Transcript Diapositiva 1

EDUCATION AND HEALTH: WHAT IS
THE ROLE OF LIFESTYLES?
Giorgio Brunello (University of Padova)
Margherita Fort (University of Bologna)
Nicole Schneeweis (University of Linz)
1
Rudolf Winter Ebmer (University of Linz)
Regensburg May 2011
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 lifestyles

3
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
LITERATURE
6
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 gender
7
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
THEORY
9
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
it  U (Cit )  G ( Lit )  h( E ) H it
Health production function
Hit  Li,t 1 Ei  Yit  Hi,t 1
Budget constraint
Yit ( Ei )  Cit  pt Lit
Inter-temporal utility
T
r
  t  r
r 0
11
OPTIMAL LIFESTYLES
Lit  L( Ei | X it )
Using this in the health production function and substituting sequentially
lagged health yields
H it  H ( Ei | X it )
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
DATA
15
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 age
16
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)
self
reported Has chronic Long term
diseases
illness
poor
health
Heart
diseases
High blood
pressure
Diabetes
Bone
related
diseases
Respiratory
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
782
Czech
Republic
41.76
77.65
51.71
29.64
41.68
13.58
24.53
9.37
4.04
12.02
63.33
2452
Denmark
20.81
69.98
46.78
23.01
26.54
6.10
25.48
10.16
5.43
11.80
59.29
1899
England
37.30
82.88
62.00
41.24
38.64
3.62
44.88
15.56
4.54
10.70
72.27
4779
France
33.06
70.94
45.74
31.30
26.13
8.32
29.17
7.31
5.35
11.31
63.41
2223
Italy
33.73
71.12
35.81
26.00
33.89
8.17
31.20
7.19
3.29
8.82
59.83
2092
Netherlands
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
7415
Total males
35.04
77.36
51.28
31.02
34.91
6.98
39.08
10.86
4.80
10.64
65.71
8652
Total
females
33.87
75.34
49.87
31.59
34.01
7.76
31.33
10.66
4.63
10.89
65.63
16067
Country
18
IV ESTIMATES
19
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
FIRST STAGE ESTIMATES BY GENDER
VARIABLES
females
males
years of compulsory education
0.268
(0.057)***
0.325
(0.079)***
Observations
F test
8,652
21.82
7,415
17.10
Note: clustered standard errors in parentheses
21
POOLING TESTS, FIRST STAGE AND REDUCED FORM
Health variable
Pooling test Pooling test reduced form
firstage males
males
Pooling test firstage
females
self reported bad health
does not reject
rejects
does not reject does not reject
has chronic diseases
does not reject
rejects
does not reject
long term health
does not reject
rejects
does not reject does not reject
limited activities (b/c of poor health)
does not reject does not reject does not reject
heart problems
does not reject does not reject does not reject does not reject
high blood pressure
does not reject does not reject does not reject does not reject
diabetes
does not reject
bone related problems
does not reject does not reject does not reject does not reject
respiratory problems
does not reject
rejects
does not reject
rejects
cancer
does not reject
rejects
does not reject
rejects
indicator of diseases linear
does not reject does not reject does not reject does not reject
rejects
Pooling test reduced form
females
rejects
rejects
does not reject does not reject
Note: “rejects” means that the null hypothesis of poolable education coefficients is rejected
22
EFFECTS OF EDUCATION ON HEALTH OUTCOMES. FEMALES
(SEMI-ELASTICITIES)
Observa
tions
Probit estimate
Reduced form
2SLS
IV
-0,079
***
-0,008
***
-0,015
***
-0,088
***
-0,036
***
-0,041
***
-0,071
***
-0,018
***
-0,042
***
0,036
**
-0,017
-0,05
**
-0,038
***
-0,036
**
-0,01
-0,189
**
-0,142
**
-0,135
8,602
-0,04
-0,197
**
-0,129
**
-0,137
**
-0,193
0,0005
0,002
-0,008
8,652
-0,065
**
-0,206
***
-0,029
-0,245
**
-0,776
**
-0,111
-0,241
***
-0,462
***
-0,128
8,652
0,05
(0.060)
-0,134
0,189
0,235
8,652
-0,502
8,652
-0,022
-0,084
-0,347
*
-0,084
Health variable
self reported bad health
has chronic diseases
long term illness
limited activities (b/c of poor health)
heart problems
high blood pressure
diabetes
bone related problems
respiratory problems
cancer
indicator of diseases linear
8,652
8,651
8,652
8,652
8,652
8,652
23
EFFECTS OF EDUCATION ON HEALTH OUTCOMES. MALES (SEMIELASTICITIES)
Reduced form
estimate
2SLS
IV Probit
-0,063
***
-0,007
***
-0,017
***
-0,055
**
0,009
-0,171
*
0,029
7358
0,037
**
0,116
**
-0,183
**
0,023
**
0,111
**
-0,07
***
-0,012
***
-0,017
***
-0,038
***
-0,043
***
-0,052
***
0,006
**
-0,012
***
0,009
0,029
0,062
7415
0,093
***
0,044
0,286
**
0,137
7415
-0,057
-0,175
0,22
***
0,135
*
-0,238
-0,05
-0,155
-0,181
7415
0,098
**
-0,13
0,301
*
-0,399
7415
0,019
**
0,06
*
0,275
***
-0,326
**
0,06
*
Probit estimate
Observa
tions
Health variable
self reported bad health
has chronic diseases
long term illness
limited activities (b/c poor health)
heart problems
high blood pressure
diabetes
bone related problems
respiratory problems
cancer
indicator of diseases (linear)
7415
7415
7415
7415
7415
7415
24
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 …?“
P ( D)  P ( S ) P ( D | S )
S...Screening D ... Disease

