the dynamics of health & individual determinants econ g041, …uctpjea/healthecon2.pdf ·...

1

1EconG041, Jerome Adda

The Dynamics of Health & Individual DeterminantsEcon G041, Lecture 2


Overview

• Demand for Health over the life-cycle:– Grossman (1972) “On the Concept of Health Capital and the Demand

for Health”, JPE.• Health in Children:

– Case, Lubotsky and Paxson (2002) “Economic status and health in childhood: the origins of the gradient”, AER.

– Currie and Stabile (2003) “Socioeconomic Status and Health, Why is the Relationship Stronger for Older Children?" AER.

• Long lasting influences:– Almond (2006) “Is the 1918 Influenza Pandemic Over? Long-Term

Effects of In Utero Influenza Exposure in the Post-1940 U.S. Population.” JPE.

2


Overview

• Role of Education:– Lleras-Muney (2005) The Relationship Between Education and Adult

Mortality in the United States. REStud.– Kenkel (1991) “Health Behavior, Health Knowledge and Schooling”, JPE

• Role of Stress:– Chandola et al (2009) “Does work stress explain the social gradient in

CHD? A prospective cohort study”, mimeo UCL.• We leave the role of income and of health behavior to next lectures.


On the Concept of Health Capital and the Demand for Health

Grossman (1972)

• Transposes the Beckerian concept of human capital to health.• Health is a capital in which individuals invest in.

– Determines future productivity as does human capital.– However, health is also directly an argument of the utility function.

• Health evolves partly – Exogenously, depreciation with age or other fixed characteristics.– Endogenously, through investment using time inputs and expenditures.

3


The Setup

• Life-cycle model.• Health stock evolves (declines) over the life-cycle.

– Death if falls below a threshold H.– Investment in health prevents its decline. – Investment depends on medical inputs (M), and time (TH)

– Agents spend TS hours being sick.• Individuals accumulate wealth by working (TW hours), and spend

money on medical goods (M) and other goods (X):

),()1(' Ht TMIHH +−= δ

)(' MpXpwTARA MXW −−+=


Dynamic Problem

• Total hours add up to 24 hours

• Individuals value:– Service flow of health φtH– Composite good Z

• Dynamic programme of the agent:

WHS TTHT +=− )(24

)','(),(max),( )(,,HAVZHuHAV HHttTMXt

H>+= IIβφ

4


A Numerical Example

• Solve the programme of the agent.• Utility function:• Health depreciation varies between 4% and 50% with age.• Beta=0.95 annually.• Simulate the optimal path for behavioral variables and health.

)log(),( tttt ZHZHu +=φφ


Dynamics of Health

0

10

20

0102030405060

0

0.2

0.4

0.6

0.8

1

AgeInitial Wealth

Hea

lth

5


Health Behavior

0 10 20 30 40 50 6016

17

18

19

20

21

22

23

24

Age

Tim

e Sp

ent o

n H

ealth

Initial Wealth: LowInitial Wealth: High

0 10 20 30 40 50 600

0.5

1

1.5

2

2.5

Age

Exp

endi

ture

Tow

ards

Hea

lth

Initial Wealth: LowInitial Wealth: High


Summary

• Model emphasises the endogeneity of health.• Forms the basis of numerous reduce form estimation relating health

to income, price of medical care, education and age.• Good starting point to explore the determinants of health, but reality

is complex and the literature needs to deal with many endogeneityissues:– Role of initial health and its determinants.– Health depreciation does not only depends on age but other factors as

well.– Investment in health vs disinvestments.– Is education exogenous?

6


Economic status and health in childhood: the origins of the gradient

Case et al (2002)

• Investigates some determinants of childhood health: role of socio-economic status of parents.

• Investigates how the relationship changes as children age. • Convenient setup to investigate the effect of income on health,

reverse causality should be small.


Data

• Three different data sets, cross-sectional or longitudinal:– National Health Interview Survey (NHIS).– Panel Study of Income Dynamics (PSID).– Third National Health and Nutrition Examination Survey (NHANES).

• Restrict analysis to children between age 0 -17.• Health measures:

– Self-reported health– Bed days, hospitalization– Chronic conditions

7


Summary Statistics


Health and income for children and adults.

