Part 2. The Relationship between Health and Schooling

Introduction to Part 2



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or theoretical and empirical reasons, the positive relationship between more

schooling and better health is one of the most fundamental relationships in

health economics. Clearly, the relationship is part of the massive literature in

health economics on the determinants of the health of the population, a literature that originates from a demand for health model that I developed and that

serves as the basis of part 1 of this book. That model emphasizes that medical

care is only one of many determinants of health, and it is natural to explore others. Moreover, my model views health as a form of human capital and therefore

a determinant of earnings. Hence, it is natural to allow for and explore complementarities between health capital and other forms of human capital, the most

important of which is knowledge capital, as proxied by the number of years of

formal schooling completed.

Empirically, the importance of the relationship is highlighted by Gina

Kolata (2007, p. 1): “The one social factor that researchers agree is consistently

linked to longer lives in every country where it has been studied is education.

It is more important than race; it obliterates any effects of income.” It also is

underscored by Ellen Meara, Seth Richards, and David Cutler (2008, p. 350):

“With the exception of black males, all recent gains in life expectancy at age

twenty-five have occurred among better educated groups, raising education differentials in life expectancy by 30 percent.” Even more dramatically, Michael



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L. Spittel, William T. Riley, and Robert M. Kaplan (2015) summarize evidence

suggesting that the effects of schooling on life expectancy are much larger

than mammography screenings or elevated LDL cholesterol. The increase in

life expectancy experienced by women who undergo a yearly mammography

screening compared to those who do not amounts to one month. The difference in that outcome between those with elevated LDL cholesterol compared

to those with normal cholesterol is roughly six months. On the other hand, the

difference in life expectancy between those with less than a high school degree

and those with an advanced degree is at least ten years.

The relationship between schooling and health is more than just of theoretical and empirical interest. Improvements in health are widely accepted goals in

developed and developing countries. In a 2002 issue of Health Affairs devoted

primarily to the nonmedical determinants of health, Nobel Laureate Angus

Deaton (2002) argues that policies to increase education in the United States,

and to increase income in developing countries, are very likely to have larger

payoffs in terms of health than those that focus on health care, even if inequalities in health rise. The same proposition, with regard to the United States, can be

found in a much earlier study by Richard Auster, Irving Leveson, and Deborah

Sarachek (1969). Because more education typically leads to higher income,

policies to increase the former appear to have large returns for more than one

generation throughout the world.

Efforts to improve the health of an individual by increasing the amount

of formal schooling that he or she acquires, or that try to improve child health

by raising parental schooling, assume that the schooling effects summarized

previously imply that more schooling causes better health. Yet reverse causality

from health to schooling may exist, and omitted “third variables” may cause

schooling and health to vary in the same direction. Governments can employ a

variety of policies to raise the educational levels of their citizens. These include

compulsory schooling laws, new school construction, and targeted subsidies to

parents and students. If the third-variable hypothesis and the hypothesis that

health causes schooling have some validity, evaluations of the impacts of these

policies on health should not be based on studies that simply correlate measures

of health and schooling.

Students in poor health are almost certain to miss more days of school

due to illness than their healthy peers and may also learn less while they are

in school. Both factors suggest negative effects of poor health in childhood on

school achievement and ultimately on years of formal schooling completed.

Furthermore, this causal path may have long-lasting effects if past health is

an input into current health status. Thus, even for nonstudents, a positive relationship between health and schooling may reflect causality from health to



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schooling in the absence of controls for past health. Health also may cause

schooling because a reduction in mortality increases the number of periods over

which the returns from investments in knowledge can be collected.

Productive and allocative efficiency models generate causality from education to health. In the former model, which is developed in detail in part 1 of this

book, the more educated are assumed to obtain more health output from given

amounts of medical care and other inputs. In the latter model, the more educated

are assumed to pick a different input mix to produce health than the less educated.

That mix gives them more output than the mix selected by the less educated. For

example, the more educated may have more knowledge about the harmful effects

of cigarette smoking or about what constitutes an appropriate diet.

