Transcript Change Matters: The Change in Health and the Demand for

Jennifer L. Kohn
[email protected]
Change Matters:
A Dynamic Demand for Medical Care
Agenda:
1. Motivation
2. Literature & Contribution
3. Model
4. Empirical Tests
5. Conclusion and Future Research
Motivation
The top 5% of spenders account for nearly 50% of spending.
poor
health
> 65
100.0%
96.9%
90.0%
Percent of Total Health Care Spending
< 65
80.3%
80.0%
73.6%
70.0%
60.0%
64.1%
> $13,000
< $730
49.0%
50.0%
40.0%
30.0%
22.5%
20.0%
10.0%
3.1%
0.0%
Top 1%
Top 5%
Top 10%
Top 15%
Top 20%
Top 50%
Bottom 50%
Percent of U.S. Population
Source: Kaiser Family Foundation calculations using data from the U.S. Department of Health and Human Services,
Agency for Healthcare Research and Quality, Medical Expenditure Panel Survey (MEPS), 2004.
Literature & Contribution
Theoretical Literature:
Willingness to Pay: Mishan (1971), Berger et.al. (1987), Murphy & Topel (2006)
Human Capital: Grossman (1972), Ehrlich & Chuma (1990), Liljas (1998)
Theoretical Contributions:
1. An explanation for the observed pattern of medical care spending.
2. Model of health transition consistent with current accounting.
3. Testable hypotheses about the demand for medical care.
Empirical Literature:
Theoretical Testing: Grossman (1972), Wagstaff (1986, 1993), VanDoorslayer (1987)
Health & Wealth: Viscusi & Evans (1990), Blau & Gilleskie (2006), Finkelstein et. al. (2008)
Econometrics: Newhouse et. al. (1980), Deb & Trivedi (1997), Greene (2007)
Empirical Contributions:
1. Multiple equation model consistent with the theory.
2. Empirical support for the significance of the change in health.
3. Empirical support for the assumption that health and wealth are
complements and consumption and medical care are not separable.
Model: Utility
“Utility” = Consumption, Health, and the Change in Health
yesterday
yesterday
Health gets better!
Health gets worse.
today
Literature: Habit Formation -- Constantinides (1990)
Adaptation -- Groot (2000), Gjerde et. al. (2005)
Model: Utility
Change in Health
max LU   ertU  Z (t ), H (t ), (t ) dt
T
Z ,m,T
0
1. Health State, not a flow of healthy days
Grossman (1972), Ehrlich & Chuma (1990)
H
Berger et. al., (1987)
“…the utility individuals derive from consumption
depends on their state of health.”
2. General functional form
Murphy & Topel (2006)

RU   H (t )U c(t ), l (t )da
a
Model: Change in Health
Change in Health = Investment - Depreciation
medical care
AND
current health
Medical literature:
co-morbidities
Grossman (1972)
I (t )  I (M (t ), m(t ); E (t ))
I
0
H
an amount
NOT
a rate x stock
FASB # 142 (2001)
Old way:
New way:
Ht  It  t Ht 1
t  It   t
H
   0
H

I

0
H H
Model: Change in Health
Source: NYT 7/30/06
Multiplicative Depreciation
120
Health
100
80
60
40
20
0
1
10 19 28 37 46 55 64 73 82 91 100
time
Higher health,
more negative the decline in health
Higher health,
less negative the decline in health
Model: Change in Wealth
and Endpoint Conditions
R
 R  rR(t )  w( H (t ))  P(t )m(t )  Z (t )
t
Endpoint Conditions:
H (0)  H 0  H min
H (T )  H min
R (0)  R0  0
R (T )  0
T  Tmax
Hmin and Tmax are exogenously
fixed at the beginning of the
planning horizon
Why do we demand medical care?
No inevitable disequilibrium
Marginal Benefits
From Health
Marginal utility from health +
Marginal income from health
+ Marginal utility from the
change in health x
Marginal productivity of
health to investment
=
Marginal Cost of
Health Capital
Marginal cost of medical care +
Interest rate + rate of depreciation
- Marginal productivity of
health to investment
Inevitable disequilibrium!
Larger disequilibrium the lower the state of health
Larger disequilibrium the greater the decline in health
Why do we demand more medical care?
Grossman (1972): “…even though Health capital falls over the life cycle, gross
investment might increase, remain constant or decrease.”
U hi

i
 '( H )  (1  r )  Wi    i 1  r   i 
 

Benefits
t
↓
T
(t)H(t)
H
Marginal benefits of longevity
decrease over the lifecycle
U H  U H
 wH  g (t )  r  g (t )   H 
R
 (0)
Benefits
(t)
H
Marginal utility from the change in
health and marginal productivity of
health keep benefits high.
Why do we demand more
medical care at the end of life?
RAND (2003)
Ehrlich & Chuma (1990)
g(T)
[ ( u ) r ( u )] du 

