K.C. Land et al. (eds.), Handbook of Social Indicators and Quality of Life Research,
DOI 10.1007/978-94-007-2421-1_12, © Springer Science+Business Media B.V. 2012
Education and Quality of Life
Jason D. Edgerton , Lance W. Roberts ,
and Susanne von Below
The Importance of Education and Its
Connection to Quality of Life
The purpose of formal education, of schooling, can be
broadly conceptualized as fourfold—socialization,
allocation, economic production, and legitimation—
with each process interrelating with the others.
Schooling is a primary means for the transmission of
culture and passing along of values, knowledge, and
skills deemed important in a society and for the
responsible participation of citizens within that soci-
ety. This socialization function has, in recent decades,
increasingly come to encompass training and prepara-
tion for productive employment in the globalizing
“knowledge economy.” Critical theorists point to the
“hidden curriculum” embedded within the formal
education system which instills in students the pat-
terns of thought and behavior compatible with mod-
ern capitalist society. Others point to the upper and
middle-class values (e.g., achievement orientation,
extended time horizon) imbuing formal education and
the cultural, material, and social advantages incum-
bent upon their acquisition.
The formal education system is also a means of rationing
opportunity, of differentiating and allocating individu-
als into different positions within a society’s social
stratiﬁ cation structure; attainment of educationally
contingent credentials is linked to occupational trajec-
tory, income, and attendant life chances. Depending on
the chosen theoretical perspective, the formal educa-
tion system can be seen as promoting social mobility
or curtailing it, the weight of empirical evidence sug-
gests it does both to varying degrees (Hout and DiPrete
2006 ) . Functionalist and liberal approaches see stratiﬁ -
cation as an inevitable feature of education as an alloca-
tive mechanism; individuals of differing ability and
motivation are sorted out according to the needs of
society and/or the economy. Critical approaches empha-
size the allocative inequities within education systems,
contending that formal education systems tend to repro-
duce existing social inequalities. Functionalist and lib-
eral accounts emphasize the notion of equal educational
opportunity; all children should have access to public
education, but that ultimately meritocratic competition
will ensure that the “cream rises to the top.” Critics see
claims of equal opportunity as illusory and argue that
children are already on unequal footing when they
enter the formal education system and that these dis-
parities tend to grow and multiply at successive levels,
such that over their educational careers, those from
privileged backgrounds experience a cumulative
advantage over their less fortunate peers.
J. D. Edgerton (*) • L. W. Roberts
University of Manitoba , Winnipeg , Canada
S. von Below
Assistant Head, Statistics and International Comparative
Analyses, German Federal Ministry of Education,
University of Manitoba , Winnipeg , Canada
266 J.D. Edgerton et al.
Employment and Economic Development
Increasingly education is seen as vital cog in a coun-
try’s economic engine, both in terms of training, and
research and development. One prominent economic
rationalist approach, human capital theory , focuses on
returns to investment in education: education and train-
ing (human capital
1 ) increase worker productivity and
hence the value of educated workers. Thus, individuals
who invest time, energy, and money into education do
so with the expectation of securing a better job and
enhanced lifetime earnings. At the individual level,
increasing education (human capital) increases worker
productivity and thus garners better employment and
income for the individual. At the social or aggregate
level, general increments in the stock of human capital
are supposed to increase overall productivity, prosper-
ity, and social cohesion (OECD 1998, 2001 ) . Many
governments have embraced this policy rationale even
though deﬁ nitive evidence of the macro-level effects of
human capital investment remains rather elusive (e.g.,
Barro 2001 ; McMahon 1997, 1999, 2000 ; Helliwell
2001 ; Sweetman 2002 ; Krueger and Lindahl 2001 ) .
Legitimation of Knowledge and Status
A contrarian screening or signaling hypothesis exists
which questions the strength of the education-produc-
tivity relationship. This hypothesis argues that it is just
as likely that it is not the increased level of knowledge
per se that enhances a person’s educational returns but
rather what the attainment of a particular credential
signiﬁ es to employers about the characteristics of a
potential employee (i.e., that they have the value orien-
tations, motivation, habits, etc., sought by or familiar
to the employer). A related aspect of such credential-
ism is professionalization, by which certain occupa-
tional groups seek to elevate the status of their work
(and corresponding level of compensation). This is done
by, among other means, establishing institutionalized
authority over a specialized area of knowledge and
practice (such as medicine or accountancy) and limiting
professional membership by requiring certain higher
education credentials. This gatekeeping function is a
form of social selection that contributes to the stratiﬁ ca-
tion within society, as various occupational groups seek
to establish or maintain the relative advantages of insti-
tutionally sanctioned expertise. Accordingly, the acquisi-
tion of certain education-contingent professional
credentials has a potent effect on a person’s standing within
society’s socioeconomic structure.
From this brief review of the basic purposes of for-
mal education within modern western society, it is
apparent that schooling is integrally related to life
chances, both in terms of those who are afforded (and
equipped to capitalize on) educational opportunity and
those who are excluded from or afforded less opportu-
nity. Indeed, there is a vast body of literature document-
ing various aspects of this relationship across regional,
national, and international contexts. It follows that if
education affects life chances, then it also has the poten-
tial to affect quality of life. The contemporary tendency
to view education as remedy for various social and eco-
nomic ills is testament to its perceived importance to qual-
ity of life. For example, the Organization for Economic
Cooperation and Development (OECD) enthusiastically
advocates investment in human capital as a strategy for
overcoming labor market challenges in the global econ-
omy, increasing individual opportunity and national eco-
nomic productivity, as well as contributing to the resolution
of a host of social problems (OECD 1998, 1999a, 2001 ) .
UNESCO ( 2000 : 8) also underscores the funda-
mental nature of the connection between quality of life
and education in its assertion that
…all children, young people and adults have the human
right to beneﬁ t from an education that will meet their basic
learning needs in the best and fullest sense of the term, an
education that includes learning to know, to do, to live
together and to be. It is an education geared to tapping each
individual’s talents and potential, and developing learners’
personalities, so that they can improve their lives and trans-
form their societies…Without accelerated progress towards
education for all, national and internationally agreed tar-
gets for poverty reduction will be missed, and inequalities
between countries and within societies will widen.
D e ﬁ ning Quality of Life
The term “quality of life” (QoL) is rendered somewhat
problematic by its broad application in different con-
texts for different purposes by analysts working within
1 Human capital is succinctly deﬁ ned by the OECD ( 1998 : 9) as
“the knowledge, skills, competences, and other attributes embod-
ied in individuals that are relevant to economic activity.” Highest
level of education attained and/or number of years of schooling
are the most common operational deﬁ nitions of human capital.
26712 Education and Quality of Life
various distinct academic disciplines (Rapley 2003 ;
Phillips 2006 ) . 2 While the generic connotations of the
term may be cursorily familiar to most people, its mul-
tidimensional and multidisciplinary scope makes more
precise conceptualization a task rife with inconsistency
and ambiguity. At base, ascertaining quality of life
involves some assessment of welfare, whether of the
individual or of the collective, and this assessment
typically involves objective (i.e., measurable in terms
of quantity or frequency) and subjective (i.e., measure-
ment contingent on the perception of the particular
individual) indicators. Deﬁ ning what constitutes welfare
or the requisite conditions for the “good life” is itself a
value-laden enterprise and underscores the normative
footings of quality of life research (particularly the
search for “objective” indicators). Often, which
indicators—subjective or objective—and which level
of aggregation—e.g., individual, family, community,
nation—a researcher is interested in depend on the dis-
cipline within which that researcher is working. One of
the characteristic difﬁ culties in QoL research is that
subjective and objective indicators are often poorly
correlated, and so it is common practice to include
both in research (Rapley 2003 ; Cummins 1997 ) .
Subjective measures typically involve self-report sur-
veys or interviews. Common examples of objective
measures include per capita income, life expectancy,
morbidity rates, literacy rates, average or median level
of educational attainment, and unemployment rates.
Given space considerations, the present chapter will
primarily focus on the relationship between education
and quality of life outcomes at the individual/familial
level. Circumscribing our topic in this way also aids
the choice of a deﬁ nition of quality of life. Rapley
( 2003 ) considers a number of proposed deﬁ nitions of
quality of life at various levels of aggregation. He suggests
that the most inﬂ uential individual-level deﬁ nition of
quality of life is that posited by Robert Cummins (and
operationalized by the Comprehensive Quality of Life
3 ). Cummins ( 1997 : 132) deﬁ nes quality of life in
terms of both subjective and objective dimensions,
with each dimension consisting of seven domains:
“material well-being, health, productivity, intimacy,
safety, community, and emotional well-being. Objective
domains comprise culturally relevant measures of
objective well-being. Subjective domains comprise
domain satisfaction weighted by their importance to
the individual.” These domains each contribute to
overall quality of life. Cummins ( 1996 ) conducted a
meta-analysis of 32 articles purporting to measure var-
iously 173 dimensions of quality of life (invoking 351
labels) and found that the seven COMQoL dimensions
incorporated 83% of the dimensions reported. Haggerty
et al. ( 2001 ) review 22 prominent QoL indexes and
conclude that the seven domains posited by Cummins
currently provide the most useful standardized taxon-
omy for discussing QoL domains.
This chapter uses (with slight modiﬁ cations) these
7 QoL domains—Material Well-being/Standard of
Living, Productivity/Achieving in Life, Emotional Well-
being/Resiliency, Health, Community, Relationships/
Intimacy, Personal Safety/Future Security—as an heu-
ristic framework to organize an overview of research
(primarily in the ﬁ elds of economics, psychology, and
sociology) conducted since 1990 on the relationship
between education and quality of life.
4 It should be
noted that while we have in several sections drawn
attention to the importance of comparison across
national contexts, the preponderance of research con-
sidered here is focused on the United States. We will
ﬁ rst brieﬂ y mention education as an indicator of qual-
ity of life (output or outcome), and then we will offer a
more extensive review of evidence on education as a
cause (throughput) of quality of life.
2 See Sirgy et al. ( 2006 ) for an overview and progress report of
QoL research across several prominent ﬁ elds of inquiry.
3 The ComQoL was abandoned in 2001 due to persistent prob-
lems with the instrument (see Cummins
2002 ) . Cummins and
associates subsequently established the International Wellbeing
Group that is developing a new quality of life measurement, the
Personal Wellbeing Index (International Wellbeing Group
2006 ) .
In the PWI, the original ComQoL domains have been modiﬁ ed
slightly and an eighth added. Thus, the PWI quality of life domains
are standard of living, health, achieving in life, relationships,
safety, community-connectedness, future security, and spiritual-
ity/religion. While the PWI itself is intended only to measure
subjective satisfaction within these domains, one of the criteria
for domain selection was commensurability with objective mea-
surement (or at least the possibility of objective measurement
when suitable indicators are established) of each domain as well
(International Wellbeing Group
2006 ) .
4 The literature review was conducted using the following data-
bases: Education: A SAGE Full-text Collection, Psychology: A
SAGE Full-text Collection, Sociology: A SAGE Full-text
Collection, ERIC, CSA Sociological Abstracts, EconLit, and
PsychINFO. In addition to quality of life, other potentially
equivalent keywords used in the search included wellbeing,
wellness, standard of living, happiness, subjective wellbeing,
life satisfaction, beneﬁ ts.
268 J.D. Edgerton et al.
Education as Quality of Life Indicator
Extending from the belief in education as integral to
life chances is the assumption that educational indica-
tors (e.g., enrolment rates, average scores on standard-
ized achievement tests) are also social indicators or
markers of the distribution of living conditions within
a society. Social indicators are statistical tools useful to
policymakers for monitoring various aspects of social
systems and for guiding the implementation and evalu-
ation of policies directed at improving and maintaining
quality of life (Ferris 1988 ; Land 2000 ) . Numerous
indexes of quality of life or well-being include educa-
tion as an indicator, for example, the Human
Development Index (UNHDP 2003 ) , Quality of Life
(Diener 1995 ) , and Index of Social Progress (Estes
1997 ) each incorporate some measure of educational
participation and literacy rates. Other prominent exam-
ples of QoL measures that variously incorporate edu-
cation indicators include Johnston’s ( 1988 ) QoL Index,
the International Living Index (see Haggerty et al.
2001 ) , Miringoff’s Index of Social Health ( Miringoff
et al. 1996 , Miringoff and Miringoff 1999 ), Michalos’
( 1980 –82) North American Social Report, Netherland’s
Living Conditions Index (Boelhouwer and Stoop 1999 ) ,
and the Swedish ULF system (Haggerty et al. 2001 ) .