Health and schooling are both endogenous, so unobserved “third variables”

may cause both of these outcomes to vary in the same direction. Victor R. Fuchs

(1982) identifies time preference as perhaps the key third variable. He argues

that people who are more future oriented (who have a high degree of time

preference for the future or discount it at a modest rate) attend school for longer periods of time and make larger investments in their own health and in the

health of their children. Thus the effects of schooling on these outcomes are

biased if one fails to control for time preference.1

The time preference hypothesis is analogous to the hypothesis that the positive effect of schooling on earnings may be biased upward by the omission

of ability. The latter hypothesis and the general issue of whether more schooling causes higher earnings have generated a massive number of studies in the

labor economics literature (see David Card 1999, 2001 for reviews of many

of these studies). The related literature on the relationship between health and

schooling in health economics is not as large but is rapidly growing. For example, in Grossman (2015), I counted thirty-eight such studies in the period from

2010 through 2014. And that count excludes the more than twenty papers that

appeared in a special issue of Social Science and Medicine in February 2015

titled “Educational Attainment and Health: Contextualizing Causality (Jennifer

Karas Montez and Esther M. Friedman 2015).

The studies that investigate whether more schooling causes better health

employ one of three econometric procedures. The first one directly includes

such hard-to-measure third variables as time preference, cognitive development,

noncognitive development, and past health. The second procedure controls for

unobserved genetic and environmental factors by examining the effects of differences in schooling obtained by identical twins on differences in their health

outcomes. The third procedure employs the technique of instrumental variables.

The idea here is to find exogenous variables that are correlated with schooling

but not correlated with unmeasured variables that affect health. In addition, they



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have no effect on health, with schooling held constant. These variables serve

as instruments for schooling in the estimation of health equations by two-stage

least squares. One such instrument is the enactment of a law that increases the

required amount of formal schooling.

In addition to employing three alternative econometric techniques, the

health-schooling studies address three types of relationships: (1) the relationship between an individual’s own schooling and his or her own health;

(2) the relationship between parents’ schooling and their children’s health;

and (3) the relationship between schooling and mechanisms that may lead

to worse or better health outcomes. Examples are health knowledge, fertility

choices, and such unhealthy behaviors as cigarette smoking, excessive alcohol consumption, and overeating and lack of exercise—sometimes reflected

by a large body mass index (BMI) and obesity.2

I have been investigating whether more schooling causes better health since

I began to work on my PhD dissertation in 1966. I have studied all three schooling–health relationships just mentioned using direct inclusion of third variables

and instrumental variables. I have not employed twin differences in my research

but summarize findings that do so in the afterword to this section of the book.

The first paper in this section (Grossman 1976) is my initial attempt to

focus on the causality issue in a systematic fashion. It sets the stage for the

large literature on this topic that has emerged since the mid- to late 1980s. My

strategy in the paper is to try to control for as many hard-to-measure third variables as possible.

I conclude that schooling has a significant positive impact on the current

self-rated health of middle-aged white males in the NBER-Thorndike sample. The estimated schooling effect controls for health in high school, parents’

schooling, scores on physical and mental tests taken by the men when they were

in their early twenties, current hourly wage rates, and property income. My

finding is particularly notable because all the men graduated from high school.

Hence, it suggests that the favorable impact of schooling on health persists even

at high levels of schooling.

I also identify three endogenous mechanisms via which the schooling effect

operates. More educated men marry more educated women, are more satisfied

with their jobs, and are less likely to be overweight. In turn, these three factors

have positive impacts on their health.

My analysis of the mortality experience of the Thorndike sample between

1955 and 1969 confirms the important role of schooling in health outcomes. This

analysis is restricted to men who reported positive full-time salaries in 1955.

In the fitted logit functions, schooling has a positive and statistically significant effect on the probability of survival. Indeed, schooling is the only variable



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whose logit coefficient differs from zero in a statistical sense. The schooling

effect is independent of the level of median salary in 1955 and suggests that, in

the vicinity of the mean death rate, a one-year increase in schooling lowers the

probability of death by 0.4 percentage points or by 14 percent relative to the

mean death rate of 2.8 percent in the fourteen-year period at issue.

