THE RELATIONSHIP BETWEEN

HEALTH AND SCHOOLING: WHAT’S

NEW?

Michael Grossman

City University of New York Graduate Center and

National Bureau of Economic Research

Fourth Congress of the Colombian Health Economics

Association

Cali, Colombia

February 18-20, 2015

1

INTRODUCTION

Two most fundamental relationships in health economics

• Relationship between health insurance and medical care utilization

• Relationship between health and schooling

• Both generated by causality in both directions and by omitted “third variables”

• My keynote address: research on the second of these relationships that has

appeared in the past five years (2010-2014)

• Focus on empirical studies but will call your attention to a few theoretical

developments

• Will perhaps raise more questions than answers

2

WHY IS THE RELATIONSHIP BETWEEN

HEALTH AND SCHOOLING SO IMPORTANT?

• Demand for health model emphasizes that medical

care is only one of many determinants of health,

natural to explore others

• Demand for health model views health as a form of

human capital, natural to allow for and explore

complementarities between health capital and other

forms of human capital, the most important of which

is knowledge capital (schooling)

3

EMPIRICAL MOTIVATION

• “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.” Gina Kolata, “A Surprising Secret to Long Life: Stay in School,” New York Times, January 3, 2007

• “With the exception of black males, all recent gains in life expectancy at age twenty-five have occurred among better educated groups, raising educational differentials in life expectancy by 30 percent.” Meara, Richards, and Cutler, Health Affairs, March/April 2008

• Link does not necessarily imply causality from more schooling to better health

• Health may cause schooling or omitted “third variables” may cause health and schooling to vary in same direction

4

Table 1

Infant Mortality Rate, Age-Adjusted Mortality Rate, and Educational Attainment, United States, Selected Years, 1910-2000

Year

Infant Mortality Rate

(Deaths per 1,000 live births)

Age-Adjusted Mortality Rate (Deaths per

100,000 population based on year 2000 standard

population)

College Graduates (Percentage of persons aged 25 and older who

completed four years of college or more)

1910 131.8 2,317.2 2.7 1920 92.2 2,147.1 3.3 1930 69.0 1,943.8 3.9 1940 54.9 1,785.0 4.6 1950 33.0 1,446.0 6.2 1960 27.0 1,339.2 7.7 1970 21.4 1,222.6 11.0 1980 12.9 1,039.1 17.0 1990 9.7 938.7 20.3 2000 7.4 869.0 25.6

5

6

Table 2

Infant Mortality Rate and Educational Attainment,

United States versus Colombia

Year

Infant Mortality Rate

U.S.

Infant Mortality

Rate

Colombia

College

Graduates

U.S

College

Graduates

Colombia

1960 27.0 88.9 7.7 1.0

1970 21.4 69.7 11.0 1.8

1980 12.9 44.6 17.0 3.6

1990 9.7 29.0 20.3 7.3

2000 7.4 21.2 25.6 9.4

Source for Colombia data: http://databank.worldbank.org/data/databases.aspx. For Colombia,

college graduates are the percentage of persons 25 years of age and older with tertiary schooling

completed—includes universities as well as institutions that teach specific capacities of higher

learning such as colleges, technical training institutes, community colleges, nursing schools,

research laboratories, centers of excellence, and distance learning centers.

Table 3

Infant and Age-Adjusted Mortality Regressions, U.S.a

Infant Mortality Rate

Age-Adjusted Mortality Rate

Percentage with four years of college or more

-1.617 (-5.06)

-28.950 (-3.96)

R2 0.996 0.990

F-statistic 2,814.71 1,078.82

aEach regression contains an intercept and a cubic time trend. t-statistics are given in parentheses.

