

not be viewed as ensuring causal identification

but instead as preferred for controlling for the

large battery of time-stable traits that can bias

coefficients of effects of educational attainment

on health. The effects of cognitive and noncogni-

tive skills should be captured in the unit-specific

intercept. As with the random-effects specifica-

tion, we estimate the ML-FE models with an

autoregressive component with a lag of 1 (AR1).

To our knowledge, these models have not been

used in the study of educational attainment and

health, yet they are powerful for assessing the

importance of time-stable attributes formed early

in life—prior to the key cut-points of educational

attainment—that might confound the education-

health relationship. Still, our approach has affini-

ties with a long and insightful tradition of dynamic

modeling of health (e.g., Adams et al. 2003; Con-

toyannis, Jones, and Rice 2004; Halliday 2008;

Preston and Taubman 1994). As noted, we present

effects based on categorical coding to allow for

substantive comparison across indicators that

have varying levels of measurement. These are

not standardized coefficients in the traditional

sense; instead, they represent group-based differ-

ences in health conditional on the various covari-

ates in each model.

RESULTS

We begin by estimating between- and within-

person variance for health and educational attain-

ment. This is a fundamental precursor for our

research, as it shows change in both SRH and edu-

cational attainment over time as well as heteroge-

neity in such change, which is the foundational

requirement for the panel models we ultimately

use. For SRH, we see statistically significant linear

declines over time (b = .027, p \ .001) as well as

significant cross-respondent heterogeneity in

slopes (Oc = .0542, p \ .001). The intraclass cor-

relation coefficient indicates that 55 percent of the

variance is longitudinal, within respondents over

time. Similar dynamics are clear with respect to

educational attainment. Educational attainment

increases substantially (b = .141, p \ .001), on

average, and with significant heterogeneity (Oc

= .0993, p \ .001). In contrast to SRH, across-

person variance is somewhat larger than within-

person variance: The intraclass correlation coeffi-

cients show that 36 percent of the variance is over

time. In general, these data conform well to the

necessary statistical criteria for estimation of the

models we use.

Skills and Schooling

Our substantive analyses begin by briefly describ-

ing results modeling different levels of educa-

tional attainment (in 2013) based on race-sex,

family socioeconomic status, early health, and

cognitive and noncognitive skills (in 1997/2002).

For the sake of brevity, we simply note four items

(see Table 2). Evidence of socioeconomic is

strong. For respondents who reported living in

‘‘hard times,’’ the odds of successively greater

educational attainment, beyond not completing

high school, are .65 (e2.437 = .646), .65 (e2.425 =

.654), .37 (e2.994 = .370), and .25 (e21.382 = .251)

for high school completion, some college, a two-

year degree, and a four-year degree, respectively.

The effects of parental education are even stron-

ger. For example, respondents with a parent with

a four-year degree or more than three times as

likely (e1.232 = 3.428) to graduate high school,

almost 18 times more likely (e2.880 = 17.814) to

have some college, 21.8 times more likely (e3.085

= 21.867) to have a two-year degree, and 97 times

more likely (e4.579 = 97.417) to have a four-year

degree or more compared to respondents whose

parents did not finish high school. Evidence of

health selection is more mixed. Respondents who

reported chronic conditions in early life were 38

percent less likely (e2.479 = .619) to achieve

a two-year degree, but no other associations

were apparent. Similarly, we find no significant

effects for respondents with limb limitations.

Second, both cognitive and noncognitive skills

have significant associations with educational

attainment independent of the effects of early

health and early socioeconomic status. For exam-

ple, quintile increases in cognitive ability track

increases in educational attainment (i.e., coeffi-

cients get successively larger as one moves up

quintile rank and up educational attainment). For

school commitment, effects are less linear across




Tab

le2.

Multin

om

ialLo

git

Coef

ficie

nts

:Educa

tional

Att

ainm

ent

in2013

Reg

ress

edon

Dem

ogr

aphic

,So

cioec

onom

ic,an

dH

ealth

Bac

kgro

und

Fact

ors

and

Cogn

itiv

ean

dN

onco

gnitiv

eSk

ills.

Educa

tional

Att

ainm

ent

in2013

Hig

hSc

hoolC

om

ple

tion

Som

eC

olle

geTw

o-y

ear

Deg

ree

Four-

year

Deg

ree

or

More

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Rac

e-se

x(r

efer

ence

=w

hite

mal

es)

White

fem

ale

20.1

13

20.2

04

0.1

89

0.1

10

0.1

98

0.0

85

0.6

19***

0.4

74*

(0.1

76)

(0.1

85)

(0.1

74)

(0.1

91)

(0.2

06)

(0.2

23)

(0.1

77)

(0.2

01)

Bla

ckm

ale

20.2

32

0.0

18

20.1

14

0.6

66***

20.9

06***

20.1

26

20.8

69***

0.4

47*

(0.1

71)

(0.1

81)

(0.1

73)

(0.1

92)

(0.2

49)

(0.2

68)

(0.1

97)

(0.2

25)

Bla

ckfe

mal

e2

0.0

82

0.1

55

0.6

94***

1.4

70***

0.2

15

0.9

39***

0.4

86*

1.6

58***

(0.1

97)

(0.2

11)

(0.1

92)

(0.2

16)

(0.2

41)

(0.2

66)

(0.2

04)

(0.2

36)

His

pan

icm

ale

20.3

80*

20.2

37

20.0

93

0.3

29

20.5

47*

20.1

10

20.7

02***

20.0

20

(0.1

85)

(0.1

92)

(0.1

87)

(0.2

03)

(0.2

59)

(0.2

75)

(0.2

13)

(0.2

40)

His

pan

icfe

mal

e2

0.0

28

0.0

46

0.5

71**

0.9

60***

0.4

00

0.7

98***

0.5

09*

1.2

06***

(0.2

08)

(0.2

17)

(0.2

07)

(0.2

25)

(0.2

62)

(0.2

81)

(0.2

21)

(0.2

50)

Cohort

(ref

eren

ce=

1980)

1981

20.0

57

20.0

99

0.0

12

20.0

52

20.0

04

20.0

78

0.1

30

20.0

33

(0.1

78)

(0.1

82)

(0.1

75)

(0.1

87)

(0.2

28)

(0.2

38)

(0.1

88)

(0.2

06)

1982

20.1

44

20.7

02**

20.3

18

21.2

58***

20.1

04

22.0

61***

20.0

28

21.8

17***

(0.1

75)

(0.2

91)

(0.1

75)

(0.3

16)

(0.2

25)

(0.5

52)

(0.1

86)

(0.3

77)

1983

0.0

38

20.5

56

20.0

13

20.9

88***

0.0

81

21.9

56***

0.0

01

21.9

28***

(0.1

79)

(0.3

01)

(0.1

78)

(0.3

24)

(0.2

28)

(0.5

59)

(0.1

90)

(0.3

87)

1984

20.1

20

20.7

61**

20.1

11

21.1

27***

20.0

96

22.2

14***

20.0

15

22.0

58***

(0.1

77)

(0.3

06)

(0.1

75)

(0.3

28)

(0.2

28)

(0.5

63)

(0.1

88)

(0.3

89)

Bac

kgro

und

vari

able

s(1

997)

Live

din

‘‘har

dtim

es’’

20.4

37**

20.2

82

20.4

25*

20.1

24

20.9

94***

20.6

44*

21.3

82***

20.9

26***

(0.1

83)

(0.1

90)

(0.1

83)

(0.1

99)

(0.3

15)

(0.3

27)

(0.2

49)

(0.2

81)

Has

limb

def

orm

ity

0.8

31

1.0

49*

0.3

16

0.5

82

0.7

53

0.9

97

0.0

63

0.3

44

(0.5

42)

(0.5

60)

(0.5

58)

(0.5

95)

(0.6

22)

(0.6

60)

(0.5

78)

(0.6

31)

Has

chro

nic

conditio

n2

0.1

03

20.1

37

20.2

82

20.3

71

20.4

79*

20.5

38*

20.3

05

20.3

23

(0.1

65)

(0.1

72)

(0.1

66)

(0.1

80)

(0.2

31)

(0.2

42)

(0.1

78)

(0.2

00)

(con

tinue

d)

182



Tab

le2.

(Co

nti

nu

ed

)

Educa

tional

Att

ainm

ent

in2013

Hig

hSc

hoolC

om

ple

tion

Som

eC

olle

geTw

o-y

ear

Deg

ree

Four-

year

Deg

ree

or

More

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Par

ent

=hig

hsc

hoolgr

aduat

e0.5

82***

0.4

64***

0.9

89***

0.7

29***

1.2

25***

0.9

39***

1.1

45***

0.7

04***

(0.1

34)

(0.1

38)

(0.1

39)

(0.1

49)

(0.2

18)

(0.2

27)

(0.1

77)

(0.1

98)

Par

ent

=so

me

colle

ge0.8

39***

0.7

44***

1.8

16***

1.5

05***

1.9

51***

1.6

61***

2.4

55***

1.9

65***

(0.1

67)

(0.1

73)

(0.1

68)

(0.1

80)

(0.2

40)

(0.2

52)

(0.1

97)

(0.2

20)

Par

ent

=fo

ur-

year

deg

ree

or

more

1.2

32***

1.0

57***

2.8

80***

2.3

79***

3.0

85***

2.5

60***

4.5

79***

3.7

24***

(0.2

76)

(0.2

81)

(0.2

69)

(0.2

81)

(0.3

23)

(0.3

36)

(0.2

85)

(0.3

05)

Cogn

itiv

eab

ility

,A

SVA

Bper

centile

(1997)

20th

—0.7

90***

—1.6

39***

—1.7

03***

—2.7

88***

(0.1

62)

(0.1

73)

(0.2

43)

(0.2

71)

40th

—1.2

22***

—2.5

82***

—2.2

60***

—3.9

52***

(0.2

14)

(0.2

19)

(0.2

83)

(0.2

98)

60th

—1.9

45***

—3.7

98***

3.7

41***

—5.7

12***

(0.3

63)

(0.3

63)

(0.4

03)

(0.4

13)

80th

—1.9

05***

—4.3

01***

—4.5

25***

—7.0

57***

(0.6

09)

(0.5

99)

(0.6

25)

(0.6

30)

Mis

sing

—0.2

31

—1.0

19***

—0.7

82**

—2.5

15***

(0.1

44)

(0.1

57)

(0.2

46)

(0.2

57)

Schoolco

mm

itm

ent

per

centile

(1997)

20th

—2

0.4

88**

—2

0.6

23***

—2

0.4

02

—2

0.8

31***

(0.2

05)

(0.2

10)

(0.2

49)

(0.2

23)

40th

—2

0.2

58

—2

0.3

70

—2

0.4

48

—2

0.7

04***

(0.1

87)

(0.1

91)

(0.2

32)

(0.2

04)

60th

—2

0.3

95*

—2

0.5

81***

—2

0.5

34*

—2

1.1

10***

(0.1

83)

(0.1

88)

(0.2

30)

(0.2

05)

80th

—2

0.8

82***

—2

1.1

30***

—2

1.2

22***

—2

1.9

56***

(0.1

70)

(0.1

76)

(0.2

30)

(0.2

02)

(con

tinue

d)

183



Tab

le2.