Like the first paper, the second paper in this section is an early forerunner

of the large literature on the schooling–health causality question (Shakotko,

Edwards, and Grossman 1981). Although the empirical techniques are similar to

those in the first paper, the focus shifts to some extent to the effects of parents’

schooling on the health of their children. We use a panel that received physical examinations and took IQ and cognitive development tests when they were

approximately ages eight and fourteen to investigate the nature–nurture controversy and to obtain estimates of the impact of cognitive development in childhood (an important precursor of the ultimate amount of schooling an individual

obtains) on health in adolescence. We find that mother’s schooling has a positive

impact on a variety of health outcomes in adolescence (at age fourteen), with

health in childhood (at age eight) and at birth held constant. We also find that an

increase in cognitive development at age eight improves health at age fourteen.

The last two papers in the section, which are much more recent than the

first two, employ the technique of instrumental variables to assess whether

more educated parents have healthier infants and engage in health behaviors

that contribute to this outcome (Chou, Liu, Grossman, and Joyce 2010; Dinỗer,

Kaushal, and Grossman 2014). Both studies use compulsory school reform as

the instrument for schooling: the first in Taiwan and the second in Turkey. Chou

et al. (2010) uncover negative effects of mother’s schooling on low birth weight,

neonatal mortality, postneonatal mortality, and infant mortality in Taiwan. Dinỗer

et al. (2014) find positive effects of mothers schooling on age at first marriage

and at first birth, a negative effect on number of pregnancies, and weak evidence

of negative effect on infant mortality in Turkey.



NOTES

1. For a detailed discussion of the framework employed to study the health–schooling relationship, see Grossman (2006) and the references that I cite in that paper. My discussion

includes a model in which schooling causes health because it makes people more future

oriented.

2. I consider fertility and mother’s age at birth as outcomes because children in large families tend to have worse health outcomes than those in smaller families, and children born

to teenage mothers have worse outcomes than those born to older mothers.



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REFERENCES

Auster, Richard, Irving Leveson, and Deborah Sarachek. 1969. “The Production of Health: an

Exploratory Study.” Journal of Human Resources 4(4): 411–436.

Card, David. 1999. “The Causal Effect of Education on Earnings.” In Handbook of Labor

Economics, Volume 3, ed. Orley Ashenfelter and David Card. Amsterdam: Elsevier Science B.V., North-Holland Publishing: 1801–1863.

Card, David. 2001. “Estimating the Return to Schooling: Progress on Some Persistent Econometric Problems.” Econometrica 69(5): 1127–1160.

Chou, Shin-Yi, Jin-Tan Liu, Michael Grossman, and Ted Joyce. 2010. “Parental Education

and Child Health: Evidence from a Natural Experiment in Taiwan.” American Economic

Journal: Applied Economics 2(1): 33–61.

Deaton, Angus. 2002. “Policy Implications of the Gradient of Health and Wealth. Health

Affairs 21(2): 1320.

Dinỗer, Mehmet Alper, Neeraj Kaushal, and Michael Grossman. 2014. “Women’s Education:

Harbinger of Another Spring? Evidence from a Natural Experiment in Turkey.” World

Development 64(December): 243–25.

Fuchs, Victor R. 1982. “Time Preference and Health: An Exploratory Study.” In Economic

Aspects of Health, ed. Victor R. Fuchs. Chicago: University of Chicago Press: 93–120.

Grossman, Michael. 1976. “The Correlation between Health and Schooling.” In Household

Production and Consumption, ed. Nestor E. Terleckyj. Studies in Income and Wealth,

Volume 40, by the Conference on Research in Income and Wealth. New York: Columbia

University Press for the National Bureau of Economic Research: 147–211.

Grossman, Michael. 2006. “Education and Nonmarket Outcomes.” In Handbook of the Economics of Education, Volume 1, ed. Eric Hanushek and Finis Welch. Amsterdam: Elsevier

Science B.V., North-Holland Publishing: 577–633.

Grossman, Michael. 2015. “The Relationship between Health and Schooling: What’s New?”

Nordic Journal of Health Economics 3(1): 7–17.

Karas Montez, Jennifer, and Esther M. Friedman, ed. 2015. “Special Issue: Educational

Attainment and Adult Health: Contextualizing Causality.” Social Science and Medicine

127(February): 1–206.

