EDUCATION AND HEALTH: WHAT IS THE ROLE OF

LIFESTYLES?

Giorgio Brunello (University of Padova) Margherita Fort (University of Bologna) Nicole Schneeweis (University of Linz) Rudolf Winter Ebmer (University of Linz)

Regensburg May 2011

1

MOTIVATION 2

RESEARCH QUESTIONS

Does education cause health outcomes?

Are lifestyles an important channel through which education improves health?

Problem: confounding factors affecting both education and health

2 strategies: IV strategy to identify total causal effect Aggregation and differencing to decompose total

effect into effect due to lifestyles3

Education

Health

Lifestyles

4

CHANNELS FROM EDUCATION TO HEALTH (LOCHNER, 2011)

Stress reduction Better decision making Health Insurance Better information gathering Better jobs (and higher income) Healthier peers and neighbourhoods Lifestyles (eating, drinking, smoking,

exercising...)5

LITERATURE6

PREVIOUS RESEARCH ON CAUSAL EFFECTS

Recent literature uses changes in mandatory schooling laws to identify causal effects Simple OLS models likely to be biased due to

confounders

Mixed results so far (see review by Lochner, 2011)

Important differences by gender7

PREVIOUS RESEARCH ON THE ROLE OF LIFESTYLES

Cutler – Lleras Muney, 2006: in the US the overall effect of education on mortality is reduced by 30% when controlling for lifestyles However, they ignore endogeneity issues and only

look at the effect of current lifestyles Contoyannis and Jones, 2004, estimate a

structural health equation and lifestyle equations by FIML, but treat education as exogenous. Using Canadian data, they find that treating

lifestyles as endogenous could change radically estimated mediating effects

8

THEORY9

STANDARD THEORETICAL APPROACH (ROSENZWEIG – SCHULTZ)

Individuals care about health H (health in the utility function)

They choose optimal lifestyles L by maximizing an inter-temporal utility function subject to a budget constraint

Education affects optimal lifestyles because it affects the valuation of health the discount factor preferences the health production function 10

Dynamic Health Production

Instantaneous utility

Health production function

1,1, tiititiit HYELH Budget constraint

ittitiit LpCEY )(

Inter-temporal utility

rtrT

r

0

itititit HEhLGCU )()()(

11

OPTIMAL LIFESTYLES

)|( itiit XELL

Using this in the health production function and substituting sequentially lagged health yields

)|( itiit XEHH

Where X includes prices and other exogenous factors. We call the latter

“reduced form” health function

Education can influence life style because it increases evaluation of health and increases the discount factor.

12

MEDIATING EFFECT OF LIFESTYLES

Mediating role of lifestyles: effect of education on health going through lifestyles

Current health status most likely depends not only on lifestyles in the previous year, but also on the entire history of lifestyles

Ex: smoking last year matters, but also smoking in the previous years (albeit with lower weights?) 13

SHORT AND LONG RUN MEDIATING EFFECTS

Short run mediating effect: the effect of education on health going through lifestyles lagged once

Long run mediating effect: the effect of education on health going through lifestyles lagged from 1 to T

14

DATA15

DATA: SHARE AND ELSA

Survey of Health, Ageing and Retirement Waves 1 and 2 We also use SHARELIFE

English Longitudinal Survey of Ageing Waves 2 and 3

Sample Females and males (separately), aged 50+ In IV estimates birth cohorts max 10 years

around pivotal age16

INTERESTING FEATURES OF THE DATA By using data on 50+ males and females, we

focus on the effects of education acquired close to or more than 30 years earlier

Not clear whether effects of education on health increase with age

We have information on self reported health self reported limited activity due to poor health long term illness 14 health conditions (heart-related, respiratory,

bones-related, cancer, diabetes, ...)17

MEANS OF HEALTH MEASURES (PERCENTAGE WITH CONDITION)