The rest of this chapter will focus on education not as a
macro-level indicator of quality of life but rather as a
primary factor affecting and affected by individuals’
quality of life, both directly and indirectly.
Education Effects by Quality of Life
Achieving in Life
Level of educational attainment itself is an indicator of
achievement in that particular levels of educational
credentials are associated with particular levels of edu-
cational attainment or performance. In the labor mar-
ket, individual academic credentials signify to
employers a particular history of achievement or per-
formance by their holder and by extension, the future
performance potential of that individual as an
employee. More speciﬁ c vocational credentials may
signify that an individual is formally qualiﬁ ed (i.e., has
completed the requisite training) for a particular job.
It follows that educational achievement is crucial to
occupational status attainment as well. Hauser et al.
( 2000 : 197) analyzed several national survey datasets
from the USA and concluded that the net effect of edu-
cation on occupational status (controlling for mother’s
and father’s education levels, family head’s occupa-
tional status, and several other relevant social back-
ground variables) is much greater for high school and
postsecondary education than for sub-high school lev-
els of education. Similarly, using longitudinal data
from the Wisconsin Longitudinal Survey, they docu-
ment a substantial and enduring positive effect for
post-high school education on occupational status over
the lifespan, adjusting for social background, ability,
and various socialpsychological variables (Hauser,
et al. 2000 : 225). 5
Pascarella and Terenzini ( 2005 ) identify several net
effects of higher education on labor market success.
With regard to occupational status, they ﬁ nd that a
bachelor’s degree provides a substantial advantage
over a high school diploma. An associate (i.e., 2-year)
degree provides a moderate status advantage, while
lesser amounts of postsecondary education or sub-bac-
calaureate credentials, such as vocational diplomas,
provide a modest advantage. In terms of labor force
attachment, their gathered evidence generally indicates
a positive association between amount of postsecond-
ary education and workforce participation and, con-
versely, a negative association between amount of
postsecondary education and likelihood of unemploy-
ment. As well, workers with postsecondary education
are more likely to rise to supervisory roles (Ross and
Reskin 1992 ; Bound et al. 1995 ; Krahn 2004 ) .
In all OECD countries, educational achievement is
strongly linked to the occupations, education, and eco-
nomic status of students’ parents, although the magni-
tude of the relationship differs across counties (UNICEF
2002 ) . There is a well-documented positive relation-
ship between parental education and child education
level and cognitive development (Wolfe and Haveman
2001 ) . Conversely, poor education is associated with a
5 Hauser and Sewell have developed a socialpsychological model
to account for impact of social background and education on
occupational status (Hauser et al.
2000 : 209–210; Sewell and
1992a ; b ) .
26912 Education and Quality of Life
number of detrimental intergenerational consequences
(Haveman and Wolfe 1994 , 1995 ; Maynard and
McGrath 1997 : 127). Wolfe and Haveman ( 2001 )
observe that there are two paths of inﬂ uence generally
identiﬁ ed in the literature, a direct path (via better
choices and investments by parents) and an indirect
path (contextual effects—such as better quality human
and social capital—of the neighborhoods in which
children are raised). They review a number of studies
and conclude there seems to be a strong relationship
between number of years of parental schooling and
several important outcomes for their offspring such as
school performance, teenaged childbearing, health,
and criminal behavior. As well, Wolfe and Haveman
( 2001 ; Ginther et al. 2000 ) identify a “persistent”
(although not unanimous) pattern of ﬁ ndings linking
neighborhood contextual variables with offspring out-
comes such as schooling, teenaged childbearing, and
Parental postsecondary attendance has a net posi-
tive effect on the high school math and science scores
of a child. The effect seems to be largely accounted for
by the relatively learning-enriched or intellectually
stimulating home environment (“learning capital”)
provided by more educated parents (Pascarella and
Terenzini 2005 : 590; Feinstein et al. 2004 ) . Educated
parents are not only more likely to cultivate the dispo-
sition and the capacity to learn but are also more apt to
ingrain an appreciation and enjoyment of learning
along higher achievement expectations (Krahn 2004 ) .
Reared in more cognitively enriching home environ-
ments from an early age (UNICEF 2002 ) , children
from socioeconomically advantaged backgrounds
enter formal schooling with a greater “readiness to
learn.” Conversely, Miech et al. ( 2001 ) found that chil-
dren from lower SES backgrounds are more likely to
enter the education system with lower levels of self-
6 which is associated negatively with school
adjustment outcomes—even when family background
is controlled for. Haas ( 2006 ) found that socioeco-
nomic disadvantage is associated with poorer child-
hood health, which, in turn, has signiﬁ cant negative
effects on educational attainment and adult socioeco-
nomic status (occupational earnings, wealth) over the
More educated parents are also more likely to settle
in neighborhoods where not only are there more stimu-
lating and supportive public resources, but where their
children interact—in school and out—with peers
primed in similarly enriched home environments and
frequently exposed to high-achieving adult role mod-
els (Feinstein et al. 2004 ) . There is also some evidence
that student’s performance is affected by peer grouping,
with students beneﬁ ting from immersion in context of
high performing peers and high expectations (Davies
1999 ; Ho and Willms 1996 ; Frempong and Willms
2002 ; Feinstein et al. 2004 ) .
Furthermore, the early educational advantage
tends to persist at successive educational levels
(Kerckhoff and Glennie 1999 ) . Students whose par-
ents attended postsecondary institutions are more
likely to pursue postsecondary education themselves,
more likely to attain a ﬁ rst degree, and are more likely
to continue on to graduate or professional school. For
instance, students whose parents attended a postsec-
ondary educational institution are twice as likely to
complete a bachelor’s degree as ﬁ rst generation stu-
dents (those whose parents did not attend). While stu-
dents whose parents hold bachelor degrees are ﬁ ve
times as likely as ﬁ rst generation students to also earn
one (Pascarella and Terenzini 2005 ) . The children of
university-educated parents are also much more likely
to enter into managerial or professional occupations
(Krahn 2004 ) .
Material Well-Being/Standard of Living
Space limitations prevent a comprehensive treatment
of the socioeconomic returns to education literature, so
we will content ourselves with touching on some of the
basic ﬁ ndings. Educational attainment directly effects
occupational status (one’s initial level of entry and
subsequent stability of attachment to the labor market),
and both contribute to determining how much one
earns (Tachibanaki 1997 ) . OECD data on employment
and unemployment rates by level of education gener-
ally demonstrate this, as seen in Tables 12.1 and 12.2 .
On average across OECD countries, the probability of
unemployment decreases while the probability of
employment increases with higher levels of education.
In terms of earnings premiums for higher levels of
education, Table 12.3 shows that, on average, across
OECD countries, those with less than upper secondary
6 “Speciﬁ cally, self-regulation refers to processes, such as the
tendency to maintain attention on a task and to suppress inap-
propriate behavior under instructions” (Miech et al.
2001 : 103).
270 J.D. Edgerton et al.
Table 12.1 Trends in unemployment rates by educational attainment (1991–2004)
Number of 25- to 64-year-olds in unemployment as a percentage of the labor force aged 25–64, by level of
1995 1998 2000 2001 2002 2003 2004
OECD average Below upper secondary 10.8 9.5 9.1 8.9 9.4 10.2 10.4
Upper secondary and
7.3 6.4 5.8 5.6 5.9 6.2 6.2
Tertiary education 4.6 4.1 3.6 3.3 3.8 4.0 3.9
Source: Table A8.4a in OECD (
a International Standard Classiﬁ cation of Education (ISCED), see Appendix for deﬁ nitions of educational levels
Table 12.2 Trends in employment rates by educational attainment (1991–2004)
Number of 25- to 64-year-olds in employment as a percentage of the population aged 25–64 by level
of educational attainment
1995 1998 2000 2001 2002 2003 2004
OECD average Below upper secondary 57 57 57 57 57 56 56
Upper secondary and
73 75 75 75 75 74 74
Tertiary education 84 85 85 85 84 83 84
Source: Table A8.3a in OECD (
2006 ) .
Table 12.3 Relative earnings of the population with income from employment
By level of educational attainment and gender for 25- to 64-year-olds and 30- to 44-year-olds (upper secondary education = 100)
Australia 2001 77 NA 106 148 133
Belgium 2002 91 NA 114 152 132
Canada 2001 79 105 115 177 143
Czech Rep. 1999 68 NA 151 180 179
Denmark 2001 87 118 114 127 125
Finland 2001 95 NA 121 181 150
France 2002 84 NA 125 167 150
Germany 2002 78 116 120 161 146
Hungary 2001 77 131 164 210 210
Ireland 2000 87 82 124 163 149
Italy 2000 78 NA NA 138 138
Korea 1998 78 NA 106 147 135
Netherlands 1997 85 121 139 144 144
New Zealand 2001 74 NA NA 133 133
Norway 2002 85 125 155 135 137
Portugal 1999 62 NA 141 192 178
Spain 2001 78 NA 95 141 129
Sweden 2001 89 127 110 148 135
Switzerland 2003 76 112 141 168 158
UK 2001 67 NA 128 174 159
USA 2002 71 120 118 195 186
Average 79 116 126 161 150
NA not applicable or data not available
Source: Table A11.1a in OECD (
27112 Education and Quality of Life
education earn 21% less than individuals with upper
secondary education (i.e., high school diploma);
individuals with postsecondary but non-tertiary education
earn 16% more. Individuals with type B tertiary educa-
tion (i.e., technical/vocational training) earn 26% more
than those with upper secondary education while those
with type A tertiary education (usually university)
enjoy the greatest advantage of all, earning 61% more.
The table also indicates that the steepness of this
educational-level earnings gradient varies substantially
Card ( 1998 ) conducted an extensive review of the
economic literature pertaining to the impact of educa-
tion on earnings and concluded that “A unifying theme
in much of this work is that the return to education is
not a single parameter in the population, but rather a
random variable that may vary with other characteris-
tics of individuals, such as family background, ability,
or level of schooling” (Card 1998 : 2). Thus, while the
weight of evidence points toward a causal link, the
relationship is far from straightforward as the effect of
education on earnings is variously conditioned by a
host of other variables. Yet as complicated as the pic-
ture can become, as Soloman and Fagano ( 1997 : 826)
aptly summarize, “everything else being equal, those
with more and better education seem to earn more.”
Consistent with this, Pascarella and Terenzini
( 2005 ) identify an income premium related to higher
educational attainment. Using data from representative
nationwide samples, Pascarella and Terenzini estimate
the general premium for a bachelor’s degree (com-
pared to a high school diploma) in the USA to be about
37% for men and about 39% for women. They esti-
mate the hourly wage premium to be about 28% for
men and about 35% for women.
Pascarella and Terenzini ( 2005 ) also ﬁ nd evidence
of a credentialing effect. The term credentialing effect
is used to denote the earnings advantage that accrues to
those who complete a degree compared to others who
have the same amount of credits or courses but no
degree. Pascarella and Terenzini estimate that men
with a bachelor’s degree earn, on average, about 15%
more than men with 4 years of university credit but no
degree. For women, they estimate the average advantage
at about 12%. The average earnings advantage for men
who complete a 2-year associate degree is 9% over
men with 2 years of postsecondary course credit but no
degree. For women, the estimated average advantage is
about 11%. Heckman et al. ( 1996 ) ﬁ ndings suggest the
credentialing effect represents only a small proportion
of the relationship between educational attainment and
earnings. Their results indicate a statistically signiﬁ cant
credentialing effect, but they also found an enduring net
return to years of schooling.
Another important source of evidence regarding
the effects on education on earnings comes from lon-
gitudinal studies. Grubb ( 1993 ) analyzed data from
the 1972 National Longitudinal Survey (NLS) in the
USA and found an earnings advantage related to
higher education (even after correcting for factors
such as socioeconomic status, race, ability, work expe-
rience, and access to on-the-job training). For males,
about one half of the earnings advantage offered by
obtaining a 4-year bachelor degree (compared to just
high school completion) is due to the additional
schooling itself; for females, extra schooling accounts
for about a third of the advantage. He found that while
community college (2-year) degrees offer a return, it
is less than for 4-year degrees and is due mostly to
increased access to jobs that offer greater opportunity
for on-the-job training rather than the additional
schooling per se.