Kolata, Gina. 2007. “A Surprising Secret to Long Life: Stay in School.” New York Times,

January 3: 1.

Meara, Ellen, Seth Richards, and David M. Cutler. 2008. “The Gap Gets Bigger: Changes in

Mortality and Life Expectancy by Education, 1980–2001.” Health Affairs 27(2): 350–360.

Shakotko, Robert A., Linda N. Edwards, and Michael Grossman. 1981. “An Exploration of

the Dynamic Relationship between Health and Cognitive Development in Adolescence.”

In Contributions to Economic Analysis: Health, Economics, and Health Economics,

ed. Jacques van der Gaag and Mark Perlman. Amsterdam: North-Holland Publishing,

305–328.

Spittel, Michael L., William T. Riley, and Robert M. Kaplan. 2015. “Educational Attainment

and Life Expectancy: A Perspective from the NIH Office of Behavioral and Social Science

Research.” Social Science and Medicine 127(February): 203–205.



The Correlation between



THREE



Health and Schooling

Michael Grossman



T



he relationship between health status and socioeconomic conditions is a

subject of increasing concern for both medicine and social science. Several

recent studies in the United States indicate that among socioeconomic variables,

years of formal schooling completed is probably the most important correlate

of good health (Stockwell 1963; Fuchs 1965; Hinkle et al. 1968; Kitagawa and

Hauser 1968; Auster, Leveson, and Sarachek 1969; Breslow and Klein 1971;

Grossman 1972b; Silver 1972). This finding emerges whether health levels are

measured by mortality rates, morbidity rates, or self-evaluation of health status,

and whether the units of observation are individuals or groups. The relationship

is usually statistically significant at levels of confidence of .05 or better in both

simple and partial correlations.

This chapter has two purposes. The first is to develop a methodological

framework that can be used to introduce and discuss alternative explanations of the correlation between health and schooling. The second is to test

these explanations empirically in order to select the most relevant ones and

to obtain quantitative estimates of different effects. The empirical work is

limited to one unique body of data and uses two measures of health that

are far from ideal. The methodological framework can, however, serve as a

point of departure for future research when longitudinal samples with more



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refined measures of current and past health and background characteristics

become available.

In a broad sense, the observed positive correlation between health and

schooling may be explained in one of three ways. The first argues that there is a

causal relationship that runs from increases in schooling to increases in health.

The second holds that the direction of causality runs from better health to more

schooling. The third argues that no causal relationship is implied by the correlation. Instead, differences in one or more “third variables,” such as physical

and mental ability and parental characteristics, affect both health and schooling

in the same direction.

It should be noted that these three explanations are not mutually exclusive

and can be used to rationalize any observed correlation between two variables.

But from both a public policy and a theoretical point of view, it is important

to distinguish among them and to obtain quantitative estimates of their relative

magnitudes. A stated goal of public policy in the United States is to improve the

level of health of the population or of certain groups in the population. Given

this goal and given the high correlation between health and schooling, it might

appear that one method of implementing it would be to increase government

outlays on schooling. In fact, Auster, Leveson, and Sarachek (1969) suggest that

the rate of return on increases in health via higher schooling outlays far exceeds

the rate of return on increases in health via higher medical care outlays. This

argument assumes that the correlation between health and schooling reflects

only the effect of schooling on health. If, however, the causality ran the other

way or if the third-variable hypothesis were relevant, then increased outlays on

schooling would not accomplish the goal of improved health.

From a theoretical point of view, recent new approaches to demand theory

assume that consumers produce all their basic objects of choice, called commodities, with inputs of market goods and services and their own time (Becker

1965; Lancaster 1966; Muth 1966; Michael 1972; Ghez and Becker 1975;

Michael and Becker 1973). Within the context of the household production

function model, there are compelling reasons for treating health and schooling

as jointly determined variables. It is reasonable to assume that healthier students

are more efficient producers of additions to the stock of knowledge, or human

capital, via formal schooling. If so, then they would tend to increase the quantity of investment in knowledge they demand as well as the number of years

they attend school. Similarly, the efficiency with which individuals transform

medical care and other inputs into better health might rise with schooling. This

would tend to create a positive correlation between schooling and the quantity of health demanded. Moreover, genetic and early childhood environmental