H
t
g (t )  g (T )e


T
t
T
  U h (u )
  tu  ( s )r ds 
 wh (u )  e

du
  R (0)


Time Path of Medical Care Demand


 
  rt    ?
  
  
e  m  U Z Z  U H H  U    t   H H    t    

  
 

 








 rt
H 
  e U     mt   mH H   m  t







sgn m  sgn  


    rt


 rt
H
R
  m  re U  e U H  U  H     H   wH 





 

  R  rp (t )  p 

Time Path for Consumption

?

?
 ?

   ?  
H  U ZH  U Z  H   U Z   t   m m    



Z 

U ZZ
The demands for consumption and medical care are not separable.
The sign of the relationship between health and consumption, and
between the demands for medical care and consumption are
empirical questions.
Summary of Theoretical Implications
1. Change matters: the change in health is a significant factor in
an individual’s demand for medical care.
2. Price matters less over the lifecycle.
3. Quality of life matters more than longevity at the end of life.
4. The advance of medical technology increases the demand for
medical care over time.
5. Health and wealth: health and consumption and consumption
and medical care demands are not separable.
Empirical Issues
1. Consistency with the theory Gilleskie (1998)
Joint estimation of the demands for medical care and consumption
Consistency with economic restrictions
2. Unobservable health and price for medical care
Single or multiple indicator, Self-assessed health
MIMIC Van der Gaag and Wolfe (1982), Ersblad et. al. (1995)
Latent Variable Bound (1999), Disney et. al. (2006)
MCA Greenacre (2002) Hadley and Weidman (2006)
3. Discrete counts and unobservable heterogeneity
Negative binomial, finite mixture Deb and Trivedi (1997), HHG (1984)
Incidental parameters problem Greene (2007)
Initial conditions problem Wooldridge (2005)
Empirical Specification
Non-linear system of equations (NLSUR)
3


mj
Z
Z  exp cZZ p   cZm j p  bZZ Budget  bZ Change  bZH Healtht 1   Z Controls    Z
j 1


3


mj
j
Z
m  exp cm j p  cZm j p   cm j mi p mi  bm j Z Budget  bm j Change  bm j H Healtht 1   m j Controls    m j
i 2