Kane and Rouse ( 1995 ) also utilize the 1972 NLS
data to estimate the annual returns (% increase in
income) to community college and 4-year university
degrees to be 7% and 28% respectively for men and
26% and 39% for women. They also ﬁ nd evidence of
returns for those who completed some course credits
but not a degree, the rate of return per completed credit
was higher for university courses than community col-
lege ones, and higher for women than men. Kane and
Rouse also analyzed data from a different survey, the
National Longitudinal Survey of Youth (NLSY), and
found somewhat different results in that male college
and university dropouts held an earnings advantage
over their high school graduate counterparts while
female dropouts did not.
Murname et al. ( 1995 ) found that the net wage gap
between university graduates and high school gradu-
ates increases over the career span. Arias and McMahon
7 Also called the “sheepskin effect” (Card 1998 : 7).
272 J.D. Edgerton et al.
( 1997 ) used cross-sectional earnings data (1967–1995)
from the Current Population Survey (CPS) to estimate
“dynamic rates of return.” Their ﬁ ndings indicate that
the earnings premium for completing a university
degree is increasing relative to the rate of return for
only partially completing a degree (i.e., earning some
credits). The cumulative nature of this economic gap is
evident in Land and Russell’s ( 1996 ) ﬁ nding (using
7 years of panel data from the Survey of Income and
Program Participation) that households with a highly
educated head have more wealth (net assets) than
households with a poorly educated head.
Two important “third” variables to be considered
when examining the education-income relationship
are family background and ability. First, individuals
with higher education tend to have parents with higher
education as well. It might be that the income advan-
tage results from family background (for instance,
from having a parent with connections). Second, it can
be argued that those who attain higher levels of educa-
tion do so because they have greater ability and that
those individuals would earn higher wages even with-
out higher schooling. In short, it might actually be
underlying ability—not education—that is responsible
for higher income.
Intrafamily comparisons provide an opportunity to
control for family background effects on earnings.
example, Ashenfelter and Zimmerman ( 1997 ) esti-
mated the relationship of educational attainment dif-
ferences to income differences between fathers and
sons. They found that a 1-year difference in educa-
tional attainment resulted in a 5-percent difference in
wage rates. Altonji and Dunn ( 1996 ) looked at siblings
and found that an additional year of schooling trans-
lated into a 3.7-percent increase in earnings among
brothers and a 6.3-percent increase among sisters.
Identical twin studies are a useful method for isolating
the effect of schooling on earnings from the effects of
both family background and ability differentials. The
rationale behind such studies is that studying geneti-
cally identical individuals raised in the same family
provides increased control (sometimes referred to as a
“natural experiment”) over variance due to disparities
in social background and ability. Hence, “contrasts of
the wage differences of identical twins with their edu-
cation differences may provide a particularly useful
way to isolate the causal effect of schooling on earnings”
(Ashenfelter and Rouse 1998 : 281). If there are earn-
ings differences between identical twins with differing
levels of education, the difference is presumed not to
be due to genetically determined ability, and we can be
more conﬁ dent that schooling does indeed affect
earnings over and above any contribution by family
background or ability. Ashenfelter and Rouse ( 1998 )
estimate an earnings advantage of about 8% per extra
year of schooling for the more educated twin (adjusted
upward to 9.9% when accounting for family back-
ground and measurement error in the self-reported
education variable). Similarly, Miller et al. ( 1995 )
found an adjusted income (log of annual earnings)
advantage of 4.5% per extra year of schooling among
another sample of twins.
Although the above discussion of returns to educa-
tion has focused almost exclusively on ﬁ ndings in the
American context, there is evidence from other coun-
tries as well—for examples, see Asplund and Pereira
( 1999 ) for a review of European evidence, see Johnes
( 1993 ) for evidence from developing countries, see
Blundell et al. ( 2000 ) and Chevalier et al. ( 2002 ) for
UK evidence. But cross-national comparison also
adds to the complexity of the emerging picture since
the transition from school to work is not uniform
across nations. For example, Sullivan and Smeeding
( 1997 ) utilize Luxembourg Income study (LIS) data
(1989–1994) to compare the educational attainment-
income gradient across eight nations. They conclude
that “among advanced economies there is no obvious
relationship between the degree of earnings inequal-
ity and the percentage of the labor force attaining
higher levels of education. Countries differ substan-
tially both in the way in which they organize their
educational systems and the way in which they inte-
grate the educational system with the labor market”
(p. 513). Thus, we can add institutional features of
the linkages between national education systems and
labor markets to the list of important variables that
condition the education-earnings relationship.
Similarly, Kerckhoff ( 2000 , 2001 ) concludes that
various institutional features of education systems
determine their “capacity to structure” students’ tran-
sition into the workforce. Müller and Shavit ( 1998 ) ,
for example, analyze data and case studies from 13
8 The outcome variable in the monetary return to education is
typically the average log hourly wage.
27312 Education and Quality of Life
developed countries in an effort to examine the impor-
tance of three institutional characteristics of national
education systems—vocational speciﬁ city of creden-
tials, standardization of credentials, and degree of
stratiﬁ cation within the education system. Educational
systems vary in terms of the extent to which they focus
on specialist versus generalist education credentials.
Some systems (“qualiﬁ cational”) are characterized by
a high degree of specialized vocational training, while
others (“organizational”) offer a more generalized
(academic) education aimed at providing a basic set of
skills that are widely transferable across vocational
settings, to be ﬁ ne-tuned by on the job learning.
Educational systems also vary in terms of degree of
formal stratiﬁ cation and standardization. For instance,
the German system is a qualiﬁ cational one that is
highly stratiﬁ ed in that it sorts students from early on
into differing educational trajectories leading either to
an academic track or a vocational track in which spe-
cialized training is linked to particular vocations. The
German credential system is also characterized by a
high degree of standardization of credentials.
Employers are more involved in determining and sanc-
tioning the skill requirements of a particular creden-
tial, with the result that a speciﬁ c credential from
different schools has uniform meaning. The USA, on
the other hand, is an organizational system where cre-
dentials tend to be more generic, formal sorting begins
later, and credentials are much less uniform in their
value and meaning. In the USA, a credential is typi-
cally not considered specialized preparation for a par-
ticular job (although there are exceptions such as
professional schools or vocational training programs)
but rather a broad indication (a signal) of the ability
and potential of that individual. The process of match-
ing skills to job requirements is much more “a trial-
and-error process” in North America (Heinz 1999 ) .
Müller and Shavit ( 1998 ) found that while there
were some signiﬁ cant differences among the 13 coun-
tries studied, there were also some important common-
alities such as educational credentials are positively
linked with occupational prestige; the marginal returns
to postsecondary education are greater than for lower
level education; educational attainment is an important
determinant of labor force participation; and educa-
tional attainment (particularly postsecondary) is asso-
ciated with lower risk of unemployment. One of the
most notable differences between countries concerned
is the magnitude of the effects of credentials on occu-
pational outcomes, with some countries exhibiting a
more rigid credential contingent occupational hierar-
chy than others.
Conventional human capital theory holds that it is
the skill-imparting, productivity-enhancing aspects of
education that lead to the earnings advantage enjoyed
by the more educated. But some scholars argue that
there is more to the equation, making the case for the
importance of the socialization aspects of schooling in
the determination of labor market success as well
(Bowles and et al. 2001a, b ; Farkas 1996 ; Heckman
2000 ; Rosenbaum 2001 ) . Bowles and Gintis ( 2000 )
conducted a meta-analysis of 25 studies that looked at
the schooling-earnings connection and found that cog-
nitive skills were only part of the equation; formal edu-
cation imparts not only skills but also instills the
attitudes and habits valued by employers. They call
these qualities (e.g., trustworthiness, identiﬁ cation
with company or management goals, diligence, future-
orientation, strong sense of self-efﬁ cacy) “incentive-
Thus, it may be that those individuals who suc-
ceed in higher education (which rewards many simi-
lar preferences or habits of conduct) may be more
prepared to succeed within the similar incentive
structure of a demanding high performance (and
hence higher paying) workplace. A higher level of
education is associated with enhanced “psychologi-
cal capital,” that is, the motivational and attitudinal
orientation—particularly high self-esteem and inter-
nal locus of control—likely to lead to higher wage
employment (Goldsmith et al. 1997 ) . These habits,
skills, and styles associated with school and occupa-
tional success are also referred to by some analysts
as “cultural capital” (e.g., Farkas 1996, 2003 ; Lareau
2001 ; Lareau and Weininger 2003 ) .
Further evidence for the importance of such non-
cognitive skills (or “soft skills”
9 ) in the labor market
comes from studies by Heckman and colleagues
(Cameron and Heckman 1993 ; Heckman and
Rubinstein 2001 ) which reveal that while GED
(Graduate Education Development) certiﬁ cate holders
exhibit substantially superior cognitive skills than
other high school dropouts, they do not experience a
9 See also Duncan and Dunifon ( 1998 ) .
274 J.D. Edgerton et al.
corresponding earnings advantage. Part of the reason
appears to be related to behavioral issues such as
delinquency and crime. Thus, the authors suggest
that the GED sends a “mixed signal” in the job mar-
ket—that the individual has the cognitive capacity to
complete high school but may be lacking other attitu-
dinal and behavioral qualities that are valued by the
There is an abundance of evidence pointing to a positive
correlation between education and psychological
health and well-being. Educational achievement is
associated directly with increased self-esteem and
indirectly via effect of earnings. Education is associ-
ated with an increased sense of self-efﬁ cacy, and self-
efﬁ cacy is associated with numerous physical and
mental beneﬁ ts (see Ross and Van Willigen 1997 ) .
“Emotional resilience” or the ability to cope with
adversity and stress is related to self-efﬁ cacy and
self-esteem both of which can be enhanced through
education and successful learning (Hammond 2004 ) .
Schooling can also help foster the acquisition of
adaptive skill sets—such as problem-solving skills
and communication skills—that contribute to resil-
ience (Howard et al. 1999 ) . There is evidence that
undesirable events and adverse experiences have
greater negative emotional repercussions for lower
SES individuals compared to those with higher SES
(e.g., McLeod and Kessler 1990 ) . Ranchor et al.
( 1996 ) found an association between SES, especially
education level and signiﬁ cant variation in coping
styles and resources, with lower SES individuals being
disadvantaged along several psychosocial dimensions
(negative self-esteem, social desirability, hostility,
de Ridder ( 1995 : 313) found that level of educa-
tional attainment inﬂ uenced beliefs related to SES dif-
ferences in vulnerability to psychological distress. She
deﬁ ned beliefs as “lay theories” or “cultural models”
held by the individual that are shaped by their social
group and social position, and which “reﬂ ect the gen-
eralized experiences about the meaning of stressful
events, their impact on health, and their controllability.”
Similarly, a previous interview study ( n = 10) conducted
by de Ridder (cited in de Ridder 1995 : 322) found that
“lower educated participants were more easily agitated
by daily hassles, thought them very unpleasant and
disruptive, and felt they had no control,” compared to
more highly educated participants who “…limited
their deﬁ nition of stress to severe problems for which
no solution was available; daily hassles were consid-
ered part of their normal routine.” As well, more educated
respondents “thought of stress more positively:
although they agreed that stress was potentially
harmful in speciﬁ c situations, in many cases, they also
considered it a challenge, as they felt able to control or
solve the situation.”
Many studies have found the effects of education on
psychological health are mediated by work conditions
(e.g., Lennon 1994 ; Link et al. 1993 ) . Individuals with
higher education are more likely to be involved in work
with greater intrinsic and extrinsic rewards. For example,
more educated individuals are less likely to be involved
in alienating repetitive labor and more likely to be
involved in work that permits greater autonomy (devel-
oping and reinforcing feelings of self-efﬁ cacy), creativity,
more novelty and opportunity for continued learning
and personal growth (Mirowsky and Ross 2003, 2005 ;
Ross and Wu 1995 ; Schieman 2002 ) , and greater social
support which enhances resilience to psychological
distress, depression, and anxiety (Ross and Van
Willigen 1997 ) .
Although people who are college educated rate
higher on a wide range of quality of life indicators
(e.g., income, self-efﬁ cacy, social support network,
mortality risk, perceived health status, time spent in
developmentally enriching activities with children),
they do not consistently express a higher degree of sat-
isfaction with their lives (Ross and Van Willigen 1997 ;
Pascarella and Terenzini 2005 ) . It seems that with
increased educational attainment and socioeconomic
status come higher expectations, people’s sense of life
satisfaction is affected by their rising frame of refer-
ence and the tendency to recalibrate expectations
upward at each level of achievement and acquisition.