7

CAUSALITY FROM HEALTH TO

SCHOOLING

• Students in poor health miss more days of school due to

illness and learn less while in school

• Result: Negative effect on school achievement and years of

formal schooling completed

• Long-lasting effect if past health an input into current health

• Reduction in mortality increases number of periods over which

returns from investments in knowledge can be collected

8

CAUSALITY FROM SCHOOLING TO

HEALTH

• Productive efficiency: more educated obtain more

health output from given amounts of medical care

and other inputs

• Allocative efficiency: more educated pick a different

input mix to produce a certain commodity than less

educated; mix gives them more output of that

commodity than the mix selected by the less

educated

9

OMITTED THIRD VARIABLES

• Fuchs (1982) time preference hypothesis

• Persons who are more future oriented attend school

for longer periods of time and are more likely to

make investments in their own health and in their

children’s health

10

THEORETICAL DEVELOPMENTS

11

iHEA SESSION: REASSESSING

GROSSMAN

• Audrey Laporte: “Should the Grossman Model Retain Its Iconic

Status in Health Economics?”

• Yes, but in context of pure consumption model with savings

ruled out

12

T. GALAMA AND H. VAN KIPPERSLUIS, “A

THEORY OF EDUCATION AND HEALTH”

• “Currently, we still lack comprehensive theoretical models in which the

stocks of health and knowledge are determined simultaneously…. The rich

empirical literature treating interactions between schooling and health

underscores the potential payoff to this undertaking.” (Old Discredited

Economist, 2000, quoted by Titus and Hans, page 3 of their paper)

• How can we go beyond the following simple structural model based on

their paper?

H = H(E, Prices of Health Inputs, Assets, Time Preference)

E = E(H, Prices of Schooling Inputs, Assets, Time Preference)

• GV do comparative dynamics by assuming stock of HC is sum of

knowledge and health stocks and no sick time

13

JAMES HECKMAN AND COLLEAGUES, SKILL

PRODUCTION (Cuhna, Heckman, and

Schennach 2010)

• Conceptualization of technology of cognitive and noncognitive skill

formation in childhood and adolescence; key determinants of

completed schooling and health as an adult

• Introduce concepts of self-productivity (skills produced early in life

increase skills at later stages) and dynamic complementarity (early

investments raise marginal productivity of later investments), which

interact to generate multiplier effects

• Investments in adolescence have much larger payoffs when earlier

investments have been made

• Stress importance of noncognitive skills: “big five personality traits”

(conscientiousness , openness , extraversion, agreeableness, and

neuroticism), and related personality measures

14

E. FRIEDMAN AND R. MARE: NEW CAUSAL

RELATIONSHIP FROM SCHOOLING TO

HEALTH

• Typical assumption: Parents invest in their children’s

schooling because child quality enters their utility function

• Friedman and Mare (Demography, 2014): time of adult children

an input in the production of their elderly parents’ health,

efficiency of this time a positive function of schooling

• Evidence that adult offspring’s education positively associated

with elderly parents’ survival

15

EMPIRICAL STUDIES: INTRODUCTION

Three Types • Direct Inclusion of Third Variables

• Twin Studies

• Instrumental Variables

• Note: Include studies by Heckman and colleagues in first type, but

they are much more complicated, latent cognitive and personality

skills, measured and unmeasured components of these skills, factor

analysis to control for measured and unmeasured cognitive and non-

cognitive ability, simultaneous estimation of outcome equation and

measurement equations

16

INVENTORY OF EMPIRICAL STUDIES

Inclusion of Third Variables (8)

Conti and Hansman (2013); Conti and Heckman (2010); Conti,

Heckman, and Urzua (2010); De Walque (2010); Kaestner and

Callison (2011); Savelyev (2014); Savelyev and Tan (2014); Van

Der Pol (2011)

Twin Studies (9)

Amin and Behrman (2014); Amin, Behrman, and Spector (2013);

Amin, Lundborg, and Rooth (2011); Behrman et al. (2011);

Lundborg, Lyttkens, and Nystedt (2012); Lundborg (2013);

Lundborg, Nordin, and Rooth (2012); Madsen et al. (2010);

Webbink, Martin, and Visscher (2010)

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INVENTORY-CONTINUED

Instrumental Variables (24)