(Co

nti

nu

ed

)

Educa

tional

Att

ainm

ent

in2013

Hig

hSc

hoolC

om

ple

tion

Som

eC

olle

geTw

o-y

ear

Deg

ree

Four-

year

Deg

ree

or

More

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Self-

contr

ol(1

997)

Hig

h—

20.4

26***

—2

0.4

13**

—2

0.5

41***

—2

0.8

80***

(0.1

34)

(0.1

38)

(0.1

71)

(0.1

50)

Moder

ate

—2

0.5

28***

—2

0.5

08**

—2

0.7

16***

—2

1.4

81***

(0.1

74)

(0.1

80)

(0.2

29)

(0.2

07)

Low

—2

0.4

60*

—2

0.7

09***

—2

1.7

56***

—2

2.3

07***

(0.2

15)

(0.2

29)

(0.3

92)

(0.3

07)

Soci

abili

typer

centile

(2002)

20th

—0.1

21

—0.2

57

—0.2

13

—0.3

79

(0.1

95)

(0.2

04)

(0.2

68)

(0.2

30)

40th

—0.3

63

—0.6

82*

—0.8

46**

—0.8

59***

(0.2

62)

(0.2

70)

(0.3

29)

(0.2

96)

60th

—0.4

81

—0.8

81***

—0.9

85***

—1.0

27***

(0.2

53)

(0.2

58)

(0.3

13)

(0.2

81)

80th

—0.4

25

—0.5

83**

—0.8

34**

—0.7

45*

(0.2

75)

(0.2

85)

(0.3

43)

(0.3

10)

Mis

sing

—2

0.8

92

—2

0.7

46

—2

0.2

08

—2

1.8

81

(0.9

92)

(1.0

77)

(2.0

10)

(1.2

56)

Task

-ori

ente

dper

centile

(2002)

20th

—2

0.0

73

—0.0

91

—0.2

12

—0.3

70

(0.2

29)

(0.2

37)

(0.3

06)

(0.2

71)

40th

—0.4

02*

—0.5

43**

—0.6

45**

—0.9

09***

(0.1

97)

(0.2

05)

(0.2

57)

(0.2

30)

60th

—0.3

85

—0.6

91*

—0.9

12**

—1.4

15***

(0.3

19)

(0.3

26)

(0.3

86)

(0.3

49)

80th

—0.4

28

—0.5

10

—0.6

38

—1.1

48***

(0.2

87)

(0.2

93)

(0.3

48)

(0.3

13)

(con

tinue

d)

184



Tab

le2.

(Co

nti

nu

ed

)

Educa

tional

Att

ainm

ent

in2013

Hig

hSc

hoolC

om

ple

tion

Som

eC

olle

geTw

o-y

ear

Deg

ree

Four-

year

Deg

ree

or

More

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Mis

sing

—0.7

58

—0.5

48

—2

0.8

38

—1.4

91

(0.9

80)

(1.0

61)

(2.0

04)

(1.2

26)

Const

ant

0.9

01***

1.3

53***

0.2

45

20.1

60

21.2

36***

20.5

54

20.7

53***

21.8

25***

(0.1

99)

(0.3

96)

(0.2

03)

(0.4

27)

(0.2

83)

(0.6

78)

(0.2

32)

(0.5

33)

Obse

rvat

ions

5.8

29

5,8

29

5.8

29

5,8

29

5.8

29

5,8

29

5.8

29

5,8

29

Sour

ce.N

atio

nal

Longi

tudin

alSu

rvey

of

Youth

-1997.Sa

mple

isre

stri

cted

tore

sponden

tsw

ith

no

mis

sing

dat

aon

the

full

cova

riat

ese

t.N

ote.

Dat

aar

eunw

eigh

ted.St

andar

der

rors

inpar

enth

eses

.*p

\.1

0.**p

\.0

5.***p

\.0

1.

185



quintiles but still show that higher commitment is

associated with successively greater educational

attainment. Effects for self-control, in contrast,

are quite linear and show low self-control to be

a distinct liability for greater educational attain-

ment. For example, low self-control decreases

the odds of high school completion by 36 percent

(e2460 = .631) and decreases the odds of a four-

year degree or more by 90 percent (e22.307 =

.010). Finally, sociability and task-orientedness

also show myriad associations, and higher levels

of each are associated with successively greater

educational attainment.

Third, the inclusion of cognitive and noncogni-

tive skills significantly reduces associations with

family socioeconomic status. In comparing across

models, the effect of living in ‘‘hard times’’ is

reduced to nonsignificance, and the effects of

parental attainment are reduced by 14, 17, 17, and

19 percent, for high school completion, some col-

lege, two-year, and four-year or more degree attain-

ment, respectively. Finally, the effects of cognitive

and noncognitive skills are large in magnitude. If

one uses parents with a four-year or more college

degree as a benchmark, the effects of being in the

top quintile of cognitive ability are 80 percent

larger for high school completion, 181 percent

larger for some college, 177 percent larger for

two-year degree attainment, and 189 percent larger

for four-year or more degree attainment. If one

wanted to find comparable groups, having parents

with a four-year degree is equivalent for educa-

tional attainment to respondents around the 30th

percentile of cognitive ability for high school com-

pletion, some college, and a four-year or more

degree, and it is equivalent to respondents around

the median for two-year degree attainment.

Although less dramatic, respondents with the high-

est levels of school commitment have similar

attainments to respondents whose parents have

‘‘some college,’’ and respondents with the highest

levels of sociability and task-orientedness have

similar attainments to respondents with parents

who have a high school degree. Finally, self-control

has a more nonlinear association with the most pro-

nounced associations seen for four-year degree;

here, the effects are larger than for all levels of

parental education, with the exception of parents

having a four-year degree or more. In summary,

these analyses show that cognitive and noncogni-

tive skills are fundamental determinants of educa-

tional attainment; by extension, without appropriate

model specification, measured educational

attainment will typically capture unmeasured stocks

of cognitive and noncognitive skills.

Skills, Schooling, and Health:Between-effects Analyses

Our initial analyses of health involve between-

effects regressions. These models mimic in panel

data the standard OLS regression models typically

used in sociological research on health. OLS mod-

els have produced the conventional wisdom of

quasi-linear effects for educational differences

that are large in magnitude and reasonably robust

to background controls and proximal determi-

nants. Table 3 shows the results.

Model 1 includes covariates indexing sociode-

mographic, family background, and early health sta-

tus. Consistent with a wide body of research, SRH is

poorer among women, particularly black and His-

panic women; for respondents who lived in ‘‘hard

times’’ or whose parents had low educational attain-

ment; and for people who have chronic conditions or

limb limitations. When educational attainment and

enrollment are added (see Model 2), the differences

across educational categories are very large. With

a ‘‘between’’ standard deviation of .639 for SRH,

the effect for being a high school graduate is

–.154, the effect for having some college is –.253,

the effect for attaining a two-year degree is –.411,

and the effect for a four-year degree or more is

–.774. In short, the gradient is large and entirely in

line with expectations. School environments are

also conducive to better health: Being enrolled has

a moderate (b = –.219, p \ .001) effect on SRH.

Model 3 includes cognitive and noncognitive

ability measures, and the effects of education are

diminished. But they are not diminished a lot,

and we continue to see large associations for the

attainment of two-year (b = –.306) and four-year

or more (–.609) degrees. The effects of high levels

of cognitive and noncognitive skills are less

impressive: We find coefficients where even the

effects for the highest quantiles are similar in mag-

nitude to that of high school graduation (e.g.,

–.070 for cognitive skills vs. –.112) or some col-

lege (e.g., .188 for self-control vs. –.195). When

risk behaviors and states measures are included

in Model 4, they have the expected effects on

health and significantly reduce the effects of edu-

cational attainment. Still, the effects of educa-

tional attainment continue to be large in size and

comparatively larger to those seen for cognitive




Table 3. Between-effects (BE) Regression Coefficients: Self-rated Health Regressed on EducationalAttainment; Demographic, Socioeconomic, and Health Background Factors; Cognitive and Noncognitiveskills; and Proximal Causes of Health, National Longitudinal Survey of Youth-1997 (NLSY97).

Self-rated health

BE BE BE BE(1) (2) (3) (4)

Aging 0.037*** 0.050*** 0.048*** 0.033***(0.005) (0.006) (0.006) (0.006)

Race-sex (reference = white males)White female 0.122*** 0.173*** 0.188*** 0.180***

(0.021) (0.020) (0.020) (0.020)Black male 20.019 20.065** 20.068** 20.044

(0.026) (0.026) (0.026) (0.025)Black female 0.260*** 0.289*** 0.291*** 0.241***

(0.027) (0.026) (0.026) (0.026)Hispanic male 0.062* 0.032 0.026 0.052

(0.029) (0.028) (0.028) (0.027)Hispanic female 0.221*** 0.251*** 0.244*** 0.279***

(0.030) (0.029) (0.029) (0.028)Cohort (reference = 1980)

1981 20.037 20.036 20.026 20.011(0.025) (0.024) (0.024) (0.022)

1982 20.073** 20.085*** 20.167*** 20.112**(0.026) (0.025) (0.041) (0.039)

1983 20.046 20.074** 20.147*** 20.095**(0.026) (0.027) (0.043) (0.041)

1984 20.084*** 20.118*** 20.181*** 20.116***(0.027) (0.029) (0.045) (0.043)

Background variables (1997)Lived in ‘‘hard times’’ 0.135*** 0.070* 0.051 0.040

(0.035) (0.034) (0.033) (0.031)Has limb deformity 0.231*** 0.207*** 0.187*** 0.145**

(0.065) (0.063) (0.062) (0.058)Has chronic condition 0.210*** 0.200*** 0.185*** 0.171***

(0.025) (0.024) (0.024) (0.022)Parental education = high school

graduate20.112*** 20.057* 20.056* 20.066**

(0.025) (0.025) (0.024) (0.023)Parental education = some college 20.169*** 20.050 20.060* 20.073**

(0.026) (0.026) (0.026) (0.025)Parental education = four-year

degree or more20.298*** 20.059** 20.062* 20.060*

(0.027) (0.028) (0.028) (0.027)Respondent’s educational attainment

High school graduate — 20.154*** 20.112*** 20.066*(0.032) (0.033) (0.031)

Some college — 20.253*** 20.194*** 20.132***(0.034) (0.035) (0.033)

Two-year degree — 20.411*** 20.306*** 20.182***(0.068) (0.068) (0.064)

Four-year degree or more — 20.774*** 20.609*** 20.350***(0.049) (0.052) (0.050)

(continued)




Table 3.(Continued)

Self-rated health

BE BE BE BE(1) (2) (3) (4)

EnrolledYes 20.219*** 20.146*** 20.060

(0.044) (0.043) (0.041)Cognitive ability, ASVAB percentile

(1997)20th — — 0.051 0.026

(0.027) (0.025)40th — — 20.013 20.023

(0.028) (0.027)60th — — 20.031 20.060*

(0.030) (0.028)80th — — 20.070** 20.089**

(0.032) (0.030)0.004 20.012

(0.026) (0.025)School commitment percentile (1997)

20th — — 0.019 0.009(0.024) (0.022)

40th — — 0.037 0.034*(0.021) (0.020)

60th — — 0.067*** 0.048*(0.022) (0.021)

80th — — 0.145*** 0.109***(0.023) (0.022)

Self-control (1997)Moderate — — 0.045*** 0.011

(0.017) (0.016)Low — — 0.115*** 0.054*

(0.024) (0.023)Very low — — 0.188*** 0.103**

(0.034) (0.033)Sociability percentile (2002)