Marginal effect of education:
 P( D)  P( S ) P( D | S )  P( D | S ) P ( S )
E
E
E
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
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
Marginal effect
of YCOMP
Males
Marginal effect of Marginal effect of
Marginal effect of
YCOMP YCOMP 5 years
YCOMP
placebo test –
ahead - placebo test
- Females
Males
-Males
Marginal effect of
YCOMP
- placebo test Females
Marginal effect of
YCOMP 5 years
ahead
- placebo test Females
Health variable
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
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
0.039
-0,057
-0,065
**
-0,206
***
-0.061
*
-0.252
***
0.050
diabetes
0.058
*
-0.068
bone related problems
-0,05
-0.013
0.111
**
-0,029
-0.030
0.011
respiratory problems
0,098
**
-0,13
0.089
*
-0.138
-0.029
0,05
0.115
0.112
-0.022
-0,134
-0.178
-0.159
0,019
**
0,026
***
0.0207
*
-0,022
*
-0,017
0.0176
limited activities due to
poor health
cancer
indicator of diseases
linear
-0.033
0.026
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
H icgbt  1 Licgb ( t 1)   2 Eicg   3 H icgbt 1   icgbt
 icgbt  ucgbt   cbt  eicgbt
where i=individual; c: cohort; g: gender; t=time. We assume
E[eicgbt | b, g , c, t ]  0
We take gender differences to remove  cbt
F
H cbt  1Lcb ( t 1)  1F LFcb ( t 1)   2 Ec 0   2F EcF0   3H cbt 1   3F H cbt
1  ucbt
We model the residual error as function of Z (parental
background and early health from SHARELIFE)
F
ucbt  1Z cbt  2F Z cbt
 vcbt
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
Reduced form
health equation Females
years of schooling
-0.017
***
lagged dependent variable
drunk alcohol every day
in year t-1
was smoking in year t-1
did vigorous activity in
year t-1
BMI in year t-1
father drunk or had
mental troubles (age 10)
presence of parents in the
house at 10
Dynamic health
Reduced form Dynamic health
equation health equation equation Females
Males
Males
-0.005
***
0.503
-0.014
***
-0.006
***
0.491
***
***
-0.029
-0.032
**
**
0.064
0.047
***
***
-0.016
-0.023
***
***
0.007
0.005
***
***
0.033
**
0.021
*
0.021
0.004
0.017
0.011
-0.001
-0.007
34
ESTIMATED EFFECTS OF EDUCATION ON SELF REPORTED BAD HEALTH, WITH
AND WITHOUT HEALTH LIFESTYLES. GENDER DIFFERENCES. CELL DATA.
WEIGHTED REGRESSIONS.
Dynamic health
Dynamic health
Reduced form health
Reduced form health
equation with income
equation with income
equation -Females
equation - Males
- Females
- Males
years of schooling
-0,023
-0,018
***
*
lagged dependent variable
0.003
0.013
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
35
ESTIMATED EFFECTS OF EDUCATION ON LIMITED ACTIVITY DUE TO POOR
HEALTH, WITH AND WITHOUT HEALTH LIFESTYLES. LINEAR PROBABILITY
MODELS MICRO DATA
Reduced form
Dynamic health
Reduced form Dynamic health
health equation - equation with income - health equation equation with
Females
Females
- Males
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)
presence of parents in the house at 10
0.0259
**
0.0125
-0.0005
-0.0054
0.0188
0.0150
0.0106
0.0018
36
ESTIMATED EFFECTS OF EDUCATION ON LIMITED ACTIVITY BECAUSE OF POOR
HEALTH, WITH AND WITHOUT HEALTH LIFESTYLES. GENDER DIFFERENCES.
CELL DATA. WEIGHTED REGRESSIONS.
Dynamic health
Reduced form
Reduced form Dynamic health
equation with
health equation
health equation equation with
income -Females
- Males
income - Males
Females
years of schooling
-0,020
***
lagged dependent variable
-0,024
***
0,146
drunk alcohol every day in year t-1
0,018
-0.017
**
0.156
*
0.029
was smoking in year t-1
-0,022
-0.076
did vigorous activity in year t-1
-0,001
BMI in year t-1
0,008
*
0,000
-0.034
*
0.002
real income
Observations
232
230
-0.018
***
-0.001
232
230
37
ESTIMATION OF MEDIATING EFFECT