18

Country

self reported

poor health

Has chronic

diseasesLong term

illnessHeart

diseasesHigh blood pressure Diabetes

Bone related

diseasesRespiratory

diseases Cancer Years of education Age Observations

Austria 23.27 64.06 38.49 22.76 28.13 8.05 16.19 4.84 3.70 11.37 59.01 782Czech Republic 41.76 77.65 51.71 29.64 41.68 13.58 24.53 9.37 4.04 12.02 63.33 2452Denmark 20.81 69.98 46.78 23.01 26.54 6.10 25.48 10.16 5.43 11.80 59.29 1899England 37.30 82.88 62.00 41.24 38.64 3.62 44.88 15.56 4.54 10.70 72.27 4779France 33.06 70.94 45.74 31.30 26.13 8.32 29.17 7.31 5.35 11.31 63.41 2223Italy 33.73 71.12 35.81 26.00 33.89 8.17 31.20 7.19 3.29 8.82 59.83 2092Netherlands 33.80 73.09 44.97 28.47 31.63 11.19 19.88 10.50 5.92 10.60 70.10 1840

Total 33.51 72.97 48.12 32.27 32.96 8.67 22.27 10.43 4.43 11.19 65.54 7415Total males 35.04 77.36 51.28 31.02 34.91 6.98 39.08 10.86 4.80 10.64 65.71 8652Total females 33.87 75.34 49.87 31.59 34.01 7.76 31.33 10.66 4.63 10.89 65.63 16067

IV ESTIMATES19

CAUSAL EFFECT OF EDUCATION ON HEALTH

Use multi-country data (see Brunello, Fort and Weber, 2009; Brunello, Fabbri and Fort, 2010)

Identification

Compulsory schooling reforms in Europe as natural experiment

Reforms in the 1930s-60s in 7 European countries Country fixed effects Cohort fixed effects Country specific trends in cohorts

20

Note: clustered standard errors in parentheses

FIRST STAGE ESTIMATES BY GENDER

21

VARIABLES females males

years of compulsory education 0.268 0.325(0.057)*** (0.079)***

Observations 8,652 7,415F test 21.82 17.10

     

Note: “rejects” means that the null hypothesis of poolable education coefficients is rejected

POOLING TESTS, FIRST STAGE AND REDUCED FORM

22

       

Health variable

Pooling test - firstage males

Pooling test - reduced form

males

Pooling test - firstage females

Pooling test - reduced form

females

self reported bad healthdoes not

reject rejectsdoes not

rejectdoes not

reject

has chronic diseasesdoes not

reject rejectsdoes not

reject rejects

long term healthdoes not

reject rejectsdoes not

rejectdoes not

rejectlimited activities (b/c of poor health)

does not reject

does not reject

does not reject rejects

heart problemsdoes not

rejectdoes not

rejectdoes not

rejectdoes not

reject

high blood pressuredoes not

rejectdoes not

rejectdoes not

rejectdoes not

reject

diabetesdoes not

reject rejectsdoes not

rejectdoes not

reject

bone related problemsdoes not

rejectdoes not

rejectdoes not

rejectdoes not

reject

respiratory problemsdoes not

reject rejectsdoes not

reject rejects

cancerdoes not

reject rejectsdoes not

reject rejects

indicator of diseases linear does not

rejectdoes not

rejectdoes not

rejectdoes not

reject

EFFECTS OF EDUCATION ON HEALTH OUTCOMES. FEMALES (SEMI-ELASTICITIES)

23

Health variable

Probit estimate Reduced form 2SLS IV Observations

self reported bad health -0,079 -0,05 -0,189 -0,197 8,602 *** ** ** **

has chronic diseases -0,008 -0,038 -0,142 -0,129 8,652*** *** ** **

long term illness -0,015 -0,036 -0,135 -0,137 8,651 *** ** **

limited activities (b/c of poor health) -0,088 -0,01 -0,04 -0,193 8,652

***heart problems -0,036 0,0005 0,002 -0,008 8,652

***high blood pressure -0,041 -0,065 -0,245 -0,241 8,652

*** ** ** ***diabetes -0,071 -0,206 -0,776 -0,462 8,652

*** *** ** ***bone related problems -0,018 -0,029 -0,111 -0,128 8,652

***respiratory problems -0,042 0,05 0,189 0,235 8,652

*** (0.060)cancer 0,036 -0,134 -0,502 -0,347 8,652

** *indicator of diseases linear -0,017 -0,022 -0,084 -0,084 8,652

EFFECTS OF EDUCATION ON HEALTH OUTCOMES. MALES (SEMI-ELASTICITIES)