20th — — 20.095*** 20.074***(0.028) (0.026)

40th — — 20.095*** 20.078**(0.033) (0.031)

60th — — 20.128*** 20.098***(0.030) (0.029)

80th — — 20.220*** 20.193***(0.034) (0.032)

Missing 0.063 0.035(0.155) (0.145)

Task-oriented percentile (2002)20th — — 20.054 20.025

(0.033) (0.031)40th — — 20.109*** 20.092***

(0.027) (0.025)

(continued)




Table 3.(Continued)

Self-rated health

BE BE BE BE(1) (2) (3) (4)

60th — — 20.132*** 20.095**(0.037) (0.034)

80th — — 20.181*** 20.133***(0.032) (0.030)

20.364** 20.260(0.152) (0.142)

Marital statusMarried — — — 20.130***

(0.030)Separated/divorced — — — 0.221***

(0.063)Body mass (reference = normal range)

Underweight — — — 0.285***(0.083)

Overweight — — — 0.099***(0.026)

Obese — — — 0.434***(0.035)

Severely obese — — — 0.709***(0.033)

SmokingLight — — — 0.118***

(0.041)Moderate — — — 0.209***

(0.043)Heavy — — — 0.364***

(0.031)Drinking

Light — — — 0.086(0.047)

Moderate — — — 20.066(0.036)

Heavy — — — 20.108***(0.035)

Drug useMarijuana — — — 0.148***

(0.032)Other drugs — — — 0.127*

(0.065)Constant 1.887*** 1.979*** 2.119*** 1.901***

(0.048) (0.057) (0.074) (0.071)Respondents 7.177 7.177 7.177 7.177Observations 80.282 80.282 80.282 80.282R2 0.071 0.135 0.164 0.268

Source. National Longitudinal Survey of Youth-1997. Sample is restricted to respondents with no missing data on thefull covariate set.Note. Data are unweighted. Standard errors in parentheses.*p \ .10. **p \ .05. ***p \ .01.




and noncognitive skills. Equally important, effect

sizes are large even in comparison to those of

the proximal determinants. Only being obese

(b = .434, p \ .001) or severely obese (b = .709,

p \ .001) has effects substantially larger, and fac-

tors like intimate ties, being overweight, smoking,

drinking, and drug use typically have effects

smaller in magnitude to that of high educational

attainment (e.g., –.350 for a four-year degree or

more vs. .209 for moderate smoking). From

a purely empirical standpoint, the conventional

wisdom on educational attainment and health is

clearly demonstrated with the NLSY97 data and

a between-effects estimation strategy.

Skills, Schooling, and Health: Random-effects Analyses

We next turn to a random-effects approach (see

Table 4). Beginning with Model 1, sociodemo-

graphic, family socioeconomic status, and health

status variables have the expected effects and

show coefficients very similar to that observed

with a ‘‘between’’ approach. The same cannot be

said for educational attainment (see Model 2).

Although still statistically significant, the effects

of education are dramatically smaller and show

a gradient only moderate in magnitude. Here, the

coefficient for having a four-year degree or more

is –.225 with a random-effects specification, as

opposed to –.774 with a between-effects specifica-

tion. In general, the coefficients for the different

levels of educational attainment are much smaller,

with values of –.031, –.050, and –.129 for high

school completion, some college, and two-year

degree attainment, respectively. Because the

effects for cognitive and noncognitive skills are

robust, and there are further decreases in effect

sizes for educational attainment (see Model 3),

the effects of cognitive and noncognitive skills

are now either similar to or greater than even the

highest levels of educational attainment. For

example, if one uses the coefficient for a four-

year degree or more as a benchmark, the effect

for being in the upper 20th percentile for cognitive

ability is 29 percent larger (b = –.210 vs. –.163),

the effect for being in the upper quintile for school

commitment is 13 percent larger (b = .184), the

effect for low self-control is 40 percent larger

(b = .229), the effect for high sociality is 39 per-

cent larger (b = –.227), and the effect for task-

orientedness is 28 percent larger (b = –.208).

The final model includes the risk behavior and

state measures that are proximal determinants of

health (see Model 4). Effects are as expected with

obesity (b = .252, p \ .001), severe obesity (b =

.460, p \ .001), and heavy smoking (b = .230,

p \ .001) showing moderate to large associations,

and marital status, light or moderate smoking, and

drug use showing small but statistically significant

effects. Relevant coefficients indicate that magni-

tudes of the education effects are further reduced.

Effects for high school graduation and some college

are still small (b = –.025 and –.032, p \ .001).

Effects for a two-year degree are larger but still

fall into the range of a small effect (b = –.079,

p \ .001), and effects for a four-year degree or

more are somewhat larger and fall into the lower

range of moderate in magnitude (b = –.141, p \.001). Effects of cognitive and noncognitive skills

are also reduced. In the end, all the effects for the

most extreme quantiles of cognitive skills (b =

–.185, p \ .001) and noncognitive skills (bs =

.149, .156, –.210, and –.171 for commitment, self-

control, sociability, and task-orientedness, respec-

tively) fall in the lower range of moderate size.

Skills, Schooling, and Health: Fixed-effects Analyses

Our third set of analyses attempts to control even

more stringently for problems of unobserved het-

erogeneity and omitted variable bias through the

use of fixed-effects models (Table 5). Model 1

includes only educational attainment and school

enrollment. With all time-stable attributes con-

trolled, all associations between educational

attainment and health seen in previous models dis-

appear. Contrary to expectations, effects are posi-

tive for high school graduation, some college, and

a two-year degree. These effects, however, are

very small in magnitude and indicate only mar-

ginal differences in health. Effects for a four-

year degree or more are not statistically signifi-

cant. Model 2 includes the risk behavior and states

measures that are proximal determinants of health.

A number of these factors continue to have signif-

icant associations with SRH. For example,

respondents who are underweight (b = .103, p \.001), overweight (b = .052, p \ .001), obese

(b = .172, p \ .001), or severely obese (b =

.331, p \ .001) report poorer SRH. Respondents

who smoke and smoke more also report poorer

health (e.g., b = .182, p \ .001 for heavy




Table 4. Random Effects (RE) Coefficients: Self-rated Health Regressed on Educational Attainment,Demographic, Socioeconomic and Health Background Factors, Cognitive and Noncognitive Skills, andProximal Risk Factors, National Longitudinal Survey of Youth (NLSY97).

Self-rated Health

RE RE RE RE(1) (2) (3) (4)

Aging 0.027*** 0.031*** 0.030*** 0.024***(0.001) (0.001) (0.001) (0.001)

Race-sex (reference = white males)White female 0.127*** 0.142*** 0.173*** 0.183***

(0.021) (0.020) (0.020) (0.019)Black male 20.019 20.032 20.072*** 20.042

(0.026) (0.025) (0.026) (0.024)Black female 0.261*** 0.267*** 0.255*** 0.259***

(0.026) (0.025) (0.026) (0.024)Hispanic male 0.069* 0.060* 0.033 0.050

(0.029) (0.028) (0.028) (0.026)Hispanic female 0.228*** 0.235*** 0.222*** 0.265***

Cohort (reference = 1980) (0.030) (0.029) (0.029) (0.027)1981 20.030 20.029 20.014 20.008

(0.024) (0.023) (0.023) (0.021)1982 20.055* 20.058* 20.111** 20.085*

(0.024) (0.024) (0.043) (0.040)1983 20.027 20.035 20.075 20.055

(0.024) (0.024) (0.043) (0.041)1984 20.055* 20.064** 20.091* 20.066

(0.025) (0.024) (0.044) (0.042)Background variables (1997)

Lived in ‘‘hard times’’ 0.136*** 0.117*** 0.071* 0.060*(0.034) (0.033) (0.032) (0.030)

Has limb deformity 0.239*** 0.232*** 0.200*** 0.171**(0.064) (0.062) (0.061) (0.057)

Has chronic condition 0.214*** 0.211*** 0.193*** 0.184***(0.024) (0.024) (0.023) (0.022)

Parental education = high school graduate 20.113*** 20.099*** 20.074*** 20.074***(0.025) (0.024) (0.024) (0.022)

Parental education = some college 20.169*** 20.137*** 20.104*** 20.099***(0.026) (0.025) (0.025) (0.024)

Parental education = four-year degree or more 20.299*** 20.231*** 20.148*** 20.121***(0.027) (0.026) (0.027) (0.025)

Respondent’s educational attainmentHigh school graduate — 20.031** 20.021 20.025*

(0.012) (0.012) (0.012)Some college — 20.050*** 20.021 20.032**

(0.011) (0.011) (0.011)Two-year degree — 20.129*** 20.084*** 20.079***

(0.024) (0.025) (0.024)Four-year degree or more — 20.225*** 20.163*** 20.141***

(0.017) (0.017) (0.017)Enrolled

Yes — 20.069*** 20.054*** 20.035***(0.009) (0.009) (0.009)

(continued)




Table 4.(Continued)

Self-rated Health

RE RE RE RE(1) (2) (3) (4)

Cognitive ability, ASVAB percentile (1997)20th — — 0.010 0.005

(0.026) (0.025)40th — — 20.077*** 20.070**

(0.027) (0.025)60th — — 20.128*** 20.123***

(0.028) (0.026)80th — — 20.210*** 20.185***

(0.029) (0.027)Missing — — 20.045 20.044*

(0.026) (0.024)School commitment percentile (1997)

20th — — 0.023 0.015(0.023) (0.022)

40th — — 0.052* 0.044*(0.021) (0.019)

60th — — 0.091*** 0.072***(0.022) (0.020)

80th — — 0.184*** 0.149***(0.023) (0.021)

Self-control (1997)Moderate — — 0.062*** 0.033*

(0.016) (0.015)Low — — 0.144*** 0.092***

(0.024) (0.022)Very low — — 0.229*** 0.156***

(0.034) (0.032)Sociability percentile (2002)

20th — — 20.100*** 20.089***(0.027) (0.026)

40th — — 20.103*** 20.089***(0.033) (0.030)

60th — — 20.143*** 20.119***(0.030) (0.028)

80th — — 20.227*** 20.210***(0.034) (0.031)

Missing — — 0.069 0.050(0.157) (0.148)

Task-oriented percentile (2002)20th — — 20.061 20.043

(0.033) (0.031)40th — — 20.121*** 20.110***

(0.026) (0.024)60th — — 20.155*** 20.128***

(0.036) (0.034)80th — — 20.208*** 20.171***

(0.032) (0.030)

(continued)




Table 4.(Continued)

Self-rated Health

RE RE RE RE(1) (2) (3) (4)

Missing — — 20.360* 20.295*(0.154) (0.145)

Marital statusMarried — — — 20.022*

(0.010)Separated/divorced — — — 20.011


Underweight — — — 0.077***(0.025)

Overweight — — — 0.078***(0.008)

Obese — — — 0.252***(0.012)

Severely obese — — — 0.460***(0.016)

SmokingLight — — — 0.061***

(0.010)Moderate — — — 0.169***

(0.012)Heavy — — — 0.230***

(0.012)Drinking

Light — — — 0.009(0.008)

Moderate — — — 0.007(0.008)

Heavy — — — 0.009(0.009)

Drug useMarijuana — — — 0.052***

(0.009)Other drugs — — — 0.089***

(0.014)Constant 1.956*** 1.975*** 2.154*** 1.991***

(0.031) (0.031) (0.059) (0.055)Respondents 7.177 7.177 7.177 7.177Observations 80.282 80.282 80.282 80.282R2





smokers). Drinking shows only very small effects

on SRH, but marijuana use (b = .050, p \ .001)

and other drug use (b = .082, p \ .001) show

somewhat larger effects. Not surprisingly, the

effects of educational attainment still show no sub-

stantive association with SRH.