Source: Case et al (2002)

8


Health Status and Log Family Income

Note: Regressions control for age, sex, race, family size, presence of mother and/or father.


Decomposing the Effect of Income on Health

• The probability that child is in poor health can be expressed as:P(H) = P(H|C=0) P(C=0) + P(H|C=1) P(C=1)

• Taking the derivative with respect to log income:

∂P(H) /∂lny = ∂P(H|C=0) / ∂lny+ (∂P(H|C=1) / ∂lny - ∂P(H|C=0) / ∂lny) P(C=1)+ (P(H|C=1)-P(H|C=0)) ∂P(H|C=1) / ∂lny

Effect of income on health

“Prevalence Effect”“Severity Effect”

9


Decomposition: Empirical Implementation

• Probability of chronic condition:C= α0 + α1 lny + X δC + εC

• Determinants of Health: H = β0 + β1 (ln y - ln y) + β2 C + β3 (ln y - ln y) C + X δH + εH

�E ∂P(H) /∂lny = β1

+ β3C+ β2 α1


Gradients in Chronic Conditions. NHIS 1986-1995.

Source: Case et al (2002)

10


Chronic Condition, Income and Poor Health

• Income usually decreases the probability of chronic conditions.

• Income decreases poor health, over and above chronic conditions.

• Chronic conditions lead to poor health.

• Income attenuates the effect of chronic conditions.


Chronic Condition, Income and Poor Health

• Parental income is protective and buffer children from the effect of chronic diseases.

• Especially for serious chronic conditions such as asthma, diabetes and epilepsy.

• Income is more protective of chronic conditions for older children.

11


Decomposition of the Relationship BetweenHealth Measures and Income


Health Status and Income at Different Ages

Income before birth matters as much as income at a given age: importance of long-run income.

12


Health, Birth Health and Income


Health and Parents

13


Socioeconomic Status and Child Health: Why Is the Relationship Stronger for Older

Children? Currie and Stabile (2003)

• Explore the cause of the stronger relationship between socio-economic status and health in children.

• Two possibilities:– Low SES children are less able to respond to health shocks.– Low SES children get more adverse health shocks.

• Use a Canadian panel data to distinguish between both hypotheses.


Data and Methodology

• National Longitudinal Survey of Children and Youth.• Ages from 0 to 11.• Waves in 1994, 1996 and 1998.• 14,169 children were surveyed three times.• Information on health, socio-economic position of parents,

14


Proportion In Poor Health by Age

Age

Per

cent

in P

oor H

ealth

Low SES High SES


Number of New Chronic Conditions by Age and SES Status

Age

Num

ber o

f New

Con

ditio

ns

Low SES High SES

15


Steepening of the Health Gradient,US vs Canada

• Steepening of the health gradient occurs both in the US and Canada.• Despite universal health coverage in Canada.


Effect of Earlier Health Conditions on Poor Health Today

16


Summary

• Confirms the results of Case et al (2002).• Both low and high SES children recover from adverse health

shocks.• The main difference is that low SES children get more of them.• Why?


Is the 1918 Pandemic Over?Almond (2006)

• Test the long term effect of in utero exposure of influenza.• More broadly, are there long-run consequences of poor maternal

health?• Difficult question to answer directly, as poor maternal health is

correlated with subsequent family environment. Many unobserved factors could lead to poorer health later in life.

• Use the “Spanish Flu” as an exogenous event that affect individuals in utero.

17


US influenza Deaths

Year

By Year By Month in 1918-19


1980 Male Disability Rates

18


Years of Schooling, Men and Women

Years of Schooling Percentage Graduating

Men

Women


High School Graduation

Raw Data

Regression adjusted

19


Male Disability Rate: Physical Disability Limits Work


Departure of 1919 Male Birth Cohort Outcomes from 1912-1922 Trend

i

ci YOBYOBYOBIy εββββ +++=+= 2

3210 )1919(

20


1912-22 Census Outcome Among Women


Summary

• Long-term effects of poor health in early age.• Stronger than a possible healthy-survivor bias.• Education effect between 0.11-0.15 years.• Wage effect is around $700-900 decrease.• If return to education estimates apply to that cohort, low educational

achievements only explain half of the wage effect.– Effect of disability as well.