24

Health variable

Probit estimate Reduced form estimate 2SLS IV Probit Observa

tions

self reported bad health -0,063 -0,055 -0,171 -0,183 7358*** ** * **

has chronic diseases -0,007 0,009 0,029 0,023 7415*** **

long term illness -0,017 0,037 0,116 0,111 7415*** ** ** **

limited activities (b/c poor health) -0,07 0,009 0,029 0,062 7415

***heart problems -0,012 0,093 0,286 0,22 7415

*** *** ** ***high blood pressure -0,017 0,044 0,137 0,135 7415

*** *diabetes -0,038 -0,057 -0,175 -0,238 7415

***bone related problems -0,043 -0,05 -0,155 -0,181 7415

***respiratory problems -0,052 0,098 0,301 0,275 7415

*** ** * ***cancer 0,006 -0,13 -0,399 -0,326 7415

** **indicator of diseases (linear) -0,012 0,019 0,06 0,06 7415  *** ** * *  

IV RESULTS (PERCENTAGES EVALUATED AT SAMPLE MEANS) Females: one additional year of education

reduces Self reported bad health (-19.7%) Presence of chronic diseases (-12.9%) High blood pressure (-24.1%) Diabetes (-46.2%)

Males: one additional year of education reduces Self reported bad health (-18.3%) INCREASES

Long term illness (11.1%) Hearth problems (22%) Respiratory problems (27.5%) Objective measure of conditions (6%) 25

IV RESULTS

We confirm important gender differences

Positive effect of education on health conditions is puzzling. Possible explanations include

Education moves males away from less sedentary occupations

Education moves males to more stressful occupations (or males are less able to cope with stress...) 26

HEALTH CONDITIONS AND SCREENING Conditions are reported by individual but

must have been detected by a doctor „Did your doctor tell you …?“

Marginal effect of education:

If more education induces e.g. males to go to the doctor more often, more diseases would be detected

Preliminary results: no effects of screening!!

27

( ) ( ) ( | )S...Screening D ... DiseaseP D P S P D S

( ) ( ) ( | )( | ) ( )P D P S P D SP D S P SE E E

POTENTIAL BIASES

Older cohorts (pre-treatment) are less healthy: we capture this with cohort dummies

Members of older cohorts who are still alive – positive selection and downward bias – we try to control for this by adding life expectancy at birth Using sampling weights that are inversely proportional to

the difference between age and life expectancy

Placebo treatment as in Black, Devereux and Salvanes (2008) ---- Placebo reforms should have no effect

28

REDUCED FORM ESTIMATES: WITH YEARS OF COMPULSORY EDUCATION 5 YEARS AHEAD

Health variable

Marginal effect of

YCOMP Males

Marginal effect of YCOMP -

placebo test – Males

Marginal effect of YCOMP 5 years

ahead - placebo test -Males

Marginal effect of YCOMP

- Females

Marginal effect of YCOMP

- placebo test - Females

Marginal effect of YCOMP 5 years

ahead - placebo test -

Females

self reported bad health -0,055 -0.038 0.052 -0,05 -0.039 0.043

** **

has chronic diseases 0,009 0.011 0.004 -0,038 -0.033 0.003*** **

long term illness 0,037 0.043 0.016 -0,036 -0.030 0.037** * **

limited activities due to poor health 0,009 0.023 0.040 -0,01 -0.040 0.008

heart problems 0,093 0.108 0.044 0,0005 -0.014 -0.044*** ***

high blood pressure 0,044 0.058 0.039 -0,065 -0.061 0.050* ** *

diabetes -0,057 -0.068 -0.033 -0,206 -0.252 0.026*** ***

bone related problems -0,05 -0.013 0.111 -0,029 -0.030 0.011**

respiratory problems 0,098 0.089 -0.029 0,05 0.115 0.112** *

cancer -0,13 -0.138 -0.022 -0,134 -0.178 -0.159

indicator of diseases linear 0,019 0,026 0.0207 -0,022 -0,017 0.0176  ** *** * *    

THE MEDIATING EFFECTS OF LIFESTYLES

30

THE CARD ROTHSTEIN APPROACH We do not have credible instruments for lifestyles

We combine gender differencing (fixed effects) to remove common un-observables with selection on observables, using SHARELIFE info.