Robustness across Race-Sex Groups

Our final analyses repeat the RE and FE analyses

for each of the six race-sex groups in our sample.

Table 6 shows these results. To summarize a large

amount of information succinctly, we simple note

the following. There is clearly some variability in

the RE estimates, but there is considerable consis-

tency.3 For high levels of cognitive ability, all

coefficients are negative, as expected. For poor

school commitment, effects are positive in all

cases, as expected, and statistically significant

for four of six groups. For low self-control, coeffi-

cients are positive in all cases, as expected, and

statistically significant in three of six cases. For

high task-orientedness, the coefficients are all neg-

ative, as expected, and statistically significant in

four of six cases. Finally, the coefficients for

high sociability are negative, as expected, in all

cases and statistically significant in three of these.

At the same time, the effects for educational

attainment are quite mixed. If one brackets com-

pletion of a four-year degree, we see significant

negative associations for only 2 of 18 coefficients,

for white women for attainment of a two-year

degree and for Hispanic men for some college

attainment. For the highest level of attainment in

our model, we find somewhat more consistency:

Effects are negative and statistically significant

in four of six cases. Yet, a comparison of magni-

tude of effects with those of cognitive and noncog-

nitive skills suggests that the latter, in numerous

instances, have effects of similar size or greater.

Summarizing the educational coefficients with

an FE specification is much simpler. Out of 24

coefficients estimated, only 1—for a four-year

degree or more among Hispanic men—is statisti-

cally significant and in the expected direction

(b = –.151, p \ .05).

DISCUSSION ANDCONCLUSIONS

This research presents five pieces of evidence that

collectively challenge the conventional wisdom of

a life course–human capital account and derivative

causal interpretations of the positive association

between educational attainment and health. First,

traditional between-effect regression approaches

show a strong, quasi-linear relationship between

educational attainment and SRH, in which high

levels of educational attainment have effect sizes

that rival or best some of the strongest proximal

determinants of health. Second, measured cogni-

tive and noncognitive skills that are temporally

exogenous and potential sources of spuriousness

account for a sizable proportion of the effects of

educational attainment on SRH. Third, the appli-

cation of controls for unmeasured heterogeneity

further reduces the magnitude and statistical sig-

nificance of the educational attainment effects.

Fourth, the effects of high levels of cognitive

and noncognitive skills are consistently larger

than those of educational attainment, including

the effects of a four-year college degree or greater,

when we include appropriate controls for unmea-

sured time-stable traits formed during the early

life course. Finally, a fixed-effects approach pro-

duces effects of educational attainment, including

for a four-year college degree or greater, that are

reduced to small or very small levels of magni-

tude, that are typically not statistically significant,

and where one could legitimately question their

overall importance for health dynamics. These

findings are robust across six race-sex groups.

Taken together, the findings highlight the impor-

tance of cognitive and noncognitive skills that

are formed during the early life course and in

doing so, raise questions about the typical life

course–human capital and causal effect interpreta-

tion of the education-health relationship.

Our research extends a growing body of work,

most of it outside of sociology, that questions the

causal interpretation of education on health. Natu-

ral experiments and studies of twins are clearly

valuable, but they have well-recognized limita-

tions: Statistical power is often low, scope condi-

tions are often vague (e.g., to what populations

do natural experiments that occur in a given place

at a given time and typically apply only to select

cohorts apply), and modes of instrumentation are

often laden with heavy and sometimes question-

able assumptions (e.g., that results from twin stud-

ies generalize to the wider population or even that

twins who are discordant on some factor general-

ize to the larger population of twins) (see discus-

sions in Boardman and Fletcher 2015; Fletcher

2015; Madsen and Osler 2009). In contrast, well-




Table 5. Maximum Likelihood Fixed-effects (FE) Coefficients: Self-rated Health Regressed on EducationalAttainment; Demographic, Socioeconomic, and Health Background Factors; Cognitive and NoncognitiveSkills; and Proximal Causes of Health.

Self-rated Health

FE FE(1) (2)

Aging 0.032*** 0.029***(0.001) (0.001)

Respondent’s educational attainmentHigh school graduate 0.082*** 0.073***

(0.015) (0.015)Some college 0.106*** 0.090***

(0.016) (0.016)Two-year degree 0.071* 0.058*

(0.028) (0.028)Four-year degree or more 0.039 0.031

(0.023) (0.022)Enrolled

Yes 20.001 0.004(0.009) (0.009)

Marital statusMarried — 20.004

(0.011)Separated, divorced, widowed — 20.044*


Underweight — 0.103**(0.027)

Overweigh — 0.052***(0.009)

Obese — 0.172***(0.013)

Severely obese — 0.331***(0.018)

SmokingLight — 0.052***

(0.010)Moderate — 0.141***

(0.012)Heavy — 0.182***

(0.014)Drinking

Light — 0.026***(0.008)

Moderate — 0.030***(0.008)

Heavy — 0.045***(0.009)

Drug useMarijuana — 0.050***

(0.009)

(continued)




powered between-, random-, and fixed-effects

analyses, and the breadth and heterogeneity of

the samples we use here, have advantages that

make our findings compelling evidence against

a life course–human capital and causal

interpretation.

From a theoretical standpoint, we emphasize

the role of cognitive and noncognitive abilities

because these can clearly be understood as the

engines of educational attainment, and they con-

nect other factors, such as early exposures, family

circumstances, community context, and even early

health profiles, to educational attainment (Haas

2006; Palloni 2006). In doing so, we emphasize

the dynamics of early skill formation, prior to

school entry (Entwisle and Alexander 1993), as

well as the effects of early schooling (Berhman

et al. 2015; Muennig 2015; Smith-Greenaway

2015) and their role in educational attainment

deep into the life course. Together, these frame

the social context of a ‘‘critical period’’ for skill

formation and suggest an alternative life course

model to that which dominates sociological

research (e.g., Hayward et al. 2015; Kirkpatrick

Johnson et al. 2016; Ross and Wu 1995). Our

model also emphasizes the idea of life course con-

tingency and how experiences at particular points

of the life course have unique salience and conse-

quences. At the same time, we cannot and do not

rule out the possibility of compensatory processes.

Ben-Shlomo and Kuh’s (2002) thoughtful over-

view of life course perspectives on chronic disease

epidemiology was careful to describe two critical

period perspectives within the life course frame-

work, depending on the possibility and probability

of compensation through later life events. They

stress that compensation is much more likely

when ‘‘function’’ rather than ‘‘structure’’ is under-

mined. As cognitive and noncognitive skills fall

clearly within the realm of ‘‘function,’’ this is an

important avenue for future research given that

there is no a priori reason to rule out the possibility

that attainments in later life could modify relation-

ships, and there is tantalizing evidence of the life

course variability in the education-health associa-

tion (House, Lantz, and Herd 2005; Ross and Wu

1996). Moreover, although evidence of compensa-

tion for cognitive limitations is not strong, the

same cannot be said for noncognitive skills.

Here, the extant body of research is much smaller

and possible plasticity much greater (see e.g.,

Almlund et al. 2011).

Our work offers a novel view on the complex-

ity of general or routine health management. In her

discussion of intelligence and health, L. Gottfred-

son (2004:189) argues that ‘‘health self-manage-

ment is inherently complex’’ (see also Hummer

and Lariscy 2011). From our perspective, it is

unclear that routine health management is compli-

cated, and we suggest that degree of complexity

increases with extent of illness and length of treat-

ment. This complicates conceptualization because

one is already compromised with respect to health

when one specifies the conditions that are seen to

Table 5.(Continued)

Self-rated Health

FE FE(1) (2)

Other drug — 0.082***(0.014)

Constant 1.809*** 1.700***(0.011) (0.012)

Respondents 88.485 88.485Observations 8.687 8.687





Tab

le6.

Ran

dom

-(R

E)

and

Fixe

d-e

ffec

ts(F

E)

Coef

ficie

nts

:Sel

f-ra

ted

Hea

lth

Reg

ress

edon

Educa

tional

Att

ainm

ent;

Dem

ogr

aphic

,Soci

oec

onom

ic,a

nd

Hea

lth

Bac

kgro

und

Fact

ors

;an

dC

ogn

itiv

ean

dN

onco

gnitiv

eSk

ills,

NLS

Y97

by

Rac

ean

dSe

x.

White

Mal

esW

hite

Fem

ales

Bla

ckM

ales

Bla

ckFe

mal

esH

ispan

icM

ales

His

pan

icFe

mal

es

RE

FER

EFE

RE

FER

EFE

RE

FER

EFE

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

Res

ponden

t’sed

uca

tional

atta

inm

ent

Hig

hsc

hoolgr

aduat

e2

0.0

30

0.0

24

20.0

06

0.0

38

20.0

47

20.0

12

0.0

03

0.0

54

20.0

13

20.0

07

0.0

42

0.0

61

(0.0

21)

(0.0

23)

(0.0

23)

(0.0

26)

(0.0

30)

(0.0

34)

(0.0

33)

(0.0

36)

(0.0

35)

(0.0

40)

(0.0

38)

(0.0

43)

Som

eco

llege

20.0

10

0.0

71***

20.0

24

0.0

11

20.0

08

0.0

34

0.0

07

0.0

75**

20.1

25***

20.0

48

20.0

03

0.0

56

(0.0

18)

(0.0

20)

(0.0

18)

(0.0

20)

(0.0

31)

(0.0

37)

(0.0

29)

(0.0

33)

(0.0

35)

(0.0

41)

(0.0

33)

(0.0

39)

Two-y

ear

deg

ree

20.0

42

0.0

52

20.1

03*

20.0

13

20.1

30

20.0

45

20.0

48

0.0

83

20.1

34

20.0

56

20.1

16

20.0

16

(0.0

41)

(0.0

45)

(0.0

41)

(0.0

46)

(0.0

81)

(0.0

90)

(0.0

65)

(0.0

71)

(0.0

86)

(0.0

95)

(0.0

76)

(0.0

84)

Four

-yea

rdeg

ree

or

more

20.1

13***

0.0

07

20.1

68***

20.0

46

20.0

87

20.0

09

20.1

52***

20.0

11

20.2

42***

20.1

51**

20.1

09

20.0

05

(0.0

28)

(0.0

32)

(0.0

27)

(0.0

31)

(0.0

57)

(0.0

66)

(0.0

48)

(0.0

54)

(0.0

64)

(0.0

74)

(0.0

56)

(0.0

64)

Cogn

itiv

eab

ility

,A

SVA

Bper

centile

(1997)

20th

20.1

05

—2

0.0

87

—2

0.0

10

—0.1

05

—2

0.0

48

—0.0

21

—

(0.0

54)

(0.0

64)

(0.0

55)

(0.0

61)

(0.0

66)

(0.0

73)

40th

20.1

94***

—2

0.2

76***

—2

0.0

41

—0.0

45

—0.0

84

—2

0.1

05

—

(0.0

51)

(0.0

61)

(0.0

65)

(0.0

68)

(0.0

76)

(0.0

73)