Education may also open one’s eyes to a wider range
of possibilities as well as raising the standards by
which one evaluates satisfaction in various domains of
life. As well, people tend to compare their circum-
stances not with those below them but rather with their
status peers and those above them. There are several
different accounts of the relative nature of satisfaction
such as congruity theory (Wilson et al. 1973 ) , multiple
discrepancies theory (Michalos 1985, 2008 ) , and judg-
ment theory (Meadow et al. 1992 ) .
27512 Education and Quality of Life
On the other hand, there is some afﬁ rmative evi-
dence regarding the effects of education on reported
happiness. Blanchﬂ ower and Oswald ( 2001 ) looked at
multiyear cross-sectional data from the USA (GSS)
and Great Britain (Eurobarometer Survey) and found
that educational attainment is associated positively
with happiness even when family income is controlled.
There is also some indication that education may be
indirectly related to life satisfaction in later adulthood.
Fernandez-Ballesteros ( 2001 : 27) found that more
educated individuals with higher incomes report higher
levels of participation in physical, cultural, and social
activities which are positively associated with life
Gerdtham and Johannesson ( 2001 ) also found that
reported happiness increased with education and
income, as well as with self-rated health status.
Meeks and Murrell ( 2001 ) suggest that the lifelong
health advantage associated with educational attain-
ment is mediated by “trait negative affect.” That is,
higher educational attainment is inversely associated
with levels of enduring negative effect; low negative
affect is, in turn, associated with better health and
greater life satisfaction in older adults—or what the
authors term “successful aging.” Successful aging is
contingent on a “life history of successful adaptation”
which results from the interplay of inherited and
learned psychological attributes, degree of life adver-
sity, and available resources. Educational attainment is
positively related to successful adaptation along each
of these dimensions (e.g., high intelligence, high level
of aspiration and motivation, enhanced socioeconomic
opportunities and outcomes).
The positive association between education and health
is well documented. For example, Bound et al. ( 1995 )
found that more educated men were generally less
likely to report having health problems such as severe
chronic pain, hearing and vision problems, arthritis or
functional limitations on daily activities, while indi-
viduals who attended or graduated college have a lower
risk proﬁ le for cancer and coronary heart disease
(Pascarella and Terenzini 2005 ) . There is also consid-
erable evidence to suggest that such education-related
health disparities grow across the lifespan (Mirowsky
and Ross 2003 ; O’Rand 2001 ; Prus 2004 ; Ross and
Wu 1995, 1996 ) . Mirowsky and Ross ( 2005 : 27)
observe that the cumulative health-related conse-
quences of education are evident at various levels, “…
from the socioeconomic (employment, job quality,
earnings, income, and wealth) and behavioral (habits
such as smoking or exercising, beliefs such as per-
ceived control over one’s own life, personal relation-
ships) to the physiological (blood pressure, cholesterol
levels, aerobic capacity), anatomical (body fat, joint
deterioration, arterial fatty plaque) and perhaps intrac-
ellular (insulin resistance, free radical damage).” They
also note that in addition to permeating most aspects of
life, many of these consequences of educational attain-
ment are reciprocal in nature, mutually conditioning
and compounding the effects of one another, good and
bad, such that disparities grow over time.
Mirowsky and Ross ( 2003 ) found evidence of sub-
stantial socioeconomic disparities in health which increase
across the lifespan. Modest education-contingent socio-
economic disparities upon initial entrance into the
workforce compound over time, as do related health
disparities. Speciﬁ cally, they found that persons with
college degrees have lower levels of impairment across
the lifespan; the increase in impairment with aging
was steeper for those with less than high school com-
pared to those with college degrees, thus resulting in
an increasing impairment gap across the lifespan.
Although the gap continues to grow after age 65, the
rate of divergence attenuates (see also Ross and Wu
1996 ) . Prus ( 2004 ) found that the education-contingent
gap in both subjective health and functional health
grew across the adult life course up until age 79 (survey
data was aged capped at age 79).
Higher education typically leads to occupations that
involve less health risk and provide greater ﬁ nancial
capacity to purchase better housing, nutrition, and
health care, all of which are directly linked with health
status (Roberge et al. 1995 ) . Indirect psychosocial
effects emanate from one’s position in the socioeco-
nomic structure (sense of personal agency, coping
skills, social support) and from lifestyle preferences
and practices (recreational activities, diet, smoking,
access to health care information, and services). Even
so, there is evidence that educational attainment is
predictive of health even when income is controlled
for. For example, Meeks and Murrell ( 2001 ) found that
10 There is some evidence for the positive effects of happiness on
2008 ) .
276 J.D. Edgerton et al.
education accounts for variation in health (and life satis-
faction) above and beyond that predicted by income, but
the reverse does not hold for income net of education.
Grossman and Kaestner ( 1997 ) review a number of
American studies that point to years of formal educa-
tion as the most important socioeconomic correlate of
good health, more important than either occupation or
income (both of which are partially determined by
education). This relationship holds across a number of
health indicators, including mortality rates, morbidity
rates, self-rated health status, and physiological mea-
sures, regardless of whether analyzed at the group or
individual level. Using structural equation modeling
and the National Health Interview Survey, Lynch
( 2006 ) found that only about 30% of the effect of edu-
cation on self-rated health is accounted for by income,
but that this indirect effect of education through income
is increasing across cohorts.
As with emotional well-being, many of the effects
of education on physical health are mediated by work-
place conditions. Less educated have more physically
demanding jobs with increased risk of negative physical
consequences (although less than in the past). In addi-
tion, some of the same workplace conditions that affect
psychological health also impact physical health. For
example, the fact that more educated individuals are
less likely to be involved in alienating repetitive labor
and more likely to be involved in work that develops
and reinforces feelings of self-efﬁ cacy or personal
control (Mirowsky and Ross 2003, 2005 ; Ross and Wu
1995 ; Ross and Van Willigen 1997 ) . A greater sense of
self-efﬁ cacy, or the belief in one’s ability to initiate
action and effect outcomes, is also associated with bet-
ter health outcomes. There is also some evidence that
the salutary effects associated with the more autono-
mous, less routinized employment afforded by higher
educational attainment may actually be even greater
for women than men. Due to their traditionally disad-
vantaged status within society, women may reap ampli-
ﬁ ed health beneﬁ ts from educationally augmenting
their socioeconomic position (Reynolds and Ross
1998 ; Schieman 2002 ) .
Many of the positive effects of education on health
stem from the increased likelihood of adopting or ini-
tiating proactive health measures, which prevents and/
or forestalls many ailments. If individuals believe they
have some control over the conditions of their life,
including their health, they are more likely to participate
in health-promoting lifestyles and activities. Education
increases the capacity to produce healthy outcomes via
“learned effectiveness,” education imparts analytical
and problem-solving skills that transfer to various
aspects of life including health maintenance (Mirowsky
and Ross 2005 ) . For instance, education is associated
with increased likelihood of adaptive response in the
wake of health crisis such as quitting smoking after a
heart attack ( Wray et al. 1998 ) .
More educated individuals live healthier lifestyles
including moderation in consumption and regular
exercise (Ross and Wu 1995 ; Mirowsky and Ross
1999 ) due to access to better information for health
management, greater proﬁ ciency at integrating infor-
mation into lifestyle decisions, greater resources to
facilitate health-promoting activities (e.g., money to
buy equipment, gear, memberships, more ﬂ exible
schedule to ﬁ t exercise in), and to procure health
professional assistance when needed. For example,
college graduates spend more time on ﬁ tness activities
than those with lower educational attainment (Robinson
and Godbey 1997 ) . Similarly, Kenkel ( 1991 ) found an
education-contingent difference in exercise time of
about 17 min per day per extra year of schooling.
In general, the more educated are less likely to
smoke (Bound et al. 1995 ; Sander 1995b ; Zhu et al.
1996 ; Kenkel 1991 ) . If they do smoke, the more edu-
cated tend to smoke less per day, with each additional
year of schooling reducing average daily cigarette
intake by 1.6 for men and 1.1 for women (Kenkel
1991 ) . The more educated are also more likely to quit
smoking than those with less education (Sander 1995a ;
Zhu et al. 1996 ) . Those with more education are also
less likely to be heavy drinkers than their less educated
counterparts (Kenkel 1991 ) .
Parental education is also associated with various
child and adolescent health outcomes. Several studies
by Edwards, Shakotko, and Grossman (cited in
Grossman and Kaestner 1997 : 94) ﬁ nd that parental
educational attainment, particularly mother’s, has pos-
itive and statistically signiﬁ cant effects on a number of
health indicators in childhood and adolescence. For
example, children and adolescents of better educated
mothers have better oral health and less likelihood of
obesity or anemia than those of less well-educated
Further to the body of research on the relationship
between education and health, there are also studies
looking at the relationship between education and
longevity. Numerous studies have found a positive
27712 Education and Quality of Life
relationship between education and life expectancy
(e.g., Rogot et al. 1992 ; Crimmins and Saito 2001 ) .
Connected to this is a relationship between education
and decreased morbidity (Crimmins and Saito 2001 ) .
Individuals with less health problems over the lifespan
enter their later years in better health and tend to live
longer. As cohorts age, education’s association with
health and longevity grows stronger. Individuals with
higher socioeconomic status experience a “compres-
sion of morbidity” into a short period in the ﬁ nal years
of life, whereas lower status individuals are more likely
to start experiencing health problems from middle age
onward (Mirowsky and Ross 2005 : Prus 2004 ) .
Educational attainment is negatively related to mortal-
ity across the lifespan (Guralnik et al. 1993 ; Kaplan
and Keil 1993 ) . There is a growing socioeconomic dis-
parity in mortality rates (Lauderdale 2001 ; Bartley
et al. 1998 ) . Manton et al. ( 1997 ) studied National
Long Term Care Surveys in the USA from 1982 to
1991 and found that persons with 8 or more years of
education had advantages in terms of level of function-
ing and longevity over those with less than 8 years of
education. The longevity advantage at age 65 for edu-
cated women was 7 years and 2 years for men.
Explaining the relationship between education,
health, and longevity. Ross and Wu ( 1995 ) contend
that education affects health along three basic fronts:
work and socioeconomic conditions (income security,
nature of work, satisfaction with work, access to quality
health care, etc.), socialpsychological resources (self-
efﬁ cacy, social support network, etc.), lifestyle (exer-
cise, diet, smoking, health monitoring, etc.). Mirowsky
and Ross ( 2003, 2005 ) offer a more comprehensive
cumulative advantage/disadvantage model to account
for the positive effects of education on health. They
suggest that the lifelong health advantage afforded by
greater educational attainment is due to three interre-
lated processes: permeation, accumulation, and ampli-
ﬁ cation. First, the differential effects of education
permeate most aspects of life such as the conditions of
one’s work; rewards from work; interpersonal relation-
ships; habits; economic capacity; social, psychologi-
cal, and informational resources; security; sense of
autonomy. For example, more educated individuals are
less likely to be involved in work that involves repeti-
tive task performance, or is physically demanding, and
tend to have higher degree of autonomy. The more
educated also tend to earn more and so are less likely
to experience economic stress and more able to pur-
chase goods and services (e.g., food, heath care, hous-
ing) that produce good health. Second, consequences
accumulate over the lifespan. For example, the health
consequences of habits and lifestyles (diet, exercise,
smoking) accumulate over the lifespan, both positive
(e.g., lung capacity, muscle mass) and negative (e.g.,
excess body fat, fatty plaque in blood vessels, decreas-
ing bone density). Third, cumulative outcome differen-
tials condition and amplify each other, with advantages
concentrating in some individuals and disadvantages
concentrating in others, such that disparities grow
over the lifespan. For example, over time regular exer-
cise and a healthy diet produce beneﬁ cial accumula-
tions (low body fat and high aerobic capacity), thereby
reinforcing those healthy behaviors and the sense of
control over one’s health, while lack of exercise and
poor diet produce harmful accumulations (high body
fat and low aerobic capacity) which perpetuate those
unhealthy behaviors (e.g., the more body fat one has,
the more difﬁ cult physical activity becomes, not exer-
cising results in increased fat and further aversion to
exercise) and diminishes the sense of control over
one’s health and undermines further effort. Thus, to the
degree that educational attainment is associated with
increased sense of control over one’s health (via suc-
cessful engagement in health-promoting behaviors),
the educationally advantaged are likely to enjoy cor-
responding health advantages.