j = hospital days (H), tests & services (TS), and general practitioner visits (GP)
Controls = marital status and education
Errors clustered by individual and correlated across equations
Economic Restrictions:
1.
2.
Negative own-price effects (negative semi-definite Slutsky matrix)
Symmetry in cross-price effects
Hypotheses:
H 2: bZ , bZH
The greater the decline in health, the higher the demand for
medical care.
 0 The demands for health and consumption are not independent.
H 3: cZm j  0
The demands for medical care and consumption are not separable.
H1: bm j  0
Data
Data: 14 waves (1991 – 2005) of the British Household Panel Survey
FULL sample: 119,970 person-year observations
OSM balanced panel of 40,896 observations (3,145 individuals)
Health Index: MCA using all available data for each wave.
Health Index
.08
.06
0
1
2
.04
l
a
s
t
Density
o
v
e
r
.02
5 +
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+----------------------------------------------------------------+
8.5e-07
aH1t
100
Self-reported health 1 = good
h
e
a
l
t
h
Distribution of the Change in Health
.1
Self-reported Health and the Health Index
-100
-50
0
DHt
50
If the change in health matters, we should be able to see it!
100
Empirical Results
FULL Sample, P-values reported
<0
Hospital Days
>0
Tests & Services
General Practitioner
Consumption
coefficient unrestricted restricted unrestricted restricted unrestricted restricted unrestricted restricted
Change in Health
-0.0258
-0.0285
-0.0079
-0.0109
-0.0036
-0.0042
0.0031
0.0034
Lagged Health
p(Z)
p(HD)
p(TS)
p(GP)
Budget
Couple
Education
Constant
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
-0.0229
-0.0236
-0.0109
-0.0144
-0.0044
-0.0051
0.0060
0.0057
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
-0.8754
0.0890
-2.5734
0.0385
-0.4831
-0.0012
-0.6876
-0.5723
(0.159)
(0.000)
(0.000)
(0.000)
(0.000)
(0.847)
(0.000)
(0.000)
-1.8007
-1.6547
-0.0004
0.1071
0.1983
0.2640
0.0727
0.0890
(0.000)
(0.000)
(0.943)
(0.000)
(0.000)
(0.000)
(0.000)
0.8664
0.1071
0.0011
0.1685
0.0353
0.0762
0.0385
(0.000)
(0.000)
-0.0010
-0.2265
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
-0.0102
0.2640
-0.2681
0.0353
-0.4936
-0.4224
-0.0257
-0.0012
(0.896)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.847)
0.0045
0.0037
0.0011
0.0017
-0.0016
-0.0016
0.0036
0.0031
(0.002)
(0.061)
(0.001)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
-0.4165
-0.3694
0.0714
0.0952
0.0276
0.0347
0.1906
0.1919
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
0.0030
0.0448
0.1513
0.1762
0.0232
0.0285
0.1598
0.1613
(0.000)
(0.505)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
5.2973
4.2443
5.0689
2.1289
2.1796
1.5948
1.6182
1.4792
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
Robustness Results
Signs, significance and magnitudes consistent across specifications
4 Equation Model
Full Sample
OSM Sample
coefficient unrestricted
Change in Health
Lagged Health
restricted
unrestricted
restricted
Hospital Days
3 Equation Model
Full Sample
OSM Sample
unrestricted
restricted
unrestricted
restricted
Single Equation Models
Fixed Effect NB
NB
Full
OSM
Full
OSM
-0.0258
-0.0285
-0.0279
-0.0287
-0.0253
-0.0268
-0.0269
-0.0255
-0.0191
-0.0259
-0.0459
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
-0.0526
(0.000)
-0.0229
-0.0236
-0.0267
-0.0251
-0.0223
-0.0219
-0.0254
-0.0215
-0.0161
-0.0247
-0.0490
-0.0571
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
-0.0079
-0.0109
-0.0081
-0.0112
-0.0079
-0.0094
-0.0080
-0.0099
-0.0081
-0.0108
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
-0.0109
-0.0144
-0.0110
-0.0147
-0.0109
-0.0130
-0.0110
-0.0135
-0.0110
-0.0144
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
Tests & Services
Change in Health
Lagged Health
General Practitioner Visits
Full Sample
unrestricted
Change in Health
Lagged Health
OSM Sample
restricted
Full
OSM
Ordered Logit
Full
OSM
-0.0042
-0.0043
-0.0046
-0.0205
-0.0213
-0.0292
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
-0.0044
-0.0051
-0.0053
-0.0055
-0.0222
-0.0212
-0.0342
-0.0404
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
unrestricted
Lagged Health
RE Ordered Probit
restricted
-0.0036
Full Sample
Change in Health
unrestricted
OSM Sample
restricted
unrestricted
restricted
Consumption
Full Sample
OSM Sample
unrestricted
restricted
unrestricted
restricted
RE Tobit
Full
-0.0337
Tobit
OSM
Full
OSM
0.0031
0.0034
0.0039
0.0037
0.0035
0.0035
0.0037
0.0038
0.0108
0.0115
0.0138
0.0150
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
0.0060
0.0057
0.0057
0.0053
0.0059
0.0059
0.0055
0.0056
0.0203
0.0180
0.0211
0.0197
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
Health & Wealth Implications
A decline in health is associated with an increase in the
marginal utility of consumption.
Utility
U 2
0
Z H
Higher Health
Health
Lower Health
Holding price constant,
consumption declines
consumption
Z(Low)
Z(H)
Z(High)
Value of a “Life Year”

Murphy & Topel (2006)



 

     
H  U ZH  U Z  H   U Z   t   m m    



Z 

U ZZ
Value of a Life Year
Income
Consumption
Conclusion
Change Matters!
We make trade-offs between health and consumption.
96.9%
100.0%
Econometric issues for better fit to data
90.0%
Percent of Total Health Care Spending
80.3%
Do people stay in the top 5% over time?
80.0%
73.6%
70.0%
60.0%
How are the top 5% affected by price?
< $730
What is the trade-off between quality and
quantity of life?
64.1%
> $13,000
49.0%
50.0%
40.0%
30.0%
What is the effect of medical technology?
22.5%
20.0%
10.0%
3.1%
0.0%
Top 1%
Top 5%
Top 10%
Top 15%
Top 20%
Top 50%
Bottom 50%
Percent of U.S. Population
Source: Kaiser Family Foundation calculations using data from the U.S. Department of Health and Human Services,
Agency for Healthcare Research and Quality, Medical Expenditure Panel Survey (MEPS), 2004.
Future Research
The Wall Street Journal, December 12, 2006
http://online.wsj.com/article/SB116586842161546712.html?mod=editsend
Dr. Kishnani, who led the
second clinical trial, says,
"What I learned from
these trials is that each
family has to decide when
enough is enough."
How do we design a health care financing system where
every family gets to make this choice?