60th

20.2

29***

—2

0.2

95***

—2

0.0

56

—2

0.0

37

—2

0.0

59

—2

0.2

55***

—

(0.0

50)

(0.0

59)

(0.0

81)

(0.0

77)

(0.0

87)

(0.0

84)

80th

20.2

74***

—2

0.3

80***

—2

0.1

72

—2

0.1

25

—2

0.1

52

—2

0.2

02*

—

(0.0

50)

(0.0

60)

(0.1

15)

(0.1

00)

(0.0

92)

(0.0

96)

Mis

sing

20.1

69***

—2

0.1

66**

—2

0.0

11

—0.0

57

—0.0

06

—2

0.1

01

—

(0.0

52)

(0.0

62)

(0.0

54)

(0.0

65)

(0.0

65)

(0.0

69)

Schoolco

mm

itm

ent

per

centile

(1997)

20th

0.0

59

—0.0

40

—0.0

17

—2

0.0

44

—2

0.0

41

—0.0

35

—

(0.0

37)

(0.0

39)

(0.0

62)

(0.0

70)

(0.0

71)

(0.0

79)

40th

0.0

55

—0.0

96**

—0.1

52**

—2

0.0

18

—0.0

34

—0.0

10

—

(0.0

34)

(0.0

36)

(0.0

57)

(0.0

59)

(0.0

64)

(0.0

64)

60th

0.1

01**

—0.1

59***

—0.0

89

—0.0

70

—0.0

74

—0.0

53

—

(0.0

36)

(0.0

38)

(0.0

57)

(0.0

63)

(0.0

64)

(0.0

70)

80th

0.2

38***

—0.2

09***

—0.2

31***

—0.0

67

—0.1

86**

—0.1

28

—

(0.0

40)

(0.0

39)

(0.0

60)

(0.0

64)

(0.0

65)

(0.0

70)

Self-

contr

ol(1

997)

Moder

ate

.066**

—0.0

93***

—0.0

18

—0.0

95*

—0.0

65

—0.0

15

—

(.028)

(0.0

28)

(0.0

44)

(0.0

46)

(0.0

50)

(0.0

50)

Low

.189***

—0.2

14***

—0.0

61

—2

0.0

14

—0.0

47

—0.1

72*

—

(.037)

(0.0

44)

(0.0

61)

(0.0

74)

(0.0

66)

(0.0

79)

(con

tinue

d)

197



Tab

le6.

(Co

nti

nu

ed

)

White

Mal

esW

hite

Fem

ales

Bla

ckM

ales

Bla

ckFe

mal

esH

ispan

icM

ales

His

pan

icFe

mal

es

RE

FER

EFE

RE

FER

EFE

RE

FER

EFE

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

Ver

ylo

w.2

40***

—0.2

64***

—0.1

38

—0.1

35

—0.2

51***

—0.0

70

—

(.047)

(0.0

74)

(0.0

79)

(0.1

61)

(0.0

85)

(0.1

40)

Task

-ori

ente

dper

centile

(2002)

20th

2.0

77

—2

0.0

42

—2

0.2

19**

—0.0

61

—2

0.2

05*

—0.0

32

—

(.051)

(0.0

64)

(0.0

84)

(0.0

97)

(0.0

98)

(0.0

91)

40th

2.1

76***

—2

0.0

73

—2

0.1

55*

—0.0

55

—2

0.1

99**

—2

0.0

20

—

(.042)

(0.0

48)

(0.0

68)

(0.0

74)

(0.0

83)

(0.0

80)

60th

2.2

12***

—2

0.0

98

—2

0.1

76

—2

0.0

31

—2

0.2

43*

—2

0.0

97

—

(.060)

(0.0

60)

(0.1

01)

(0.1

08)

(0.1

09)

(0.1

09)

80th

2.2

41***

—2

0.1

51**

—2

0.2

22***

—2

0.1

11

—2

0.3

19***

—2

0.0

79

—

(.055)

(0.0

53)

(0.0

86)

(0.0

89)

(0.1

06)

(0.0

98)

Mis

sing

2.2

95

—2

0.5

04

—2

0.3

65

—0.3

74

—2

0.6

42

—2

0.5

39

(.276)

(0.3

12)

(0.3

43)

(0.4

48)

(0.3

43)

(0.4

26)

Soci

abili

typer

centile

(2002)

20th

2.1

08**

—2

0.1

36***

—0.0

32

—2

0.1

45

—2

0.1

16

—0.0

14

—

(.045)

(0.0

48)

(0.0

78)

(0.0

80)

(0.0

85)

(0.0

77)

40th

2.1

00

—2

0.1

46**

—0.0

43

—2

0.0

79

—0.0

22

—2

0.2

06*

—

(.052)

(0.0

57)

(0.0

89)

(0.0

97)

(0.1

02)

(0.0

98)

60th

2.1

85***

—2

0.1

27*

—2

0.0

12

—2

0.0

71

—2

0.0

85

—2

0.2

16*

—

(.050)

(0.0

51)

(0.0

80)

(0.0

85)

(0.0

95)

(0.0

85)

80th

2.1

63***

—2

0.2

81***

—2

0.0

95

—2

0.2

01*

—2

0.1

80

—2

0.2

97**

—

(.059)

(0.0

59)

(0.0

84)

(0.0

89)

(0.1

07)

(0.1

08)

Mis

sing

2.0

66

—0.1

35

—0.1

46

—2

0.4

36

—0.3

23

—0.5

52

—

(.278)

(0.3

17)

(0.3

47)

(0.4

67)

(0.3

52)

(0.4

35)

Res

ponden

ts2,0

64

2,0

40

1,9

25

1,9

02

942

939

926

920

735

732

693

688

Obse

rvat

ions

28,0

91

26,0

27

26,5

83

24,6

58

13,1

81

12,2

39

13,6

44

12,7

18

10,0

70

9,3

35

9,9

27

9,2

34

Sour

ce.N

atio

nal

Longi

tudin

alSu

rvey

of

Youth

-1997.Sa

mple

isre

stri

cted

tore

sponden

tsw

ith

no

mis

sing

dat

aon

the

full

cova

riat

ese

t.N

ote.

Dat

aar

eunw

eigh

ted.S

tandar

der

rors

inpar

enth

eses

.For

random

-effec

tssp

ecifi

cations,

allm

odel

sin

clude

contr

olv

aria

ble

sfo

rag

ing,

cohort

,fam

ilyso

cioec

onom

icst

atus,

and

earl

yhea

lth

stat

us.

*p

\.0

5.**p

\.0

1.***p

\.0

01.

198



produce better or worse health (e.g., comprehen-

sion and compliance with physician recommenda-

tions). For the majority of the population that

spends most of its time free of complex diseases,

the basics of diet, physical activity, and avoidance

of risk behaviors and situations do not seem to

require high levels of cognitive ability and would

be mitigated by even moderate levels of self-con-

trol. If this is true, then basic cognitive and non-

cognitive skills acquired in childhood may be

more than sufficient for routine health mainte-

nance (see Behrman et al. 2015; Smith-Greenway

2015).

Some might claim that we overstate the case to

which sociologists interested in the education-

health relationship are ignorant of the importance

of skills and the endogeneity of educational attain-

ment. Sociologists are not naıve to the importance

of skills (see the excellent discussion in Farkas

2003), but it is difficult to find sociological state-

ments on education and health that accord them

even a minimal role, and articulations in other

fields, such as demography, economics, epidemi-

ology, and psychology, are neither vast nor well

developed. Moreover, when such abilities are con-

sidered, they are often bundled in with other child-

hood ‘‘conditions’’ or ‘‘exposures’’ rather than

viewed as the specific pathway that links such

exposures to educational attainment and subse-

quent health over the life course. Equally impor-

tant, researchers often simply ascribe causal status

to educational attainment and then estimate mod-

els that do nothing to control for cognitive and

noncognitive abilities. At minimum, our work sug-

gests this is a problematic and potentially mislead-

ing situation. Abilities, cognitive and noncogni-

tive, deserve more attention than they currently

receive, and researchers studying education and

health cannot claim causal effects without

accounting for such factors. Even more generally,

scholars should be particularly hesitant and cir-

cumspect about causal claims when models

include few control variables, model only

between-effects, or simply show heterogeneity in

the education-health relationship across context,

group, or outcome (cf. Montez and Friedman

2015).

Still, population science and public policy are

not entirely dependent on causality, and the find-

ings from conventional analyses are valuable in

their own right. People with lower educational

attainment have a greater likelihood of health

problems and early mortality. As such, educational

attainment, a variable easily and typically mea-

sured in a wide range of health data, provides an

important lens into social disparities in health

regardless of its causal status. Still, if our findings

generalize, manipulations to education, at either

an individual or a system level, will not yield sig-

nificant improvements in population health unless

they specifically enhance stocks of skills (cf.

Galea et al. 2011; Lleras-Muney 2005; Montez

and Friedman 2015; Schoeni et al. 2008). Given

this, policies that emphasize early life interven-

tions to augment skills will likely have particularly

strong payoffs (see discussion in Muennig 2015),

a conclusion consistent with widespread evidence

of the significance of primary education and con-

sequent literacy for population health (e.g., Baker

et al. 2011; Behrman et al. 2015; Smith-Greenway

2015). Recent research points to the salience of

early childhood education (ECE) for skill develop-

ment and educational attainment. In the past

decade, a number of evaluation studies have

examined the relationship between early child-

hood education and educational achievement

among participants in large- and small-scale pub-

lic education programs with a preschool compo-

nent (e.g., Head Start, the Chicago Child-Parent

Centers, High/Scope Perry Preschool Program,

and the Carolina Abecedarian Project). In particu-

lar, high-quality preschool program participation

is linked to lower rates of special education place-

ment and lower rates of grade retention (e.g.,

Campbell and Ramey 2010; A. J. Reynolds et al.

2007). Given this, interventions that focus on early

life and target cognitive and noncognitive skill

acquisition might have important implications

for health dynamics over the life course, particu-

larly in contexts where familial human capital is

low (A. J. Reynolds et al. 2011).

We recognize that our work is not a global test

of the role of education for health dynamics. As

noted earlier, there is evidence that the association

between educational attainment and health varies

across the life course, both in magnitude and per-

ceived causal mechanisms (House et al. 2005). By

necessity, our research focuses on a particular

slice of the life course and, while useful for the

questions we pursue, might not generalize to the

entire life course. With the age range we study,

we do not capture the entirety of variance in

SRH, given that a number of diseases and condi-

tions onset or manifest only at later ages. To

understand this better, we examined a similar

time span for the original cohorts of the Health




and Retirement Surveys (HRS), spanning 1992 to

2006, and produced the relevant variance compo-

nents for self-rated health based on an uncondi-

tional model (see Figure 2). Here, the total vari-

ance is larger in the HRS, approximately 1.4 for

both the younger (ages 50 to 59) and older (ages

60 to 69) subsamples, in comparison to .91 for

the NLSY97 sample. At the same time, within-

variances are remarkably similar across samples,

approximately .50, and hence the differences in

variance are derived from the between-variances

(.89 and .86 for the HRS samples, .36 for the

NLYS97 sample). Clearly, the NLSY97 data do

not capture the full range of variance in self-rated

health over the life course, and this is a limitation

of the research. We would also expect that differ-

ent diseases and conditions have unique variance

structures over the life course, as well as structural

determinants (Ben-Shlomo and Kuh 2002), and

this too is a limitation of our work. In the end,

we hope our test of the education-health relation-

ship and identification of the underacknowledged

importance of cognitive and noncognitive skills

is provocative enough to spark future research

on skills at different phases of the life course

and for different conditions.