Agüero and Bharadwaj (2013); Andalón, Williams, and Grossman (2014); Atella and Kopinska (2014); Braakmann (2011); Buckles et al. (2013); Caneiro, Meghir, and Parey (2013); Chou, Liu, Grossman, and Joyce (2010); Clark and Royer (2013); Cowan (2011); Dinçer, Kaushal, and Grossman (2014); Etilé and Jones (2011); Holmlund, Lindahl, and Plug (2011); Jensen and Lleras-Muney (2012); Jürges, Reinhold, and Salm (2011); Kempter, Jürges, and Reinhold (2011); Li and Powdthavee (2014) Lundborg, Nilsson, and Rooth (2014); McCrary and Royer (2011); Meghir, Palme, and Simeonova (2012); Mocan and Cannonier (2012); Powdthavee (2010); Tsai, Liu, Chou, and Grossman (2011); Van den Berg, Janys, and Christensen (2012); Van Kippersluis, O’Donnell, and Van Doorslaer (2011)

18

SELECTED EMPIRICAL RESULTS:

THIRD VARIABLES STUDIES

• All studies find positive and significant effects of completed schooling

on at least some key measures of adult health and beneficial health

behaviors

• Van Der Pol (2011): Controls for time preference in Dutch DNB

Household Survey; outcomes include self-rated health, cigarette

smoking, long-term illness, BMI, and obesity

• Conti and Heckman (2010): Control for cognitive and noncognitive

ability at age 10 and health at that age in examining outcomes at age

30 in 1970 British Cohort Study; outcomes include self-rated health,

daily smoking, and obesity; education effects bigger for those with

more cognitive ability and for those with less noncognitive ability

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THIRD VARIABLES STUDIES-

CONTINUED

• Savelyev (2014): Terman Life Cycle Study of Children with High Ability,

IQ > 140, 11 years old in 1921, followed through 1991, all high school

grads

• For men, graduation from college increases life expectancy at age 30

by approximately 9 years compared to non-grads, with IQ, big five

personality traits, and health status (all measured at age 12) held

constant; no effect for women

• Heckman student, Heckman type estimation

20

EMPIRICAL RESULTS: TWIN STUDIES

Point-Counterpoint

• Behrman et al. (2011): No effects on adult mortality or hospitalizations

in Danish twin registry, 2,500 identical (monozygotic, MZ) twin pairs

• Amin, Behrman, and Spector (2013): No effects on obesity, smoking,

and physical health in UK twins, 741 female MZ twin pairs

• Madsen et al. (2010): No effects overall in same data as Behrman but

negative effects for males born before 1935 and negative effects for

large schooling differences within pairs

• Webbink, Martin, and Visscher (2010): Negative effect on male obesity

(350 MZ pairs), no effect on female obesity, 700 MZ pairs in Australia

21

TWIN STUDIES-CONTINUED

• Lundborg (2013): Effects in expected directions of high school completion on

self-rated health, chronic conditions , and exercise behavior in Midlife in the US

survey; no effect on smoking and BMI; 347 MZ pairs

• Lundborg, Lyttkens, and Nystedt (2012): Negative effects on mortality in

Swedish twin registry, 9,000 MZ pairs; individuals with at least 13 years of

schooling can expect to live an additional 24 years at age 60 compared to 21

years for those with less than 10 years of schooling; 84 % of low-educated

individuals lived to age 70, compared to 90% of high-educated individuals;

results control for birthweight and height

• Lundborg, Nordin, and Rooth (2012): Negative effect of MZ twin mother’s

schooling on son’s health in Swedish twin registry, pertains to sons who have

enlisted in the military, health measured by physical exam administered on

enlistment; no effect of MZ twin father’s schooling; approximately 300 twin

pairs

22

INSTRUMENTAL VARIABLES STUDIES:

INTRODUCTION

• All but one study use compulsory school reform or school entry cutoff

date as an instrument, sometimes combined with new school

openings at a differential rate among areas

• Group by outcome

• Adult mortality

• Adult health, health behaviors, health knowledge

• Infant health, adolescent health, and mechanisms (effects of parents’

schooling)

23

IV STUDIES: ADULT MORTALITY

• Clark and Royer (2013): No effect in Britain; “Our results…suggest that

economic models that assume a strong causal effect of education on health

should be carefully reconsidered.”