The cumulative nature of socioeconomic health dis-
parities is highlighted by Mirowsky and Ross’s ( 2005 )
concept of “cascading structural ampliﬁ cation.” It cap-
tures the “slippery slope” nature of socioeconomic
health disadvantage, in which a sequence of circum-
stances unfolds leading the less educated down a path
of mounting health problems: low education leads to
poor income, economic hardships are rendered more
difﬁ cult by inadequate coping skills (due to educa-
tional deﬁ cits), and economic hardships exacerbate
health issues (do not live in neighborhood with rec-
reational facilities, cannot afford healthy food or
exercise gear/equipment or memberships, lack infor-
mation about health-promoting behaviors or opportu-
nities, no peer support, etc.).
Another suggested mechanism by which education
effects health is rate of time preference , or one’s time
orientation. Adoption of a longer time horizon is
assumed to be associated with health-promoting
behavior. Just as the propensity to delay gratiﬁ cation is
conducive to time (and monetary) investment in
278 J.D. Edgerton et al.
education (and hence greater educational attainment),
it may also be related to inclination to invest time
(and money) in health management (Fuchs 1982 in
Grossman and Kaestner 1997 ) . There is some sugges-
tion that the education-time preference relationship is
reciprocal; that is, more educated parents tend to instill
a more future-oriented time preference in their children
to begin with, and this preference is further reinforced
by successful educational attainment (Leigh 1998 ) .
Either way, it may be that the majority of education’s
positive effect on health is a function of education’s
effect on time preference—individuals with a longer
time horizon may be more willing to invest proactively
in the maintenance of their health. Thus, to the degree
that education alters time preference toward the future,
it also improves health.
Mirowsky and Ross ( 2005 ) suggest that the more
educated are more proﬁ cient at producing health out-
comes due to generally enhanced analytical and problem-
solving skills (“learned effectiveness”) which they
apply to health maintenance. Similarly, Grossman and
Kaestner ( 1997 ) observe that the more educated tend
to be “more efﬁ cient producers of health” than less
educated individuals. This efﬁ ciency effect is twofold:
“allocative efﬁ ciency” pertains to the augmentation of
knowledge afforded by education—better educated
individuals typically have access to greater amounts of
health relevant knowledge and are more inclined to
appreciate its import. “Productive efﬁ ciency” refers to
the greater efﬁ cacy of the better educated in producing
positive health outcomes than the poorly educated,
given that both have the same information. More edu-
cated individuals have greater familiarity with the
knowledge production process which may translate
into greater trust in “expert” recommendations and
greater likelihood of compliance. That is, more educated
individuals may be more likely (and better equipped)
to comprehend the relevance of expert recommenda-
tions and to be more effective in mitigating risk accord-
ingly (Smith 1997 ) .
It should also be noted that there is also research
that suggests that the pattern of the relationship
between education and health across the lifespan is not
linear. For example, Lynch ( 2006 ) found that the rela-
tionship between education, income, and health varies
across the lifespan and across cohorts and that the rela-
tionship between these variables peaks at different times.
In his sample, the relationship between education and
income peaked around age 81, the relationship between
income and health peaked around age 56, and the total
effect of education (including indirect effects through
income) on health peaked around age 46. His results
suggest that the cumulative health advantage associ-
ated with education grows into middle age and then
tapers off into old age.
Ross and Van Willigen ( 1997 ) found that the well-
educated reported a higher level of social support.
Further to this, there was a strong association between
the non-alienated work typical of the well-educated
and perceptions of social support. They suggest that
the non-alienated work environments characterized by
non-routine, autonomous, creative work and opportu-
nities for personal growth and learning also foster
supportive relationships among coworkers, colleagues,
According to the 1987 GSS, individuals with higher
levels of educational attainment report having mem-
bership in a greater variety of volunteer groups and
participating in more organized activities (Smith 1995 ) .
Postsecondary graduates exhibit higher levels of
involvement in civic and community groups. Pascarella
and Terenzini ( 2005 ) report that, compared to high
school graduates, individuals with a bachelor degree
were 1.8 times more likely to participate regularly in
political activities, 2.4 times as likely to be involved in
community welfare groups, and 1.8 times as likely to
be highly committed to community leadership. Such
engagement with community causes and organizations
may also foster introduction to inﬂ uential social net-
works that are less accessible to the less educated.
Curtis et al. ( 2004 ) analyzed Canadian data from
the World Values Survey and found individuals with
higher levels of education reported greater involve-
ment in public protest, in community interest groups,
as well as in supporting social movements. Utilizing
data from Statistics Canada’s National Survey of
Giving, Volunteering, and Participation, they also
found that the more educated were more likely to
report voting and participation in volunteer activities.
In recent years, the notion of social capital has been
broadly deployed to describe various dimensions of
“community.” While human capital resides in individuals,
social capital resides in relationships. There are two
basic approaches to conceptualizing social capital (see
27912 Education and Quality of Life
Portes 2000 ) ; one school sees social capital as a second
order property of individuals embedded in social net-
works, while the other sees it as a collective property
of communities and nations.
11 The following discus-
sion of returns to social capital is conducted with refer-
ence to the former (more instrumental) understanding,
the social networks (or social resources) approach.
Burt ( 2000 ) pithily characterizes the basic notion of
network social capital theory as “[b]etter connected
people enjoy higher returns.” Flap ( 1999 : 7) describes
social capital as “social networks and the resources of
others an actor can call upon [which] can be consid-
ered a social resource…another means for that actor to
improve or defend his conditions of living.” There is
considerable evidence that social capital, in the form
of social resources, signiﬁ cantly affects status attain-
ment (Lin 1999 ) . Social resources are resources acti-
vated through one’s direct and indirect contacts. The
potential utility of such resources is related to one’s
position within particular social networks (i.e., status,
connections, and inﬂ uence). Not all networks are cre-
ated equal: some networks (comprised predominantly
of socioeconomically advantaged groups) are richer in
social resources (more diverse, higher caliber connec-
tions) than others. Structural constraints and homophily
(like afﬁ liating with like) contribute to the maintenance
of this network inequality, such that the level of social
resources (and potential status outcomes) available to
the individual is substantially impacted by one’s social
background (Lin 2000 ) and resulting social capital dis-
parities tend to be cumulative in nature (Granovetter
1995 ) . Some individuals and families are embedded in
richer networks with greater access to information and
opportunity, not only from their own social network,
but via complimentary cross connections with other
networks (Burt 2000 ) . Lai et al. ( 1998 ) found evidence
that occupational attainment (current job status) is sig-
niﬁ cantly inﬂ uenced by level of education but also by
the social resources of contacts mobilized in the job
search. The caliber of contacts (i.e., the richness of
contact social resources) available to a person, in turn,
derives from “positional advantages” related to family
background (parental resources), education, and network
Lin ( 2000 : 484) observes that human capital and
social capital can be seen as reciprocally related in that
“[w]ell-connected parents and social ties can …
enhance the opportunities for individuals to obtain
better education, training, and skill and knowledge
credentials. On the other hand, it is clear that human
capital induces social capital. Better educated and
better trained individuals tend to move in social circles
and clubs rich in resources.” One compelling question
that then emerges from this insight—the convertibility
of capital forms—concerns the relative importance of
human versus social capital to status outcomes. For
example, Boxman et al. ( 1991 ) found an inverse rela-
tionship between the two forms of capital, where the
effect of human capital on income was strongest when
social capital was low and weakest when social capital
was high. Consistent with this, Flap and Boxman
( 1999 ) found that for top managers, social capital had
a positive effect on income regardless of the level of
human capital and that the effect of human capital
diminished as the level of social capital increased. Flap
and Boxman ( 2001 ) also found that social capital had
a positive effect on income, prestige of job attained,
and likelihood of informal job searching (i.e., those
with greater social capital are more likely to attempt to
invoke it via informal job searches). Taken together,
these results suggest that level of human capital is most
important to status attainment for those with lesser levels
of social capital, but that its importance diminishes as
one’s level of social capital increases. Thus, while edu-
cation may facilitate entrance to a socioeconomic tra-
jectory, beyond a certain threshold, accumulated social
capital (i.e., access to information and inﬂ uential
connections) carries greater weight and further advan-
tage. Or put another way, returns to education may be
limited without sufﬁ cient social capital.
As previously discussed in the section on intergen-
erational effects of academic achievement, there is
some evidence of “neighborhood effects” due to factors
such as disparities in the quality of resources available
11 Portes ( 1998, 2000 ) and others (e.g., Morrow 1999 ) argue that,
increasingly, the application of the social capital as collective
property approach is being uncritically stretched beyond the
limits of its usefulness and as, a result, is become increasingly
vague, all encompassing, and of dubious analytical value.
12 Mouw ( 2003 ) contends that evidence for the positive effects of
network social capital on labor market outcomes is—upon closer
inspection—confounded and that while the utility of inﬂ uential
contacts is intuitively appealing, better evidence is still required
to substantiate proponents’ claims regarding social capital
280 J.D. Edgerton et al.
to families and “collective socialization.” Children in
neighborhoods with less well-developed infrastructure
(libraries, family resource centers, literacy and after
school programs, cultural amenities like museums, and
recreational facilities) may lag behind their peers from
more afﬂ uent neighborhoods in terms of social and
physical development and school-readiness (Brooks-
Gunn et al. 1993, 1996 ; Neuman and Celano 2001 ) .
Brooks-Gunn et al. ( 1993 ) also ﬁ nd evidence consis-
tent with the notion of “collective socialization” which
highlights the importance of neighborhood adult role
models and extra-familial monitoring or informal
social control (Sampson 2001 ) to children’s psychoso-
cial development. Exposure to high-achieving adult
role models has positive effects on student conduct,
attitudes, and expectations regarding education and
occupational options. Ainsworth ( 2002 ) reported that
prevalence of high-status residents is strongly predic-
tive of increased time spent on homework and higher
math/reading test scores, results consistent with the
collective socialization thesis.
Children’s educational outcomes can also be
impacted (above and beyond individual family back-
ground inﬂ uences) by the makeup of the student
population at their school (Strand 1997 ; Feinstein et al.
1999 ; Robertson and Symons 2003 ) . Indeed, there is a
substantial body of evidence that “the average socio-
economic status of a child’s class or school has an
effect on his or her outcomes, even after taking account
of (individual-level) ability and socioeconomic status”
( Willms 2001 : 25). For example, Ho and Willms
( 1996 ) , utilizing a large representative sample of US
middle school students, found that both advantaged
and disadvantaged students achievement in mathemat-
ics and reading improves when they attend schools
with higher average socioeconomic status. Mayer
( 2002 ) found that increased economic segregation (the
afﬂ uent concentrating in particular areas and the poor
in others) in the USA increased the educational attain-
ment gap (the gradient) between socioeconomically
advantaged and disadvantaged students.
13 Thus, “eco-
nomic segregation in one generation contributes to
economic inequality in the next generation” (p. 167)
via perpetuation of disparities in educational and
Ho and Willms ( 1996 ) also found that parental
involvement in schooling (i.e., volunteering, attending
parent teacher organization meetings) has a positive
effect on student achievement, and parental involve-
ment tends to be higher in high socioeconomic status
schools (although they did not ﬁ nd signiﬁ cant family-
level SES-contingent differences in extent of parental
involvement). Furthermore, their results show that
socioeconomic gradients (SES-contingent differences
in achievement) tend to be shallower in schools with
high parental involvement. A number of studies (e.g.,
Barnard 2004 ; Fan and Chen 2001 ; Feuerstein 2000 ;
Jeynes 2003 ; Steinberg et al. 1992 ; McWayne et al.
2004 ) document the importance of parental involve-
ment (variously measured) to academic achievement,
and while Ho and Willms ( 1996 ) found no family-level
SES differences in parental involvement, other studies
(e.g., Lee and Bowen 2006 ) indicate a positive rela-
tionship between parental education and parental
involvement. Hill et al. ( 2004 ) found that parental
involvement by more educated parents tended to
increase their children’s level of academic aspiration,
school behavior, and achievement, but that parental
involvement by lower educated parents only raised
academic aspirations without signiﬁ cantly improving
school behavior or achievement.
Sampson and colleagues (Sampson et al. 1997,
1999 ) , in an attempt to augment the generic social
capital metaphor, posit the related, but more circum-
scribed, notion of “collective efﬁ cacy.” They (Sampson
et al. 1999 : 635) conceive collective efﬁ cacy as “…a
task-speciﬁ c construct that relates to the shared expec-
tations and mutual engagement by adults in the active
support and social control of children.” They argue
that collective efﬁ cacy places more emphasis on the
“agentic” dimensions of community social relations
and—consonant with the social network “instrumen-
tal” approach to social capital—focuses on the pur-
poseful mobilization of resources toward desired (child
and youth) outcomes. In a study utilizing survey data
from residents in 342 Chicago neighborhoods,
Sampson et al. ( 1999 ) found neighborhood afﬂ uence
to be positively related to collective efﬁ cacy, as well as
to “reciprocated exchange” (the intensity of interfam-
ily and adult involvement in childrearing), and “inter-
generational closure” (extent to which adults and
children in a neighborhood are linked to one another).