In the end, our research will not, and should

not, be the final word on the life course–human

capital explanation of education and health or on

whether the typically observed association

between educational attainment and health is

indeed causal. Indeed, our work remains below

the bar for claims of pure causal effects, and we

recognize that our measures of cognitive and non-

cognitive abilities are themselves endogenous to

genetic influences, family circumstances and rela-

tionships (see discussion in Cunha, Heckman, and

Schennach 2010), and even early schooling (Behr-

man et al. 2015; Muennig 2015; Smith-Greenway

2015). Yet, our research still challenges conven-

tional views of how we should think about the

association between educational attainment and

health, why it exists, and what it means. In partic-

ular, we provide an explanation of why associa-

tional models show such strong effects of educa-

tional attainment, whereas natural experiments

and twin studies are much more equivocal, given

our emphasis on skills and the fact that the latter

approaches never stratify on such things. At min-

imum, we need to consider the possibility that

dominant etiological frameworks that link educa-

tion to health may require rethinking, examining

what dimensions of health may have causal rela-

tions with educational attainment, and designing

research that identifies and measures other, poten-

tially new factors that may link social position to

health over the life course. Future studies of the

social and psychological dynamics that yield mas-

sive health disparities and diminished life expec-

tancies should focus on the nexus of early family

context and early years of schooling and how these

constitute a critical period for the development of

Figure 2. Variance decomposition: comparison of National Longitudinal Survey of Youth-1997, 1997 to2013, and younger (ages 50-59) and older cohorts (ages 60-69) drawn from the Health and RetirementSurveys, 1992–2006.




self-reproducing cognitive and noncognitive abili-

ties that are powerful engines of educational

attainment and are implicated in heterogeneity in

health over the life course.

ACKNOWLEDGMENTS

The authors thank Joshua Angrist, Eric Grodsky,

Andrew Halpern-Manners, Mark Hayward, and Ryan

Masters for helpful discussions throughout this research.

The authors also thank the reviewers and editor of Soci-

ology of Education for helpful comments on the

manuscript.

RESEARCH ETHICS

The research solely involved secondary analysis of pub-

licly available data and hence no special or secondary

human subjects issues apply. All human subjects gave

informed consent prior to their participation in the

research, and adequate steps were taken to protect partic-

ipants’ confidentiality.The authors contributed equally to

the production of the paper.

FUNDING

The author(s) disclosed receipt of the following financial

support for the research, authorship, and/or publication

of this article: The authors received support during this

research from the Consortium of Child, Youth, and Fam-

ily—University of Minnesota, the Dondena Centre for

Research on Social Dynamics and Public Policy—Boc-

coni University, the National Science Foundation

(SES-0962310), and the National Institute of Child

Health and Human Development (5R01HD061622-02,

PI: MJ Shanahan).

NOTES

1. Given that the measurement window stretches

beyond high school, we reanalyzed all models

restricting the sample to cohorts for whom cognitive

and noncognitive abilities were measured at or before

age 17. Our conclusions do not change; thus, we opt

for the larger, more representative sample.

2. Nine percent of respondents were still in school at the

final wave of data. The majority of these respondents

would be classified as some college (70.5 percent) or

four-year degree or greater (26.5 percent); problems

of right-censoring thus should not bias our results

(i.e., these respondents already have high educational

attainment).

3. Variation in statistical significance is influenced by

differences in the distribution of races across quin-

tiles and resulting variation in statistical power. We

did not recalibrate quintiles within sex and race

because this would reduce reliability in the variables.

REFERENCES

Adams, Peter, Michael D. Hurd, Daniel McFadden,

Angela Merrill, and Tiago Ribeiro. 2003. ‘‘Healthy,

Wealthy, and Wise? Tests for Direct Causal Paths

between Health and Socioeconomic Status.’’ Journal

of Econometrics 112(1):3–56.

Adler, Nancy E., Thomas Boyce, Margaret A. Chesney,

Sheldon Cohen, Susan Folkman, Robert L. Kahn,

and S. Leonard Syme. 1994. ‘‘Socioeconomic Status

and Health: The Challenge of the Gradient.’’ Ameri-

can Psychologist 49(1):15.

Albouy, Valerie, and Laurent Lequien. 2009. ‘‘Does

Compulsory Education Lower Mortality?’’ Journal

of Health Economics 28(1):155–68.

Alexander, Karl L., Doris R. Entwisle, Dale A. Blyth,

and Harriett P. McAdoo. 1988. ‘‘Achievement in the

First 2 Years of School: Patterns and Processes.’’

Monographs of the Society for Research in Child

Development, i–157.

Alexander, Karl L., Doris R. Entwisle, and Susan L.

Dauber. 1993. ‘‘First Grade Classroom Behavior:

Its Short- and Long-term Consequences for School

Performance.’’ Child Development 64(3):801–14.

Alexander, Karl L., Doris R. Entwisle, and Carrie S.

Horsey. 1997. ‘‘From First Grade forward: Early

Foundations of High School Dropout.’’ Sociology of

Education 70(2):87–107.

Allison, Paul. 2009. Fixed Effects Regression Models.

Thousand Oaks, CA: Sage.

Almlund, Mathilde, Angela L. Duckworth, James J.

Heckman, and Tim Kautz. 2011. ‘‘Personality Psy-

chology and Economics.’’ Handbook of the Econom-

ics of Education 4:1–181.

Amin, Vikesh, Jere R. Behrman, and Hans Peter Kohler.

2015. ‘‘Schooling Has Smaller or Insignificant

Effects on Adult Health in the US Than Suggested

by Cross-sectional Associations: New Estimates Using

Relatively Large Samples of Identical Twins.’’ Social

Science & Medicine 127:181–89.

Arendt, Jacob N. 2005. ‘‘Does Education Cause Better

Health? A Panel Data Analysis Using School

Reforms for Identification.’’ Economics of Education

Review 24(2):149–60.

Baker, David P., Juan Leon, Emily G. Smith Greenaway,

John Collins, and Marcela Movit. 2011. ‘‘The Edu-

cation Effect on Population Health: A Reassess-

ment.’’ Population and Development Review

37(2):307–32.

Bauldry, Shaun, Michael J. Shanahan, Jason Boardman,

Richard Miech, and Ross Macmillan. 2012. ‘‘A Life

Course Model of Self-rated Health through Adoles-

cence and young Adulthood.’’ Social Science and

Medicine 75:1311–20.




Becker, Gary S. 2009. Human Capital: A Theoretical

and Empirical Analysis, with Special Reference to

Education. Chicago, IL: University of Chicago Press.

Beckett, Celia, Barbara Maughan, Michael Rutter, Jenny

Castle, Emma Colvert, Christine Groothues, Jana

Kreppner, Suzanne Stevens, Thomas G. O’Connor,

and Edmund J.S. Sonuga-Barke. 2006. ‘‘Do the

Effects of Early Severe Deprivation on Cognition

Persist into Early Adolescence? Findings from the

English and Romanian Adoptees Study.’’ Child

Development 77(3):696–711.

Behrman, Jere R., Hans-Peter Kohler, Vibeke Myrup

Jensen, Dorthe Pedersen, Inge Petersen, Paul Bing-

ley, and Kaare Christensen. 2011. ‘‘Does More

Schooling Reduce Hospitalization and Delay Mortal-

ity? New Evidence Based on Danish Twins.’’

Demography 48(4):1347–75.

Behrman, Jere R., Yanyan Xiong, and Junsen Zhang.

2015. ‘‘Cross-sectional Schooling-health Associa-

tions Misrepresented Causal Schooling Effects on

Adult Health and Health-related Behaviors: Evi-

dence from the Chinese Adults Twins Survey.’’

Social Science & Medicine 127:190–97.

Ben-Shlomo, Yoav, and Diana Kuh. 2002. ‘‘A Life

Course Approach to Chronic Disease Epidemiology:

Conceptual Models, Empirical Challenges and Inter-

disciplinary Perspectives.’’ International Journal of

Epidemiology 31:285–93.

Boardman, Jason, and Jason Fletcher. 2015. ‘‘To Cause

or Not to Cause? That Is the Question, but Identical

Twins Might Not Have All of the Answers.’’ Social


Bureau of Labor Statistics, U.S. Department of Labor.

2013. National Longitudinal Survey of Youth 1997

cohort, 1997–2011 (rounds 1–15). Produced by the

National Opinion Research Center, the University

of Chicago and distributed by the Center for Human

Resource Research, The Ohio State University.

Columbus, OH.

Campbell, Francis A., and Craig T. Ramey. 2010. ‘‘Car-

olina Abecedarian Project.’’ Pp. 76–98 in Childhood

Programs and Practices in the First Decade of Life:

A Human Capital Integration, edited by A. J. Rey-

nolds, A. J. Rolnick, M. M. Englund, and J. A. Tem-

ple. New York: Cambridge University Press.

Carneiro, Pedro, Claire Crawford, and Alissa Goodman

2007. The Impact of Early Cognitive and Non-cogni-

tive Skills on Later Outcomes. London: Centre for the

Economics of Education, London School of

Economics.

Carroll, John B. 1992. ‘‘Cognitive Abilities: The State of

the Art.’’ Psychological Science 3(5):266–70.

Caspi, Avshalom, Dot Begg, Nigel Dickson, HonaLee

Harrington, John Langley, Terrie E. Moffitt, and

Phil A. Silva. 1997. ‘‘Personality Differences Predict

Health-risk Behaviors in Young Adulthood: Evi-

dence from a Longitudinal Study.’’ Journal of Per-

sonality and Social Psychology 73(5):1052–63.

Clark, Damon, and Heather Royer. 2010. ‘‘The Effect of

Education on Adult Health and Mortality: Evidence

from Britain.’’ Working Paper No. 16013, NBER,

Cambridge, MA.

Conti, Gabriela, James J. Heckman, and Sergio Urzua.

2010. ‘‘The Education-Health Gradient.’’ American

Economic Review, 100(2):234–38.

Contoyannis, Paul, Andrew M. Jones, and Nigel Rice.

2004. ‘‘The Dynamics of Health in the British House-

hold Panel Survey.’’ Journal of Applied Economet-

rics 19(4):473–503.

Cunha, Flavio, and James J. Heckman. 2007. ‘‘The Tech-

nology of Skill Formation.’’ American Economic

Review 97(2):31–47.

Cunha, Flavio, James J. Heckman, Lance Lochner, and

Dimitriy V. Materov. 2006. ‘‘Interpreting the Evi-

dence on Life Cycle Skill Formation.’’ Pp. 697–812

in Handbook of the Economics of Education, edited

by F. Welch and E. A. Hanushek. Amsterdam:

North-Holland Press.

Cunha, Flavio, James J. Heckman, and Susanne M.

Schennach. 2010. ‘‘Estimating the Technology of

Cognitive and Noncognitive Skill Formation.’’ Econ-

ometrica 78(3):883–931.

Cutler, David M., Wei Huang, and Adriana Lleras-Mu-

ney. 2015. ‘‘When Does Education Matter? The Pro-

tective Effect of Education for Cohorts Graduating in

Bad Times.’’ Social Science & Medicine 127:63–73.

Cutler, David M., and Adriana Lleras-Muney. 2008.