SHARELIFE variables control for early health conditions and parental background.

Fixed effects remove nature and nurture effects that are common between genders.

31

icgbticgbticgticgbicgbt HELH 132)1(1

icgbtcbtcgbticgbt eu

0],,,|[ tcgbeE icgbt

cbtFcbt

Fcbtcbt vZZu 21

cbtFcbt

Fcbt

Fc

Fc

Ftcb

Ftcbcbt uHHEELLH 13130202)1(1)1(1

where i=individual; c: cohort; g: gender; t=time. We assume

We take gender differences to remove cbt

We model the residual error as function of Z (parental background and early health from SHARELIFE)

32

ESTIMATES OF “REDUCED FORM” AND DYNAMIC HEALTH EQUATIONS

We add to the sample Germany and Sweden (in future work we plan to extend this approach to other countries included in SHARE)

We estimate these equations both on micro data using selection on observables only

and on cell data using gender differences plus selection

on observables (Card-Rothstein)

33

ESTIMATED EFFECTS OF EDUCATION ON SELF REPORTED BAD HEALTH, WITH AND WITHOUT HEALTH LIFESTYLES. LINEAR PROBABILITY MODELS MICRO DATA

34

 

Reduced form health equation -

Females

Dynamic health equation - Females

Reduced form health equation

- Males

Dynamic health

equation - Males

       years of schooling -0.017 -0.005 -0.014 -0.006

*** *** *** ***lagged dependent variable 0.503 0.491

*** ***drunk alcohol every day in year t-1 -0.029 -0.032

** **

was smoking in year t-1 0.064 0.047*** ***

did vigorous activity in year t-1 -0.016 -0.023

*** ***BMI in year t-1 0.007 0.005

*** ***father drunk or had mental troubles (age 10) 0.033 0.021 0.021 0.004

** *

presence of parents in the house at 10 0.017 0.011 -0.001 -0.007

ESTIMATED EFFECTS OF EDUCATION ON SELF REPORTED BAD HEALTH, WITH AND WITHOUT HEALTH LIFESTYLES. GENDER DIFFERENCES. CELL DATA. WEIGHTED REGRESSIONS.

35

 

Reduced form health equation -

Females

Dynamic health equation with

income - Females

Reduced form health equation -

Males

Dynamic health equation with

income - Males       

years of schooling -0,023 -0,018 0.003 0.013*** *

lagged dependent variable 0,170 0.299* ***

drunk alcohol every day in year t-1 0,082 0.079

was smoking in year t-1 0,028 -0.103

did vigorous activity in year t-1 -0,005 -0.035

BMI in year t-1 0,001 -0.001

real income -0,003 -0.004**

Observations 232 230 232 230

ESTIMATED EFFECTS OF EDUCATION ON LIMITED ACTIVITY DUE TO POOR HEALTH, WITH AND WITHOUT HEALTH LIFESTYLES. LINEAR PROBABILITY MODELS MICRO DATA

36

 

Reduced form health equation

-Females

Dynamic health equation with income

- Females

Reduced form health

equation - Males

Dynamic health

equation with income - Males

years of schooling -0.0065 -0.0026 -0.0034 -0.0008*** ** ***

lagged dependent variable 0.4320 0.4490*** ***

drunk alcohol every day in year t-1 -0.0112 0.0038

was smoking in year t-1 0.0186 -0.0029*

did vigorous activity in year t-1 -0.0141 -0.0116*** ***

BMI in year t-1 0.0052 0.0035*** ***

father drunk or had mental troubles (age 10) 0.0259 0.0125 -0.0005 -0.0054

**

presence of parents in the house at 10 0.0188 0.0150 0.0106 0.0018

ESTIMATED EFFECTS OF EDUCATION ON LIMITED ACTIVITY BECAUSE OF POOR HEALTH, WITH AND WITHOUT HEALTH LIFESTYLES. GENDER DIFFERENCES. CELL DATA. WEIGHTED REGRESSIONS.