While Ainsworth ( 2002 ) found that neighborhood
characteristics predicted educational outcomes almost
13 Similar results emerged when she conducted the analysis at the
level of census tracts and at the level of school districts.
28112 Education and Quality of Life
as strongly as family and school-related factors,
Duncan et al. ( 2001 ) found that family context is much
more important than neighborhood or school in rela-
tion to school achievement and delinquency.
results revealed much greater variability within neigh-
borhoods and schools than between different neigh-
borhoods and schools. Cook et al. ( 2002 ) contend that
neighborhood effects should be understood as merely
one context that contributes jointly, along with school,
nuclear family, and peer group, to student outcomes.
Schooling has a positive inﬂ uence on success in making
choices involving marriage and family size by allow-
ing better access to information for decision-making
(Wolfe and Haveman 2001 : 228). More educated indi-
viduals are more likely to be married, and marriage is
negatively related to various forms of distress, although
the effect of education on this is modest at best (Ross
and Van Willigen 1997 : 287).
Berrington ( 2001 ) found that enrollment in educa-
tion was a strong inhibitor of marriage among young
adults. Level of education is an especially important
determinant of marital status for women. Women with
less education tend to marry and have children earlier
than more educated women. Higher educational attain-
ment gives more educated women greater earning
power which equals greater economic independence
and greater freedom in deciding whether to marry or
not. Accordingly, marriage and childbearing tend to
occur later for more educated women. As well, women
with little or no educational credentials are more likely
to marry early than are men of similar educational
standing. (Blackwell and Bynner 2002 ) .
There is evidence of an educational effect on divorce,
and this effect is stronger for women than for men.
More educated women are generally less likely to
divorce than women with lower levels of education
(Tzeng 1992 ) . Less educated women are more likely
to marry and have children earlier, and early mar-
riage is related to higher likelihood of divorce
(Blackwell and Bynner 2002 : 9; also see Berrington
and Diamond 1999 ) . As well, women’s level of edu-
cational attainment and employment status are
important moderators of the consequences of divorce
for children (Kiernan 1996 ) . More educated women
(particularly those employed in well-paying jobs
before marital dissolution) tend to be better protected
from economic hardship postseparation. (Bianchi
et al. 1999 ) .
Educational homogamy effects marital stability.
Couples in which the wife has a higher level of educa-
tional attainment than the husband are about 28% more
likely to divorce than couples where each member has
the same level of education; when husbands have a
higher level of education than their wives, couples are
20% more likely to divorce than couples with the same
level of education (Tzeng 1992 ) .
Education may enhance communication skills
which protect against marriage breakdown. But in the
case of divorce, education is also positively associated
with ability to cope with divorce (Blackwell and
Bynner 2002 : 10).
Education is positively associated with delayed moth-
erhood and negatively associated with fertility rate,
especially among college educated women ( Rindfuss
et al. 1996 ) . That is, less educated women are more
likely have children earlier (Blackwell and Bynner
2002 ) , while more educated women are more likely to
delay motherhood (Heck et al. 1997 ; Ekert-Jaffé et al.
2002 ) . The birth rate among American women over
the age of 30 has increased in recent decades only
among those with 4-year university degrees (Martin
2000 ) . This ﬁ nding is consistent with the notion that
more educated women are waiting (perhaps to estab-
lish careers) before having children.
The increased opportunity cost for more educated
higher earning women is an important factor in delayed
childbearing. So is a shift in preference among more
educated parents from quantity to quality, that is,
toward greater intensity of investment in fewer chil-
dren, with the expectation that “higher expenditures of
time and money [will] raise the future productivity of
the child in the workforce and in everyday life”
(Greenwood 1997 : 506).
Kieran ( 1997 ) identiﬁ es a number of beneﬁ ts
associated with delayed marriage and/or parenthood,
1 4 Strong correlations for peer group are confounded by inadequate
control for self-selection.
282 J.D. Edgerton et al.
such as enhanced ﬁ nancial capacity to improve quality
of housing, consumer goods and leisure activities and
decreased likelihood of marital breakdown. As well,
delayed childbearing also often affords greater oppor-
tunity for women to become established in their careers
or employment situations which increases resilience to
economic hardship in the case of marital dissolution.
Teens (especially girls) with lower academic per-
formance are more likely to experience early parent-
hood and attendant social disadvantages (Kiernan
1997 ) . Teen parents are more likely to drop out of
high school, lack parenting skills, and live in poverty
(Maynard and McGrath 1997 ) . Children born to ado-
lescent mothers are academically and behaviorally dis-
advantaged relative to children born to older mothers
(Dahinten et al. 2007 ) and are more likely to become
teen parents themselves (Maynard and McGrath 1997 ;
Kiernan 1997 ) .
Numerous familial outcomes are associated with level
of educational attainment, including poverty, out-of-
wedlock childbearing, early parenthood, and child
abuse and neglect. All these outcomes are less preva-
lent among high school graduates than among early
school leavers (Maynard and McGrath 1997 : 130).
Wolfe and Haveman ( 2001 : 230) review a number of
studies and also conclude that there exists generally a
strong relationship between number of years of paren-
tal schooling and several important outcomes for their
offspring such as schooling, teenaged childbearing,
health, and criminal behavior. Higher parental educa-
tion is associated with ability to pay for better quality
childcare and residence in communities with more
extensive social service and educational resources,
positive peer groups, and lower crime (Maynard and
McGrath 1997 : 133).
While many of the child welfare beneﬁ ts of education
cited by Maynard and McGrath appear to be mediated
by the positive effect of more schooling on income,
there are also parental education effects above and
beyond the monetary advantage. Higher parental edu-
cation is associated with greater access to knowledge
about the developmental needs of children, greater
propensity to seek out and implement new childcare
information (Greenwood 1997 ) , increased quality of
parent-child interaction, and less negative and more
positive parenting behaviors (Feinstein et al. 2004 ) ,
greater probability of parental involvement with child’s
school, of reading to a child, and of helping with
homework (Pascarella and Terenzini
2005 ) .
Personal Safety/Future Security
As we have seen, educational disadvantage generally
translates into socioeconomic disadvantage. Such dis-
advantaged persons are disproportionately exposed to
various types of risk. They are more exposed to eco-
nomic risks such as unemployment, job insecurity, and
general economic hardship ( Abbot et al. 2006 ; Furlong
and Cartmel 1997 ; Perrons 2000 ) ; to environmental
hazards such as pollutants and toxins, proximity to
polluting industries, and insalubrious “ambient condi-
tions” such as poor housing quality (Evans and
Kantrowitz 2002 ; Lester et al. 2001 ; Liu 2001 ; Mohai
and Bryant 1992 ) ; and are often less well equipped to
deal with negative events or circumstances (e.g., lack
of marketable credentials, insufﬁ cient ﬁ nancial man-
agement knowledge, inadequate ﬁ nancial resources
for relocation, limited psychosocial coping skills).
Some aspects of the safety domain dovetail with the
health domain in that socioeconomic disparities in
safety are related to numerous disparities in health. As
Evans and Kantrowitz ( 2002 : 204) contend, much of
the “…link between SES and health derives from mul-
tiple exposures to a plethora of suboptimal environ-
mental conditions…The poor are most likely to be
exposed not only to the worst air quality, the most
noise, the lowest-quality housing and schools, etc., but
of particular consequence, also to lower-quality envi-
ronments on a wide array of multiple dimensions.” For
example, as noted before, higher educated individuals
are also less likely to have physically demanding jobs
which are associated with various negative effects on
health (Bound et al. 1995 ) . Cubbin and Smith ( 2002 :
365)—after reviewing a number of studies examining
the relationship between socioeconomic status and
injury—conclude that “SES has a strong inverse asso-
ciation with the risk of both homicide and uninten-
tional injuries in all ages; as individual or area SES
decreases, the risk of homicide or unintentional injury
increases.” In a similar vein, Adler et al. ( 1994 : 18)
observe that “…components of SES, including income,
education, and occupation, shape one’s life course and
are enmeshed in key domains of life, including (a) the
physical environment in which one lives and works
and associated exposure to pathogens, carcinogens,
28312 Education and Quality of Life
and other environmental hazards; (b) the social envi-
ronment and associated vulnerability to interpersonal
aggression and violence as well as degree of access to
social resources and supports….”
More educated individuals are less likely to suffer
the stress of economic hardship. The least qualiﬁ ed
workers are the most vulnerable to unemployment dur-
ing economic downturns (Gangl
2001 ) . Moreover,
those with higher educational attainment have greater
“ability to beneﬁ t from disequilibria” (Bowles et al.
2001a ) . In simple terms, they are better positioned to
take advantage of/proﬁ t from market trends and cycles
(i.e., to extract rents) or, conversely, to protect them-
selves and their families from economic trends and
cycles. Someone with a MBA is generally better
positioned to repackage him/herself in a changing
labor market (or migrate to a different market for new
opportunities) than is a manual laborer with grade 10
education. Such market resilience may also be
enhanced by a strong sense of agency/self-efﬁ cacy and
a more future-oriented time preference, both charac-
teristics that are associated with higher educational
attainment. As well, there is some indication that even
when income is low, education decreases the likeli-
hood of economic hardship by improving household
budget management (Mirowsky and Ross 1999 ) . This
difference may be related to the efﬁ ciency advantages
(“learned effectiveness”) apparent among the more
educated in health maintenance (Grossman and
Kaestner 1997 ; Gilleskie and Harrison 1998 ; Mirowsky
and Ross 2005 ) and environmental risk-averting
behavior (Smith 1997 ) . The better educated are likely
to have access to more relevant knowledge, to trust it,
and, given equal information, to be more proﬁ cient at
generating positive outcomes than those with less
education. This learned effectiveness advantage may
apply in ﬁ nancial management just as it does in health
There is also some indication of a negative relation-
ship between education and crime (Tauchen et al.
1994 ; Lochner and Moretti 2004 ) . Over two thirds of
incarcerated men in the USA in 1993 had not gradu-
ated high school (Freeman 1996 ) . The inhibitory effect
of education on crime seems to be primarily explained
in terms of increasing the cost opportunity—those with
more education and higher wages are more risk
averse—although there may also be effects related to
missed learning as well as peer inﬂ uence and lifestyle
factors associated with non-completion of high school
(Lochner 2004 ) . Consistent with this, several studies
(e.g., Freeman 1996 ; Machin and Meghir 2000 ; Gould
et al. 2002 ) have found negative relationships between
wages and criminal activity, although the empirical
relationship is not clear-cut (Lochner
2004 ) .
This chapter, although by no means exhaustive, has
sampled a fairly diverse body of research from multi-
ple disciplines intent on identifying various connec-
tions between education and an assortment of quality
of life outcomes. These studies vary in the degree to
which they attempt to account for threats to the valid-
ity of their ﬁ ndings. While an in-depth discussion of
the strengths and weaknesses of each study is beyond
the objectives of this chapter, in this section, we will
brieﬂ y touch upon some prominent threats to validity
that should be kept in mind when looking at returns to
education research. The two most common sets of con-
cerns pertain to (a) the spuriousness of reported educa-
tional effects due to inadequate consideration of
antecedent or intervening “third” variables and (b)
issues regarding the valid and reliable measurement of
In short, failure to adequately control for the inﬂ uence
of important third variables may lead to overestimation
or underestimation of the effects of education on
observed outcomes. That is, the more potentially con-
founding antecedent or intervening variables con-
trolled for, the more conﬁ dent one can be that the
observed relationship is in fact valid and that the dif-
ference (or some signiﬁ cant portion thereof) observed
in the outcome/dependent variable is due to the effect
of the predictor/independent variable. In addition to
level of educational attainment, there are a host of vari-
ables that might plausibly inﬂ uence some of the qual-
ity of life outcomes in question. The most prominent
factors are family background and ability as well as a
number of variables pertaining to mental and physical
health and psychological attributes (preferences) such
as motivation, aspirations, and time orientation.