‘‘Education and Health: Evaluating Theories and

Evidence.’’ Pp. 29–60 in Making Americans Health-

ier: Social and Economic Policy as Health Policy,

edited by R. F. Schoeni, J. S. House, G. A. Kaplan,

and H. Pollack. New York: Russell Sage Foundation.

Deary, Ian J., Lawrence J. Whalley, Helen Lemmon, J.

R. Crawford, and John M. Starr. 2000. ‘‘The Stability

of Individual Differences in Mental Ability from

Childhood to Old Age: Follow-up of the 1932 Scot-

tish Mental Survey.’’ Intelligence 28(1):49–55.

Deaton, Angus. 2001. ‘‘Relative Deprivation, Inequality

and Mortality.’’ Working Paper No. 6, Princeton,

Center for Health and Wellbeing.

DiPrete, Thomas A., and Gregory M. Eirich. 2006.

‘‘Cumulative Advantage as a Mechanism for

Inequality: A Review of Theoretical and Empirical

Developments.’’ Annual Review of Sociology 32:

271–97.

Doherty, Geoffrey D. 1997. ‘‘Quality, Standards, the

Consumer Paradigm and Developments in Higher

Education.’’ Quality Assurance in Education 5(4):

239–48.

Elder, Glen H. 1994. ‘‘Time, Human Agency, and Social

Change: Perspectives on the Life Course.’’ Social

Psychology Quarterly 57(1): 4–15.

Elder, Glen H. 1998. ‘‘The Life Course as Developmen-

tal Theory.’’ Child Development, 69(1):1–12.

Entwisle, Doris R., and Karl L. Alexander. 1993. ‘‘Entry

into School: The Beginning School Transition and




Educational Stratification in the United States.’’

Annual Review of Sociology 19:401–23.

Entwisle, Doris R., Karl L. Alexander, and Linda S.

Olson. 1997. Children, Schools, & Inequality. New

York: Walter de Gruyter, Inc.

Entwisle, Doris R., Karl L. Alexander, and Linda S.

Olson. 2005. ‘‘First Grade and Educational Attain-

ment by Age 22: A New Story.’’ American Journal

of Sociology 110(5):1458–502.

Evans, T. David, Francis T. Cullen, Velmer S. Burton, R.

Gregory Dunaway, and Michael L. Benson. 1997. ‘‘The

Social Consequences of Self-control: Testing the Gen-

eral Theory of Crime.’’ Criminology 35:475–504.

Farkas, George. 2003. ‘‘Cognitive Skills and Noncogni-

tive Traits and Behaviors in Stratification Pro-

cesses.’’ Annual Review of Sociology, 29:541–62.

Farkas, George., Robert P. Grobe, Daniel Sheehan, and

Yuan Shuan. 1990. ‘‘Cultural Resources and School

Success: Gender, Ethnicity, and Poverty Groups

within an Urban School District.’’ American Socio-

logical Review 55(1):127–42.

Ferraro, Kenneth F., and Melissa M. Farmer. 1999.

‘‘Utility of Health Data from Social Surveys: Is

There a Gold Standard for Measuring Morbidity?’’

American Sociological Review 64(2):303–15.

Fletcher, Jason. 2015. ‘‘New Evidence of the Effects of

Education on Health in the US: Compulsory School-

ing Laws Revisited.’’ Social Science & Medicine

127:101–07.

Fosse, Nathan E., and Steven A. Haas. 2009. ‘‘Validity

and Stability of Self-reported Health among Adoles-

cents in a Longitudinal, Nationally Representative

Survey.’’ Pediatrics 123:e496–501.

Fryer, Roland G. Jr., and Steven D. Levitt. 2004.

‘‘Understanding the Black-White Test Score Gap in

the First Two Years of School.’’ The Review of Eco-

nomics and Statistics 86(2):447–64.

Fuchs, Victor R. 2011. Who Shall Live?: Health, Eco-

nomics and Social Choice. River Edge, NJ: World

Scientific.

Fujiwara, Takeo, and Ichiro Kawachi. 2009. ‘‘Is Educa-

tion Causally Related to Better Health? A Twin Fix-

ed-effect Study in the USA.’’ International Journal

of Epidemiology 38(5):1310–22.

Galea, Sandro, Melissa Tracy, Katherine J. Hoggatt,

Charles DiMaggio, and Adam Karpati. 2011. ‘‘Esti-

mated Deaths Attributable to Social Factors in the

United States.’’ American Journal of Public Health

101(8):1456–65.

Gathmann, Christina, Hendrik Jurges, and Steffen Rein-

hold. 2015. ‘‘Compulsory Schooling Reforms, Edu-

cation and Mortality in Twentieth Century Europe.’’

Social Science & Medicine 127:74–82.

Gottfredson, Linda. 2004. ‘‘Intelligence: IS it the Epi-

demiologists’ Elusive ‘Fundamental Cause’ of Social

Class Inequalities in Health?’’ Journal of Social and

Personality Psychology 86(1):174–99.

Gottfredson, Michael R., and Travis Hirschi. 1990. A

General Theory of Crime. Stanford, CA: Stanford

University Press.

Grossman, Michael. 1972. ‘‘On the Concept of Health

Capital and the Demand for Health.’’ Journal of

Political Economy 80(2):223–55.

Haas, Steven. 2006. ‘‘Health Selection and the Process of

Social Stratification: The Effect of Childhood Health

on Socioeconomic Attainment.’’ Journal of Health

and Social Behavior 47(4):339–54.

Halaby, Charles. 2004. ‘‘Panel Models in Sociological

Research: Theory into Practice.’’ Annual Review of

Sociology 30:507–44.

Halliday, Timothy J. 2008. ‘‘Heterogeneity, State Depen-

dence and Health.’’ Econometrics Journal 11(3):

499–516.

Harris, Kathleen. 2010. ‘‘An Integrative Approach to

Health.’’ Demography 47(1):1–22.

Harris, Angel L., and Keith Robinson. 2007. ‘‘Schooling

Behaviors or Prior Skills? A Cautionary Tale of

Omitted Variable Bias within Oppositional Culture

Theory.’’ Sociology of Education 80:139–57.

Hayward, Mark D., Robert A. Hummer, and Isaac Sas-

son. 2015. ‘‘Trends and Group Differences in the

Association between Educational Attainment and

U.S. Adult Mortality: Implications for Understanding

Education’s Causal Influence.’’ Social Science &


Heckman, James J. 2000. ‘‘Policies to Foster Human

Capital.’’ Research in Economics 54(1):3–56.

Heckman, James J. 2006. ‘‘Skill Formation and the Eco-

nomics of Investing in Disadvantaged Children.’’

Science 312(5782):1900–02.

Herrnstein, Richard J., and Charles Murray. 2010. Bell

Curve: Intelligence and Class Structure in American

Life. New York, NY: Simon and Schuster.

Hopkins, Kenneth D., and Glenn H. Bracht. 1975. ‘‘Ten-

year Stability of Verbal and Nonverbal IQ Scores.’’

American Educational Research Journal 12(4):

469–77.

House, James S., Paula M. Lantz, and Pamela Herd.

2005. ‘‘Continuity and Change in the Social Stratifi-

cation of Aging and Health over the Life Course:

Evidence from a Nationally Representative Longitu-

dinal Study from 1986 to 2001/2002 (Americans’

Changing Lives Study).’’ The Journals of Gerontol-

ogy Series B: Psychological Sciences and Social Sci-

ences 60(Special Issue 2):S15–26.

Hummer, Robert A., and Joseph T. Lariscy. 2011. ‘‘Edu-

cational Attainment and Adult Mortality.’’ Pp.

241–61 in International Handbook of Adult Mortal-

ity, edited by R. G. Rogers and E. M. Crimmins.

New York: Springer.

Idler, Ellen L., and Yael Benyamini. 1997. ‘‘Self-rated

Health and Mortality: A Review of Twenty-seven

Community Studies.’’ Journal of Health and Social

Behavior 38(1):21–37.




Ingram, Sara, Lily Hechtman, and Gert Morgenstern.

1999. ‘‘Outcome Issues in ADHD: Adolescent and

Adult Long-term Outcome.’’ Mental Retardation

and Developmental Disabilities Research Reviews

5(3):243–50.

Janus, Magdalena, and Eric Duku. 2007. ‘‘The School

Entry Gap: Socioeconomic, Family, and Health Fac-

tors Associated with Children’s School Readiness to

Learn.’’ Early Education and Development 18(3):

375–403.

Jemal, Ahmedin, Michael J. Thun, Elizabeth E. Ward, S.

Jane Henley, Vilma E. Cokkinides, and Taylor E.

Murray. 2008. ‘‘Mortality from Leading Causes by

Education and Race in the United States, 2001.’’

American Journal of Preventive Medicine 34(1):1–8.

Jurges, Heinrik, Eberhart Kruk, and Steffen Reinhold.

2013. ‘‘The Effect of Compulsory Schooling on

Health—Evidence from Biomarkers.’’ Journal of

Population Economics 26(2):645–72.

Kirkpatrick Johnson, Monica, Jeremy Staff, John Schu-

lenberg, and M. Patrick. 2016. ‘‘Living Healthier

and Longer: A Life Course Perspective on Education

and Health. Connections between Childhood and

Adulthood.’’ Pp. 369–88 in Handbook of the Life

Course. 2nd ed., edited by M. J. Shanahan, J. T. Mor-

timer, and M. Kirkpatrick Johnson. New York:

Springer.

Kolb, Brian, and Ian Q. Whishaw. 2008. Fundamentals

of Human Neuropsychology. New York, NY: Worth

Publishers.

Lee, Valerie E., and David T. Burkam. 2002. Inequality

at the Starting Gate: Social Background Differences

in Achievement as Children Begin School. Washing-

ton, DC: Economic Policy Institute.

Lillard, Dean R., and Eamon Molloy. 2010. ‘‘Live and

Learn or Learn and Live: Does Education Lead to

Longer Lives?’’ Working Paper, Cornell University.

Link, Bruce G., and Jo C. Phelan. 1995. ‘‘Social Condi-

tions as Fundamental Causes.’’ Journal of Health and

Social Behavior 35(Extra Issue):80–94.

Link, Bruce G., Jo C. Phelan, Richard Miech, and Emily

L. Westin. 2008. ‘‘The Resources That Matter: Fun-

damental Social Causes of Health Disparities and the

Challenge of Intelligence.’’ Journal of Health and

Social Behavior 49(1):72–91.

Lleras-Muney, Adriana. 2005. ‘‘The Relationship

between Education and Adult Mortality in the United

States.’’ Review of Economic Studies 72(1):189–221.

Lundborg, Petter. 2008. The Health Returns to Educa-

tion. What Can We Learn from Twins? (IZA Discus-

sion Paper No. 3399). Bonn, Germany: Institute for

the Study of Labor.

Mackenbach, Johan P., Ivana Kulhanova, Matthias

Bopp, Patrick Deboosere, Terge A. Eikemo, Rasmus

Hoffmann, Margarete C. Kulik, Mall Leinsalu, Pekka

Martikainen, Gwen Menvielle, Enrique Regidor,

Bogdan Wojtyniak, and Olof Ostergren, and Olle

Lundberg for the EURO-GBD-SE Consortium.