• Should be large returns to improving maternal health.

21


The Relationship Between Education and Adult Mortality in the United States

Lleras-Muney (2005)

• Large and positive correlation between education and health.• Does education cause health (mortality)?• Possible channels:

– Better educated individuals are better decision makers and are better informed.

– Poor health results in low education.– Unobserved heterogeneity causing both poor health and low education

(genetics, parental background, low discount factor…)• This study uses changes in compulsory education laws across

states in the US between 1915 and 1939.


Data

• Use of the census of 1960, 1970 and 1980. (1% random sample)– Include all white persons – born in 48 states, – that were 14 between 1914 and 1939.– With no missing information on completed education.

• Construct synthetic cohorts as no panel data.• Groups constructed by gender, state of birth and cohort.

22


Compulsory Education Laws


Event Analysis: Education and Mortality

Year of Introduction of Compulsory Law

Yea

rs o

f Edu

catio

n

Year of Introduction of Compulsory Law

Mor

talit

y R

ate

23


Econometric Model

• With panel data, econometric model would be:

• With cross-sectional data, one can take the average over a group:

• First stage:

Dummy if deceased Between date t and t+1

EducationFixed IndividualCharacteristics

State Dummy

Cohort Dummy

State * cohort characteristics


First Stage: Compulsory Laws and Education

Yea

rs o

f Edu

catio

n

24


First Stage: Compulsory Laws and Education


Mortality and Education: OLS Results

25


Mortality and Education: IV Results


Reduced Form: Mortality and Compulsory Laws

26


Summary

• Evidence of causal effects of education on mortality:• An additional year of education decreases mortality by 3 to 6

percentage points.• IV coefficients are higher than OLS:

– Measurement error on education.– LATE interpretation: those who are induced to increase education have

an exceptionally high return.• Why is there an effect? Not so clear:

– Better understanding of health and health behavior.– Access to better and less risky jobs.– Different peers…


Education, Health and Health Knowledge

• Does schooling help choose healthier life-style?• Are more educated individuals better informed about risks?• Kenkel (1991) “health Behavior, Health Knowledge and Schooling”,

JPE• Use data from the 1985 Health Interview Survey, which describes

– Health behavior (smoking, drinking, exercise).– Health knowledge:

• Whether smoking causes emphysema, bladder cancer, cancer of larynx, oesophagus, chronic bronchitis, lung cancer and heart disease.

• Whether drinking causes throat cancer, cirrhosis and mouth cancer.

27


Tobit Model: Dependant Variable: Smoking

T-ratio in parenthesis.

Elasticity of participationElasticity of intensity

• Two stage least square in column 3. Instruments: occupation, industry, years of schooling after 1964, receipt of physician advice on life-style related topics


Summary

• Health knowledge matters for health behavior.• Even more so for those with higher education.• Schooling still matters even when knowledge is taken into account.• Evidence that knowledge is endogenous.

28


Does work stress explain the social gradient in CHD? A prospective cohort study

Chandola et al (2009)

• Use data from the Whitehall Study in England.• 10308 civil servants were followed for up to 15 years.• Looks at the incidence of Coronary Heart Disease (CHD).• Investigates the causal effect of work stress on CHD:

– Correlation between stress and CHD, not necessarily causal.– Stress may be confounded with unobserved characteristics.– Stress may be measured with error.

• Use civil department characteristics to instrument individual stress:– Staff turnover: large differences following organisational changes in the

civil service. – Work pace, rated by “experts” on a scale between 1 to 12.


Distribution of CHD Risk Factors by Employment Grade

29


OLS Results

•Model 1: No covariates.

•Model 2: adjustment for age, sex, employment grade, ethnicity, family history, BMI, total cholesterol, hypertension.


First Stage: Stress and Staff Turnover & Pace of Work

30


IV Results


Summary

• Work conditions such as stress have a causal effect on health.– Independent effect of observed (and unobserved) characteristics.– Stress is more prevalent in groups of low level education: one of the

pathways from education to health.• Literature has also focused on effect of work injuries.

the dynamics of health & individual determinants econ g041, …uctpjea/healthecon2.pdf ·...

Documents