Additionally, the beneﬁ ts of education may transpire
via both direct and indirect effects. For example,
284 J.D. Edgerton et al.
education has a substantial indirect effect on health
through income and wealth. Studies vary in terms of
how many and how well they control for these vari-
ables, but no study can incorporate them all. A notable
shortcoming of many studies is that they do not partial
income out when looking at the relationship between
education and various quality of life outcomes. The
basic idea behind the most common approach for
estimating the inﬂ uence of so-called third variables
(alternative explanations) is to compare the estimates
of the effect of educational attainment on a target out-
come when a particular variable (or set of variables) is
controlled for versus when it is not controlled for. The
observed difference in educational effects provides an
approximate indication of the inﬂ uence on returns to
education of the variable(s) in question. Practical limi-
tations (e.g., most available datasets are cross sectional
rather than longitudinal and/or are not likely to include
measures of all plausible control variables) prevent
any study from adequately considering all potentially
confounding variables, so we are left to weigh the
balance of complimentary and contradictory ﬁ ndings
across a body of studies as best we can. We also, of
course, need to exercise due caution in making causal
connections due to the correlational nature of most of
the returns to education research.
A classic example of the third variable issue is evi-
dent in the study of the education-income relationship.
Two prominent third variables that must be considered
are family background and ability. First, individuals
with higher education tend to have parents with higher
education as well. It could be that the income advan-
tage results from family background factors (i.e.,
ﬁ nancial, cultural, and social capital advantages
received from well-educated, afﬂ uent parents). Second,
it can be argued that those who attain higher levels of
education do so because they have greater ability and
that those individuals would earn higher wages even
without higher schooling. In short, it might actually be
underlying ability—not education—that is responsible
for higher income. In addition to statistically control-
ling for factors such as gender, race, and SES, some-
times preexisting groups can be incorporated into a
study in order to increase control over outcome-relevant
variance. For instance, intrafamily comparisons pro-
vide an opportunity to control for family background
effects on earnings while identical twin studies are a
useful (though impractical) method for isolating the
effect of schooling on earnings from not only family
background variation but also variation in individual
A related source of confounding variance in returns
to education research is “selection bias.” The alterna-
tive explanation offered by the selection hypothesis is
that individuals from higher SES backgrounds, with
higher ability, exhibiting a particular cluster of psycho-
logical attributes and robust health are more likely to
attain (to be selected into) higher levels of education;
thus, these factors account for part (some would argue
most) of the effects of educational attainment. For
example, there is some indication that the positive
association between educational attainment and health
is due not to the effects of education on health status
but rather to the effects of health (particularly in the
school years) on educational attainment: individuals
with better health are more apt to persist in school and
to reach higher levels of educational attainment
(Grossman and Kaestner 1997 ) . The basic question
concerns the inﬂ uence of selection versus social causa-
tion (i.e., education causes adult outcomes such as
health status). Haas ( 2006 ) found that poorer child-
hood health was negatively associated with educational
attainment and lifelong returns to education (adult
occupational SES, earnings, and wealth), a ﬁ nding
consistent with the selection hypothesis. But he also
found that the association between SES and adult
health persisted above and beyond such selection
effects; that is, adult SES had some signiﬁ cant effect
on adult health regardless of childhood health. In sum,
he found support for both selection and social causa-
tion. So while there is evidence of a selection bias in
effect, this bias does not appear to fully account for the
observed relationship between education and various
quality of life outcomes.
Measuring Educational Attainment
Educational attainment is often measured by number
of years/grades or highest degree obtained, but as
numerous commentators have pointed out such mea-
sures do not adequately capture all the relevant aspects
of education, such as variation in quality of education
(Behrman et al. 1997 ) , or the value of different creden-
tials that require the same years of schooling, nor do
such attainment indicators apply with equal accuracy
across different national contexts (Kerckhoff et al. 2002).
National education systems vary along a number of
28512 Education and Quality of Life
important dimensions such as extent of formal stratiﬁ -
cation (i.e., tracking or streaming), degree of standard-
ization and credential specialization, and articulation
with the labor market (Kerckhoff 2001 ; Müller and
Shavit 1998 ; Sullivan and Smeeding 1997 ) . For exam-
ple, years of schooling is a more valid measurement of
education in the USA than in many European coun-
tries with much more differentiated credential systems
and multiple pathways of school-to-work transition.
As well, utilizing years of schooling assumes that the
effect of educational attainment is linear and that the
returns to schooling increase linearly per additional
year of education. But there is also evidence of nonlin-
ear effects such as the credentialing or sheepskin effect,
where inordinate wage premiums are often evident for
degree holders in comparison to nondegree holders
with similar total years of schooling (Card 1998 ;
Pascarella and Terenzini 2005 ) .
Two of the most widely used standards for classify-
ing educational credential across countries are the
ISCED (International Standard Classiﬁ cation of
Education) and CASMIN (Comparative Analysis of
Social Mobility in Industrial Nations) schemes.
Traditionally, CASMIN ﬁ ts the education credential
systems of some countries better (many European
countries) while the ISCED appears to better ﬁ t other
countries (e.g., the USA) although there are examples
of CASMIN being modiﬁ ed to incorporate these other
countries (Kerckhoff et al. 2002; Müller and Shavit
1998 ) . Another approach being developed by OECD/
INES mitigates the incommensurate credentials prob-
lem by taking a broader picture of what education
actually provides individuals. This new approach is
based on the notion that schooling imparts more than
just academic skills to students and thus seeks to aug-
ment measurement of curricular subjects (i.e., math,
science, and reading literacy) with measurement of
“cross-curricular competencies,” or knowledge and
skills that transcend speciﬁ c subject areas (OECD
1997 ; Peschar 2004 ) . Cross-curricular competencies
are conceived as those competencies (life skills)
required by individuals in order to be responsible, pro-
ductive, fully functioning members of society. Four
important cross-curricular competency domains have
currently been identiﬁ ed: civics, problem-solving,
self-related cognitions, oral and written communica-
tion. The OECD’s Programme for International Student
Assessment has incorporated self-regulated cognitions
(learning) and problem-solving items into subsequent
cycles. Other similar efforts at developing indicators of
the general life skills imparted by schooling are also
underway (e.g., Hautamaki 1998 ; Meijer et al. 2001 ).
More comprehensive sets of indicators may provide a
more multidimensional understanding of how school-
ing contributes to preparing students to meet the per-
sonal, social, and economic challenges of modern life.
For example, it may help further clarify the extent to
which the positive effect of education on earnings is
due to the cultivation of cognitive versus noncognitive
This chapter has reviewed a wide array of research on
the impact of educational attainment on quality of life.
Adopting Cummins’ ( 1996, 1997 ) quality of life
schema as a heuristic framework, we looked at edu-
cational effects in seven broad life domains: achiev-
ing in life, material well-being/standard of living,
emotional well-being/resiliency, physical health,
community, intimate relationships, and personal
safety/future security. Of course, no life domain is an
island; each exists jointly with the others. Accordingly,
the effects of educational attainment on QoL are mul-
tidimensional (cutting across life domains) and often
reciprocal (conditioning of and conditioned by
domains) in nature. In light of this, we deem it useful
at this juncture to give some consideration to the
dynamic nature of the relationship between education
and quality of life.
Behrman et al. ( 1997 : 3) suggest there are basically
three underlying pathways by which schooling imparts
beneﬁ ts: (a) improving the stock of knowledge and the
analytical skills individuals use to guide their behavior,
(b) altering individuals’ preferences, and (c) altering
the constraints/opportunities presented to individuals.
(a) Enhancing Knowledge and Cognitive
Pallas ( 2000 : 505) sums up key ﬁ ndings in this area as
indicating that “individuals with more schooling have
access to a richer array of information than those with
less schooling. They know more about their social,
cultural, and political worlds, and they can apply that
knowledge to shape their futures.” In short, more
286 J.D. Edgerton et al.
educated individuals are able to bring more informa-
tion to bear in decision-making situations, thus, on the
whole, improving the quality of those decisions.
Pascarella and Terenzini ( 2005 ) reviewed a host of
studies from the 1990s that compared freshmen to
seniors in terms of a number of basic dimensions of
learning and cognitive development. In addition to sta-
tistically signiﬁ cant gains in fundamental knowledge
domains such as English, Mathematics, Science, and
Social Sciences, senior university students also dem-
onstrated statistically signiﬁ cant improvements over
their freshman counterparts in terms of general intel-
lectual sophistication. That is, they exhibited greater
propensity for critical thought and more advanced crit-
ical thinking skills, greater reﬂ ective judgment-think-
ing (“the ability to use reason and evidence to address
ill-structured problems”), and greater epistemological
sophistication or maturity. Evident long-term effects
are that, compared to high school graduates, postsec-
ondary graduates are not only “more knowledgeable
and more proﬁ cient at becoming informed” but that
they are also better equipped and more amenable to
lifelong learning and continued intellectual growth.
There are numerous beneﬁ ts to enhanced cognitive
proﬁ ciency, some of which are related to productivity,
employment, and earnings and some of which are
related to other aspects of life quality, such as health.
For example, there is strong suggestion that the more
educated, due to greater access to information and
greater proﬁ ciency at analyzing and implementing
new information, are “more efﬁ cient producers of
health” (Grossman and Kaestner 1997 ) . Education
imparts analytical and problem-solving skills (“learned
effectiveness”) that transfer to various aspects of life
including health maintenance (Mirowsky and Ross
2005 ) . More educated individuals have greater famil-
iarity with the knowledge production process which
may translate into greater trust in “expert” recommen-
dations and greater likelihood of compliance. Thus,
more educated individuals are more likely (and better
equipped) to comprehend the relevance of expert rec-
ommendations and to be more effective in mitigating
risk accordingly (Smith 1997 ) .
This increased receptivity to learning is a primary
consequence of the socialization effect of schooling. In
addition to introducing new knowledge and training
the mind to approach information, problems and ideas
with more sophistication, schooling also shape indi-
(b) Changing Preferences
When economists talk of an individual’s preferences,
they are essentially talking about a constellation of
personal attributes or tendencies, such as an individu-
al’s general values orientation or priorities—the
motives, attitudes, and ethics that guide individual
conduct (what psychologists would see as facets of
personality). Development of preferences occurs in
concert with cognitive development, the two manifest-
ing synergistically as habits of mind and behavior.
Thus, preferences include a host of possible personal
traits such as work ethic, primary incentives, desire for
autonomy, comfort with delayed gratiﬁ cation, political
beliefs, and various lifestyle choices such as diet and
Level of education also has an effect on the values
and practices that parents model for their children.
Lareau ( 2000, 2003 ) has provided ethnographic evi-
dence of important differences between middle- and
working-class parents in childrearing practices and
value orientations that translate into advantageous
educational outcomes for middle-class children.
Middle-class parents tend to be more hands on in their
children’s education, provide greater extracurricular
learning opportunities, encourage analytical thought,
impart greater achievement motivation, and model
social skills (such as self-assertiveness and negotia-
tion) conducive to success within “the rules of the
game” that constitute formal education and later occu-
pational contexts. Kohn ( 1969 ) observed an associa-
tion between education and valuing autonomy even
when controlling for subsequent occupation; increas-
ing level of education was associated with increasing
prioritization of autonomy. One of the aspects of
autonomy that is most important to individual psycho-
logical health is control over the work process (Kohn
1976 ; Kohn and Schooler 1982 ) . 16 A notable corollary
15 Farkas ( 2003 : 556) notes that a growing body of research sug-
gests that “[p]atterns of habitual behavior, particularly the extent
of conscientiousness or good work habits, developed from birth
through adolescence, in conjunction with the cognitive skills
developed alongside these behaviors, determine school success
and schooling and occupational attainment. These skills and
habits then combine with skills and habits developed on the job
to determine employment and earnings success.”
16 Kohn et al. ( 1990 ) found that while the signiﬁ cant relationship
between level of education and priority given to autonomy held
in the USA, it was not evident in Japan or Poland.
28712 Education and Quality of Life
of the desire for autonomy is self-efﬁ cacy or belief in
one’s ability to exert control over valued outcomes.