2015. ‘‘Variations in the Relation between Education

and Cause-specific Mortality in 19 European Popula-

tions: A Test of the ‘Fundamental Causes’ Theory of

Social Inequalities in Health.’’ Social Science &


Madsen, Mia, Anne-Marie N. Andersen, Kaare Christen-

sen, Per K. Andersen, and Marete Osler. 2010. ‘‘Does

Educational Status Impact Adult Mortality in Den-

mark? A Twin Approach.’’ American Journal of Epi-

demiology 172(2):225–34.

Madsen, Mia, and Marete Osler. 2009. ‘‘Commentary:

Strengths and Limitations of the Discordant Twin-

pair Design in Social Epidemiology. Where Do We

go from Here?’’ International Journal of Epidemiol-

ogy 38:1322–23.

Marmot, Michael. 2005. ‘‘Social Determinants of Health

Inequalities.’’ The Lancet 365(9464):1099–104.

Masters, Ryan K., Bruce G. Link, and Jo C. Phelan.

2015. ‘‘Trends in Education Gradients of ‘Prevent-

able’ Mortality: A Test of Fundamental Cause The-

ory.’’ Social Science & Medicine 127:19–28.

Mazumder, Bhaskar. 2008. ‘‘Does Education Improve

Health? A Reexamination of the Evidence from

Compulsory Schooling Laws.’’ Economic Perspec-

tives 32(2):1–16.

McCoy, Ann R., and Arthur J. Reynolds. 1999. ‘‘Grade

Retention and School Performance: An Extended

Investigation.’’ Journal of School Psychology 37(3):

273–98.

McLeod, Jane D., and Elbert P. Almazan. 2003. ‘‘Con-

nections between Childhood and Adulthood.’’ Pp.

391–411 in Handbook of the Life Course, edited by

J. T. Mortimer and M. J. Shanahan. New York,

NY: Springer.

Miech, Richard, Fred Pampel, Jinyoung Kim, and

Richard G. Rogers. 2011. ‘‘The Enduring Associa-

tion between Education and Mortality: The Role of

Widening and Narrowing Disparities.’’ American

Sociological Review 76(6):913–34.

Mirowsky, John, and Catherine Ross. 1998. ‘‘Education,

Personal Control, Lifestyle and Health: A Human

Capital Hypothesis.’’ Research on Aging 20(4):

415–49.

Mirowsky, John, and Catherine Ross. 2003. Education,

Social Status, and Health. New York: Walter de

Gruyter.

Mirowsky, John, and Catherine Ross. 2008. ‘‘Education

and Self-rated Health: Cumulative Advantage and

Its Rising Importance.’’ Research on Aging 30(1):

93–122.

Moffitt, Terrie E., Louise Arseneault, Daniel Belsky,

Nigel Dickson, Robert J. Hancox, HonaLee Harring-

ton, Renate Houts, Richie Poulton, Brent W. Rob-

erts, Steven Ross, and Malcolm R. Sears. 2011.

‘‘A Gradient of Childhood Self-control predicts

Health, Wealth, and Public Safety.’’ Proceedings

of the National Academy of Sciences 108(7):

2693–98.




Montez, Jennifer K., and Esther M. Friedman. 2015.

‘‘Educational Attainment and Adult Health: Under

What Conditions Is the Association Causal?’’ Social


Muennig, Peter. 2015. ‘‘Can Universal Pre-kindergarten

Programs Improve Population Health and Longev-

ity? Mechanisms, Evidence, and Policy Implica-

tions.’’ Social Science & Medicine 127:116–23.

Newport, Elissa L. 2002. ‘‘Critical Periods in Language

Development.’’ Pp. 737–40 in Encyclopedia of Cog-

nitive Science, edited by L. Nadel. New York: Wiley.

Oreopoulos, Phillip. 2006. ‘‘Estimating Average and

Local Average Treatment Effects of Education

When Compulsory Schooling Laws Really Matter.’’

American Economic Review 96(1):152–75.

Palloni, Alberto. 2006. ‘‘Reproducing Inequalities: Luck,

Wallets, and the Enduring Effects of Childhood

Health.’’ Demography 43(1):587–615.

Pearlin, Leonard, and Carmi Schooler. 1978. ‘‘The Struc-

ture of Coping.’’ Journal of Health and Social

Behavior 19(1): 2–21.

Powdthavee, Nattavedhu. 2010. ‘‘Does Education

Reduce the Risk of Hypertension? Estimating the

Biomarker Effect of Compulsory Schooling in Eng-

land.’’ Journal of Human Capital 4(2):173–202.

Preston, Samuel H., and Paul Taubman. 1994. ‘‘Socio-

economic Differences in Adult Mortality and Health

Status.’’ Demography of aging 1:279–318.

Ramsden, Sue, Fiona M. Richardson, Goulven Josse,

Michael SC Thomas, Caroline Ellis, Clare Shake-

shaft, Mohamed L. Seghier, and Cathy J. Price.

2011. ‘‘Verbal and Non-verbal Intelligence Changes

in the Teenage Brain.’’ Nature 479(7371):113–16.

Reynolds, Arthur J. 1992. ‘‘Grade Retention and School

Adjustment: An Explanatory Analysis.’’ Educational

Evaluation and Policy Analysis 14(2):101–21.

Reynolds, Arthur J., Judy A. Temple, Suh-Ruu Ou, Irma

A. Arteaga, and Barry A.B. White. 2011. ‘‘School-

based Early Childhood Education and Age-28 Well-

being: Effects by Timing, Dosage, and Subgroups.’’

Science 333(6040):360–64.

Reynolds, Arthur J., Judy A. Temple, Suh-Ruu Ou,

Dylan L. Robertson, Joshua P. Mersky, James W.

Topitzes, and Michael D. Niles. 2007. ‘‘Effects of

a School-based, Early Childhood Intervention on

Adult Health and Well-being: A 19-year Follow-up

of Low-income Families.’’ Archives of Pediatrics &

Adolescent Medicine 161(8): 730–39.

Reynolds, John R., and Catherine E. Ross. 1998. ‘‘Social

Stratification and Health: Education’s Benefit

beyond Economic Status and Social Origins.’’ Social

Problems 45(2): 221–47.

Roberts, Brent W., and Wendy F. DelVecchio. 2000.

‘‘The Rank-order Consistency of Personality Traits

from Childhood to Old Age: A Quantitative Review

of Longitudinal Studies.’’ Psychological Bulletin

126(1):3–25.

Rogers, Richard G., Bethany G. Everett, Anna Zajacova,

and R. A. Hummer. 2010. ‘‘Educational Degrees and

Adult Mortality Risk in the United States.’’ Biode-

mography and Social Biology 56(1):80–99.

Ross, Catherine E., and Chia-ling Wu. 1995. ‘‘The Links

between Education and Health.’’ American Sociolog-

ical Review 60(5):719–45.

Ross, Catherine E., and Chia-ling Wu. 1996. ‘‘Education,

Age, and the Cumulative Advantage in Health.’’

Journal of Health and Social Behavior 37(1):

104–20.

Rutter, Michael L., Jana M. Kreppner, and Thomas G.

O’Connor. 2001. ‘‘Specificity and Heterogeneity in

Children’s Responses to Profound Institutional Pri-

vation.’’ The British Journal of Psychiatry 179(2):

97–103.

Schoeni, Robert F., James S. House, George A. Kaplan,

and Harold Pollack. 2008. Making Americans

Healthier: Social and Economic Policy as Health

Policy. New York: Russell Sage Foundation.

Schuerger, James M., and Anita C. Witt. 1989. ‘‘The

Temporal Stability of Individually Tested Intelli-

gence.’’ Journal of Clinical Psychology 45(2):

294–302.

Scott, John P. 1962. ‘‘Critical Periods in Behavioral

Development Critical Periods Determine the Direc-

tion of Social, Intellectual, and Emotional Develop-

ment. Science 138(3544):949–58.

Silles, Mary A. 2009. ‘‘The Causal Effect of Education

on Health: Evidence from the United Kingdom.’’

Economics of Education Review 28(1):122–28.

Smith-Greenaway, Emily. 2015. ‘‘Are Literacy Skills

Associated with Young Adults’ Health in Africa?

Evidence from Malawi.’’ Social Science & Medicine

127:124–33.

Spasojevic, Jasmina. 2003. ‘‘Effects of Education on

Adult Health in Sweden: Results from a Natural

Experiment.’’ Pp. 179–99 in Current Issues in Health

Economics, Volume 290, edited by D. J. Slottje and

R. Tchernis. New York: Emerald Group Publishing

Limited.

Spitz, Herman L. 1986. The Raising of Intelligence: A

Selected History of Attempts to Raise Retarded Intel-

ligence. New York, NY: Routledge.

Tach, Laura Marie, and George Farkas. 2006. ‘‘Lear-

ning-related Behaviors, Cognitive Skills, and Ability

Grouping When Schooling Begins.’’ Social Science

Research 35(4):1048–79.

Vingilis, Evelyn R., Terrence J. Wade, and Jane S. See-

ley. 2002. ‘‘Predictors of Adolescent Self-rated

Health: Analysis of the National Population Health

Survey.’’ Canadian Journal of Public Health/Revue

Canadienne de Sante’e Publique 93(3):193–97.

Waite, Linda, and Maggie Gallagher. 2002. The Case for

Marriage: Why Married People Are Happier,

Healthier and Better Off Financially. New York,

NY: Broadway Books.




Warren, John R., Jennifer T. Sheridan, and Robert M.

Hauser. 2002. ‘‘Occupational Stratification across

the Life Course: Evidence from the Wisconsin Lon-

gitudinal Study.’’ American Sociological Review

67(3):432–55.

Watson, David, and Lorie M. Walker. 1996. ‘‘The

Long-term Stability and Predictive Validity of Trait

Measures of Affect.’’ Journal of Personality and

Social Psychology 70(3):567–77.

Winkleby, Marilyn A., Darius E. Jatulis, Frank Erica,

and Stephen P. Fortmann. 1992. ‘‘Socioeconomic

Status and Health: How Education, Income, and

Occupation Contribute to Risk Factors for Cardio-

vascular Disease.’’ American Journal of Public

Health 82(6):816–20.

Author Biographies

Naomi Duke is a doctoral student in the Department of

Sociology at University of Minnesota. She is also an

assistant professor in the university’s Department of

Pediatrics and board certified in internal medicine,

pediatrics, and adolescent medicine. Her research inter-

ests are in understanding the social basis of health and

health disparities across the life course by integrating

knowledge and skills in medicine and public health

with sociological theory and methodology. Her disserta-

tion investigates the impact of youth survival perceptions

over time on measures of health in adulthood, including

diagnostic profiles, physiological indices, and mental

health outcomes. Recent publications appear in Journal

of Adolescent Health, Pediatrics, and Youth & Society.

Ross Macmillan is a professor of sociology in the

Department of Policy Analysis and Public Management

and the director of the PhD program in policy analysis

and administration at Bocconi University. He is also

the former director and an affiliate of the Dondena Cen-

tre for Research on Social Dynamics and Public Policy.

His current research focuses on theoretical and method-

ological complications in the role of education in demo-

graphic processes, social mobility, and life course transi-

tions. Recent publications appear in Social Science &

Medicine, Handbook of the Life Course, and Social

Forces.




Schooling, Skills, and Self-rated Health · Schooling, Skills, and Self-rated Health: ATest of ......

Documents

Transcript of Schooling, Skills, and Self-rated Health · Schooling, Skills, and Self-rated Health: ATest of ......