We estimate both the dynamic health equation
and the „reduced form“ health equation
H   L1  E   Y   H 1
L   E,


Y  YE
Total effect of education on health
1
(   Y )
1

EG 

Use also reduced form H=f(E)
Effect NOT going through lifestyle (could be pos.
or neg.)
 DEG  1 (  Y )
1
38
EDUCATION GRADIENT
MEDIATION BY LIFESTYLE - FEMALES
Education
gradient
self reported health
has chronic diseases
long term illness
limited activities due to poor health
heart problems
high blood pressure
diabetes
bone related conditions
respiratory conditions
cancer
linear indicator of conditions
-0.023
0.002
-0.051
-0.020
-0.002
-0.004
0.001
-0.005
0.009
-0.009
-0.017
Gradient
Gradient not Gradient
mediated by
mediated by mediated by
lagged
lifestyles
lifestyles
lifestyles
-0.024
-0.003
-0.073
-0.028
-0.012
0.001
0.009
0.000
0.000
0.001
-0.026
0.001
0.006
0.022
0.008
0.010
-0.005
-0.008
-0.005
0.009
-0.010
0.008
0.001
0.004
0.012
0.007
0.006
-0.002
-0.003
-0.002
0.005
-0.008
0.005
39
EDUCATION GRADIENT
MEDIATION BY LIFESTYLE - MALES
Education
gradient
self reported health
has chronic diseases
long term illness
limited activities due to poor health
heart problems
high blood pressure
diabetes
bone related conditions
respiratory conditions
cancer
linear indicator of conditions
0.003
0.009
0.008
-0.018
-0.006
-0.000
0.005
-0.008
-0.001
-0.009
0.008
Gradient
Gradient not Gradient
mediated by
mediated by mediated by
lagged
lifestyles
lifestyles
lifestyles
0.016
0.004
-0.013
-0.021
-0.013
0.006
0.011
-0.002
-0.01
-0.006
-0.009
-0.013
0.005
0.021
0.003
0.007
-0.006
-0.006
-0.006
0.009
-0.003
0.017
-0.009
0.004
0.019
0.002
0.004
-0.004
-0.003
-0.002
0.005
-0.003
0.012
40
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
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CONCLUSIONS
44
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
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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