Formal education instills an analytical and problem-
solving orientation that leads to a greater sense of per-
sonal agency or self-efﬁ cacy which strengthens resolve
to initiate action and to better manage various aspects
of one’s life such as health status (Mirowsky and Ross
1999, 2005 ) . Similarly, Goldsmith et al. (1997) found
that higher educational attainment is associated with
enhanced “psychological capital,” the motivational
and attitudinal requisites—particularly high self-
esteem and internal locus of control—leading to higher
wage employment. Pascarella and Terenzini ( 2005 ) , in
their extensive review of the literature on the affects of
college, conclude that numerous studies indicate that
postsecondary education has a net effect (i.e., persists
after a number of potentially confounding variables
are controlled) on student self-concept. As well, they
identify a small but signiﬁ cant and long-term increase
in university students’ internal locus of control (per-
ception of internal or self-control versus external or
other-control of one’s life) as well as fairly consistent
indications of improved social skills and social self-
Pascarella and Terenzini ( 2005 ) also ﬁ nd evidence
that attainment of university education is positively
associated with increased valuation of the intrinsic
rewards of work such as interesting tasks, freedom to
use one’s skills and talents, and involvement in decision-
making. They also observe an association between uni-
versity attendance and long-term changes in graduates’
“sociopolitical attitudes and values and civic community
engagement.” Enduring changes include increased like-
lihood of voting and direct participation in the political
process, as well as involvement in civic and community
initiatives (Pascarella and Terenzini 2005 ) .
Another important preference that is affected by
education is what economists term “rate of time pref-
erence.” Basically, this construct refers to the relative
value an individual places on immediate versus future
consumption or gratiﬁ cation, or even more pointedly,
their degree of “patience” ( Becker and Mulligan 1997 ) .
People vary in their capacity to forgo more immediate
consumption, to invest time, effort, and money with
the promise of greater payoff (pleasure or “utilities”)
in the future. While some argue that a lower rate of
time preference for the present (longer time horizon)
increases the level of formal schooling attainment,
Becker and Mulligan ( 1997 : 736) suggest that schooling
enhances the ability to delay gratiﬁ cation because it
teaches problem-solving and abstract thinking skills
such as scenario simulation, and consequently, “edu-
cated people should be more productive at reducing
the remoteness of future pleasures.” As well, they sug-
gest that education increases patience indirectly via its
positive effect on earnings in that those with greater
wealth are better positioned to cultivate long-term
returns. Others suggest that the relationship between
education and time preference is probably one of recip-
rocal effects: the ability to delay gratiﬁ cation enhances
educational attainment, and greater educational attain-
ment enhances ability to delay gratiﬁ cation (Leigh
1998 ) . Thus, more educated parents tend to cultivate a
more future-oriented time preference in their children
to begin with, and this preference is further reinforced
by successful educational attainment. Health status,
occupational prestige, income, and credit rating are
examples of areas in one’s life potentially affected by
one’s time preference.
(c) Lessening Constraints
and Increasing Opportunities
Learning begets learning, schooling provides access to
substantive knowledge, but it also creates awareness of
and potential access to further learning opportunities,
thereby broadening the aspirational horizon of stu-
dents. For instance, successful students, as they
advance, become introduced to previously unknown
educational and occupational options. Or academic
success may lead to ﬁ nancial assistance such as schol-
arships and bursaries that enable a student to further
their education beyond what their ﬁ nancial resources
might otherwise afford.
Higher educational attainment also leads to occupa-
tions that are more likely to provide the opportunity for
continued reﬁ nement of the cognitive and interper-
sonal skills developed in school. As well, occupations
requiring higher education credentials tend to provide
relatively high earnings which, in turn, enable access
to wider range of material and nonmaterial resources
and opportunities linked to an array of positive long-
term outcomes (Pascarella and Terenzini 2005 ) . Higher
earnings enable individuals to live in safer, better-
resourced (libraries, schools, recreational facilities,
etc.) communities and to afford healthier lifestyles
(e.g., healthier food, gym memberships, exercise
288 J.D. Edgerton et al.
equipment, personal trainers, etc.). In addition, there
are intergenerational repercussions, in that offspring of
highly educated parents are more likely to attain
high levels of education (and attendant beneﬁ ts)
The effects of education on a broad spectrum of life
outcomes are mediated by workplace conditions. For
example, better educated individuals are generally less
likely to be employed in dangerous working conditions
and generally have better access to non-alienated work
(less routinized and monotonous, greater autonomy,
variety, and creativity) which decreases physical and
mental distress (Ross and Van Willigen 1997 ) and the
level of satisfaction derived from work (Ross and
Reskin 1992 ) .
These three pathways are interrelated; change or
development in one is accompanied by change or
development in the others, and each—to varying
degrees—affects aspects of quality of life, within and
across speciﬁ c domains and in general. Thus, like a
series of feedback loops, the effects of education in
one domain may impact and be impacted by the effects
of education in other domains. By way of simple illus-
tration, remaining in school improves an individual’s
knowledge base and the analytical tools they bring to
bear upon a range of circumstances, which may pro-
duce more successful responses (e.g., school achieve-
ment, task performance at work) and thus better
opportunities (scholarship, promotion to a better pay-
ing job), which may, in turn, reshape individual prefer-
ences (reinforces hard work ethic, expands time
horizon), which may increase the probability of the
person pursuing further schooling (either initially or
via upgrading) thereby increasing occupational and
economic status. Of course, this example ignores a
number of other factors that may differentially impinge
upon individual educational trajectories (e.g., family
background, ability, gender, race, school resources,
etc.). The array of combinations of factors that could
plausibly affect the educational attainment-quality of
life relationship is sizeable and remains a primary
challenge to researchers.
None of the returns to education studies considered
here has incorporated the full breadth of plausibly
inﬂ uential variables into its design; social reality is too
complex. Studies vary in the number of plausible inﬂ u-
ences they attempt to account for and in the rigor with
which they do so. Practical and methodological limita-
tions persist (e.g., selection bias and appropriate
measurement of education), but viewed across the
laminate proﬁ le of a large number of studies, certain
patterns become apparent. Schooling does affect (and
is affected by) individual quality of life by enhancing
knowledge and analytical capacity, shaping prefer-
ences, and expanding opportunities. These changes
feed off one another and have repercussions across all
seven life domains examined; change along one path-
way can affect the other pathways and one or more
domains which can, in turn, affect each other.
Schooling is positively associated with achieving
in life; in simple terms, success breeds success; those
who do well in school are likely to continue onto
higher levels of educational attainment which is asso-
ciated with higher socioeconomic attainment (occu-
pational status, income, etc.). Concomitant with
enhanced achievement, schooling also raises material
well-being by increasing economic returns. While
factors such as family background, ability, and health
inﬂ uence educational attainment and its effect on eco-
nomic returns, there is strong evidence for an effect of
schooling on earnings net of these factors. The exact
mechanism by which education enhances economic
returns is still not completely clear. Some ﬁ ndings
suggest that education increases the productivity of
workers by increasing knowledge and skills, while
other ﬁ ndings are more consistent with the notion that
education socializes individuals into the values, hab-
its, and attitudes favored by employers as conducive
to successful performance. From the studies reviewed
here, it seems that both views contribute something
integral to the answer that is emerging and will con-
tinue to emerge as the breadth and sophistication of
available data keeps growing.
Education also beneﬁ ts psychological and physical
health. While there is evidence of direct (net of other
factors) health beneﬁ ts to education (such as greater
health knowledge and “learned effectiveness” and
increased psychological resiliency via a greater com-
pliment of coping skills), many of the salutary effects
of education are indirect consequences of work,
whether it be the actual conditions of the workplace
(autonomy, nature of tasks and relationships, opportu-
nity for continued learning, and personal fulﬁ llment)
or the socioeconomic repercussions (occupational
prestige, ﬁ nancial resources to pursue other interests,
etc.). The various health advantages related to educa-
tion and socioeconomic status are cumulative in nature,
growing across (and extending) the lifespan. Part of
28912 Education and Quality of Life
the association between health and education seems to
be due to the effect of early health on subsequent
school attainment, but education still appears to pro-
vide signiﬁ cant health beneﬁ ts above and beyond this
There also seem to be indications of positive
associations between education and richer social
networks and social resources (social capital) as
well as context effects related to neighborhood of
residence and schoolmates, although contradictory
results also exist which suggest such connections
may be spurious. Again, the complexity of social
reality and the difﬁ culty associated with accounting
for all plausible inﬂ uences presents a stern test for
scientiﬁ c consensus.
Our review also looked at a number of studies point-
ing to a positive association between education and
various dimensions of intimate relationships such as
later onset of marriage and parenthood, greater paren-
tal resources and skills, and better child welfare. The
beneﬁ ts to women appear particularly strong in a num-
ber of respects: delayed marriage and/or motherhood
are associated with higher educational attainment,
greater economic resources, and more personal free-
dom for women, and educational attainment is nega-
tively associated with teen parenthood (the
disadvantages of which—such as poverty—seem to
fall disproportionately upon young mothers). Lastly, in
the domain of personal safety/future security, it appears
that education is associated with decreased likelihood
of exposure to an assortment of economic, social, and
environmental risks and that when such stressors are
encountered, the more educated are better equipped to
effectively cope or adapt.
In sum, while there are still numerous questions
and gaps remaining, the case for the positive effects
of educational attainment on quality of life is in the
balance very convincing. But it remains incumbent
upon researchers to keep striving toward the increas-
ingly comprehensive data required to bring the
blurry aspects into focus. For example, one increas-
ingly popular research strategy, necessarily given
short shrift in this chapter, is cross-national com-
parison. Studying the differences and similarities
between the institutional features of national educa-
tional systems promises to further reveal signiﬁ cant
insights into the importance of societal and institu-
tional context in determining quality of life returns
International Standard Classiﬁ cation of Education
( ISCED ): The International Standard Classiﬁ cation of
Education (ISCED-97) is used to deﬁ ne the levels and
ﬁ elds of education used as part of the OECD’s system
of education indicators (OECD 2006). For details on
ISCED 1997 and how it is nationally implemented, see
Classifying Educational Programmes: Manual for
ISCED-97 Implementation in OECD Countries (OECD
1999b ) . Levels include Pre-primary education (ISCED
0) , Primary education (ISCED 1) , Lower secondary
education (ISCED 2) , Upper secondary education
(ISCED 3) , Postsecondary non-tertiary level of educa-
tion (ISCED 4) , Tertiary-type A education (ISCED
5A) , Tertiary-type B education (ISCED 5B), and
Advanced Research Qualiﬁ cations (ISCED 6) .
Upper secondary education ( ISCED 3 ) : Upper sec-
ondary education (ISCED 3) corresponds to the ﬁ nal
stage of secondary education in most OECD countries.
Instruction is often more organized along subject mat-
ter lines than at ISCED level 2, and teachers usually
need to have a higher level, or more subject-speciﬁ c,
qualiﬁ cations than at ISCED 2. The entrance age to
this level is typically 15 or 16 years. There are substan-
tial differences in the typical duration of ISCED 3 pro-
grams both across and between countries, typically
ranging from 2 to 5 years of schooling. ISCED 3 may
either be “terminal” (i.e., preparing the students for
entry directly into working life) and/or “preparatory”
(i.e., preparing students for tertiary education).
Programs at level 3 can also be subdivided into three
categories based on the degree to which the program is
speciﬁ cally oriented toward a speciﬁ c class of occupa-
tions or trades and leads to a labor-market relevant
qualiﬁ cation: general, pre-vocational or pre-technical,
and vocational or technical programs.
Postsecondary non-tertiary level of education ( ISCED
4 ): Postsecondary non-tertiary education straddles the
boundary between upper secondary and postsecondary
education from an international point of view, even
though it might clearly be considered upper secondary
or postsecondary programs in a national context.
Although their content may not be signiﬁ cantly more
advanced than upper secondary programs, they serve
to broaden the knowledge of participants who have
290 J.D. Edgerton et al.
already gained an upper secondary qualiﬁ cation. The
students tend to be older than those enrolled at the
upper secondary level.
Tertiary-type A education ( ISCED 5A ): Tertiary-type
A programs (ISCED 5A) are largely theory based and
are designed to provide sufﬁ cient qualiﬁ cations for
entry to advanced research programs and professions
with high skill requirements, such as medicine, den-
tistry, or architecture. Tertiary-type A programs have a
minimum cumulative theoretical duration (at tertiary
level) of 3 years full-time equivalent, although they
typically last 4 or more years. These programs are not
exclusively offered at universities. Conversely, not all
programs nationally recognized as university programs
fulﬁ ll the criteria to be classiﬁ ed as tertiary-type A.
Tertiary-type A programs include second degree pro-
grams like the American Master. First and second pro-
grams are subclassiﬁ ed by the cumulative education of
the programs, i.e., the total study time needed at the
tertiary level to complete the degree.
Tertiary-type B education ( ISCED 5B ): Tertiary-type B
programs (ISCED 5B) are typically shorter than those
of tertiary-type A and focus on practical, technical, or
occupational skills for direct entry into the labor market,
although some theoretical foundations may be covered
in the respective programs. They have a minimum dura-
tion of 2 years full-time equivalent at the tertiary level.
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