37

 

Reduced form health

equation -Females

Dynamic health

equation with income - Females

Reduced form health

equation - Males

Dynamic health

equation with income -

Males       

years of schooling -0,020 -0,024 -0.018 -0.017*** *** *** **

lagged dependent variable 0,146 0.156*

drunk alcohol every day in year t-1 0,018 0.029

was smoking in year t-1 -0,022 -0.076

did vigorous activity in year t-1 -0,001 -0.034*

BMI in year t-1 0,008 0.002*

real income 0,000 -0.001

Observations 232 230 232 230

ESTIMATION OF MEDIATING EFFECT We estimate both the dynamic health

equation and the „reduced form“ health equation

Total effect of education on health

Use also reduced form H=f(E)

Effect NOT going through lifestyle (could be pos. or neg.)

38

1 1

,

H L E Y H

L E Y YE

1 ( )1

EG Y

1 ( )1

DEG Y

EDUCATION GRADIENT MEDIATION BY LIFESTYLE - FEMALES

39

Education gradient

Gradient not

mediated by

lifestyles

Gradient mediated

by lifestyles

Gradient mediated by lagged lifestyles

   

self reported health -0.023 -0.024 0.001 0.001has chronic diseases 0.002 -0.003 0.006 0.004long term illness -0.051 -0.073 0.022 0.012limited activities due to poor health -0.020 -0.028 0.008 0.007heart problems -0.002 -0.012 0.010 0.006high blood pressure -0.004 0.001 -0.005 -0.002diabetes 0.001 0.009 -0.008 -0.003bone related conditions -0.005 0.000 -0.005 -0.002respiratory conditions 0.009 0.000 0.009 0.005cancer -0.009 0.001 -0.010 -0.008linear indicator of conditions -0.017 -0.026 0.008 0.005         

EDUCATION GRADIENT MEDIATION BY LIFESTYLE - MALES

40

Education gradient

Gradient not

mediated by

lifestyles

Gradient mediated

by lifestyles

Gradient mediated by lagged lifestyles

   

self reported health 0.003 0.016 -0.013 -0.009has chronic diseases 0.009 0.004 0.005 0.004long term illness 0.008 -0.013 0.021 0.019limited activities due to poor health -0.018 -0.021 0.003 0.002heart problems -0.006 -0.013 0.007 0.004high blood pressure -0.000 0.006 -0.006 -0.004diabetes 0.005 0.011 -0.006 -0.003bone related conditions -0.008 -0.002 -0.006 -0.002respiratory conditions -0.001 -0.01 0.009 0.005cancer -0.009 -0.006 -0.003 -0.003linear indicator of conditions 0.008 -0.009 0.017 0.012         

LONG AND SHORT TERM EFFECTS

In most cases short and long effects are not very different, which suggests that the first lag of lifestyles captures most of the mediating effect Impact of Ht-1 small (around 0.2)

Males generally small education gradient For Females negative effect for:

Self-reported health Long-term illness Limited activities Linear indicator of diseases

41

LONG TERM MEDIATING EFFECTS OF LIFESTYLES

Generally small Effects for females:

Blood pressure Diabetes Bones Cancer

Effects for males: Self-reported health Blood pressure Diabetes Bones Cancer

42

IMPORTANT QUALIFICATION

Finding that the mediating effect of lifestyles is small does not exclude that omitted lifestyles (unprotected sex or drug abuse) are important

vehicles of the education gradients

The effect of unobserved lifestyles is incorporated in the direct effect of education on health

43

CONCLUSIONS44

CONCLUSIONS

Education has important protective effects on the health of females

The evidence for males is less compelling: in some cases education increases bad health

The mediating effect of measured lifestyles (drink, smoke, exercise and calorie balance) is close to zero for several health outcomes

Measured lifestyles really matter for high blood pressure, cancer and respiratory diseases for females, and for bone related conditions for males

45

PROBLEMS AND THINGS TO DO

We omit several important lifestyles (for instance unprotected sex, drugs)

We need to produce standard errors for our measures of mediating effects

More data (countries)

Include “screening” among chosen “lifestyles” 46