• Meghir, Palme, and Simeonova (2012): Similar lack of effect in Sweden

• Van Kippersluis, O’Donnell, and Van Doorslaer (2011): negative effect for men

but not women in the Netherlands; for men surviving to age 81, S by 1 year

probability of dying before age 89 by 3 percentage points relative to baseline

of 50%

• Buckles et al. (2013): negative effect in U.S. for men; college completion

reduces cumulative mortality from 1980-2007 by almost 30% relative to the

mean, men 38-49 in 1980; instrument is risk of induction during Vietnam War

• Conclusion by Clark and Royer somewhat premature

24

ADULT HEALTH AND HEALTH

BEHAVIORS

• Braakmann (2011): no effects on variety of self reported health measures, smoking, heavy drinking, and diet in Britain; similar findings reported by Clark and Royer (2013)

• Powdthavee (2011): negative effect on hypertension based on physical exam in Britain

• Etilé and Jones (2011): negative effect on smoking and quitting in France

• Buckles et al. (2013): negative effects on smoking, heavy drinking, and obesity, positive effect on exercise in US

• Atella and Kopinska (2014): negative effects on BMI and caloric intake, positive effect on calorie expenditure in Italy

25

HEALTH KNOWLEDGE AND RELATED

BEHAVIORS

• Agüero and Bharadwaj (2013): Positive effect on having more

knowledge about HIV and negative effect on number of sexual

partners for women in Zimbabwe

• Andalón, Williams, and Grossman (2014): Positive effects on

conceptive knowledge and use of contraception at sexual debut for

women in Mexico

• Dinçer, Kaushal, and Grossman (2014): Positive effects on knowledge

of the ovulation cycle and use of modern family planning methods for

women in Turkey

• Mocan and Cannonier (2012): Positive effects on use of modern

contraception and to be tested for AIDS for women in Sierra Leone

26

INFANT HEALTH, ADOLESCENT

HEALTH, AND MECHANISMS

• McCrary and Royer (2011): No effects of mother’s schooling on low

birthweight, infant mortality, maternal smoking and alcohol use during

pregnancy, and prenatal care use for pregnant women in California and Texas

• Chou, Liu, Grossman, and Joyce (2010): Negative effects of mother’s

schooling on low birthweight, neonatal mortality, postneonatal mortality, and

infant mortality in Taiwan; increase in schooling associated with school reform

saved almost 1 infant life in 1,000 live births

• Lundborg, Nilsson, and Rooth (2014): Positive effects of mother’s schooling

on son’s physical health and height in Swedish military enlistment register

• Dinçer, Kaushal, and Grossman (2014): Positive effects on age at first marriage

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

negative effect on infant mortality in Turkey

27

ISSUES/QUESTIONS

• School entry age used by McCrary and Royer and others as instrument may not be exogenous; parents can hold their children back or petition to have them start early; Shigeoka (2014) finds almost 2,000 births per year shifted from a week before to a week after the school entry cutoff date in Japan

• Why do results differ? ATE versus LATE

• Twin studies have small N. Differences in schooling between twins may be small. Why do identical twins obtain different amounts of schooling?

• Some IV studies find that OLS is consistent. Some do not test this. Schooling is endogenous, but is it possible that recursive model with uncorrelated errors is correct? Third variables influence schooling but have no impact on health with schooling held constant.

28

ISSUES/QUESTIONS CONTINUED

• Take seriously H S, life expectancynumber of periods

over which returns collectedS

• Estimate structural schooling equation and structural health

equation at same time

• How can theoretical advances (Galama and van Kippersluis;

Heckman and colleagues) help?

29