Insurance or Savings:
I li ti
Implications
off Individual
I di id l H
Health
lth
Accounts in China
Guan Gong, Ph.D. Shanghai University of Finance and Economics
Hongmei Wang
Wang, Ph
Ph.D.
D University of Nebraska Medical Center
Lingli Xu, Ph.D. Shanghai University
Academy Health June 2010
Individual Health Accounts (IHAs)

Individual s savings account restricted to health care
Individual’s
expenses
◦ Funds accumulated at the end of life are inheritable
inheritable.

Earliest implementation is in Singapore in 1984

Implemented in urban China since 1998.

Promoted by U.S. policy since 2004

Some discussions in other developed countries
Implementation of IHAs

An important feature is the integration of IHAs and
Catastrophic health insurance.

Encourage savings for future medical expenditure and
reduce moral hazard

Reduce
R
d
risk
i k pooling
li and
d iincrease iinequality
lit iin iincome
redistribution
◦ A healthy person will accumulate large IHA balances, while the
chronically ill may accumulate nothing
◦ The scheme may look like a savings plan to the healthy and selfinsurance to the chronically ill.
Research Objectives

To explore the implications on equality of a health
insurance scheme combining IHAs with catastrophic
g data from Kunshan China
insurance using

Limited
Li
it d d
data
t prevented
t d effective
ff ti examination
i ti off the
th
effects and the evidence remains largely descriptive.

Most studies on the effects of IHAs in China draw their
conclusions based on cross-sectional analysis.

This study uses longitudinal data and examines the
effects of IHAs from a lifetime perspective.
Kunshan Health Insurance System

First carried out in 1997 and had been extended to cover
all employees and retirees in 2000.

By 2005
2005, covered 560
560,000,
000 82% of the population
population.

A combination of IHAs, Social Risk Pooling (SRP) fund,
and
d supplemental
l
t l catastrophic
t t hi risk-pooling
i k
li (SCRP) ffund
d

Contributions to IHAs and SRP fund: 10% salary
◦ 2% from employees and 8% from employers
◦ About 4%-6.5% to IHAs and 6%-5.5% to SRP fund (by age)

Contributions to SCRP fund: 50 Yuan for an employee and
30 Yuan for a retiree.
Kunshan Health Insurance System

Payment
y
depends
p
on type
yp of health services,, working
g
status, total health expenditure, etc.

IHAs used for outpatient expenditure and SRP fund for
inpatient expenditure
Outpatient
Employee Retiree
IHA
Deductible
SRP fund
CSRP fund
Cap
600
20%
/
3000
Inpatient
Employee Retiree
/
/
300
600
500
10% <50,000
<50 000
12%-5%
12%
5% 6%-2
6% 2.5%
5%
/
50,000-200,000 10%-5% 10%-5%
200,000+
Other
Data

Longitudinal health expenditure data in Kunshan city
city,
Jiangsu Province from 2005 to 2007.

Individual health insurance record from the Bureau of
Social Insurance of Kunshan.

A sample
l off 7000 enrollees
ll
were randomly
d l selected,
l t d with
ith
6355 have all full records for all three years.
Sample
p Statistics
Variable
Mean Variance
Age（2005）
Gender（＝Male）
43.7
0.56
Health expenditure
from 2005 to 2007
N expenditure
No
dit
2005 Only
2006 Only
2007 Only
2005 and 2006
2005 and 2007
2006 and 2007
All three years
0.30
0
30
0.06
0.05
0.07
0.15
0.11
0.14
0.12
Variable
Mean Variance
10.79 Contributions to IHAs
0.49
2005
497
172
2006
503
305
2007
512
295
Health expenditure 2005 1,521 12,569
0 46
0.46
O t ti t
Outpatient
626
1 104
1,104
0.24
Inpatient
895 12,399
0.17 Health expenditure 2006 1,597
9,746
0.30
Outpatient
645
1,534
0.18
Inpatient
953
8,889
0.26 Health expenditure 2007 1,615 11,317
0.46
Outpatient
652
2,011
0.32
Inpatient
963 10,144
• Contributions to IHAs can be used as a proxy for salary income
Concentration of Health Expenditure
1
Year 2005
Year 2006
Year 2007
Average of three years
0.9
Perce
ent of Expen
nditures
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
0.6
Percent of Individuals
0.7
0.8
0.9
1
Percent Distribution of 2006 and 2007 Expenditure,
b 2005 E
by
Expenditure
dit
IInterval
t
l and
db
by A
Age (5
(5x5
5 ttable)
bl )
2005
Age
16-29
30-39
40-49
50-59
60+
2005
Expenditure
0
0-300
300-1000
1000-4000
Above 4000
0
0-300
300-1000
1000-4000
Above 4000
0
0-300
300-1000
1000 4000
1000-4000
Above 4000
0
0-300
300-1000
1000-4000
Above 4000
0
0-300
300-1000
1000-4000
Above 4000
0
54 9 (48
54.9
(48.4)
4)
32.2 (24.2)
26.7 (19.9)
19.4 (19.4)
17.8 (17.8)
38.0 (28.1)
35.7 (26.9)
28.0 (27.6)
25.2 (25.8)
15.6 (14.1)
36.2 (19.0)
45.0 (23.5)
34.7 (14.7)
29 4 (14
29.4
(14.6)
6)
15.2 (10.5)
30.7 (15.1)
37.0 (23.4)
26.9 ((19.1))
14.7 (16.1)
13.0 (13.0)
35.1 (30.6)
40.3 (22.1)
20.1 (10.7)
19.9 (14.5)
14.7 (14.7)
2006 (2007) Expenditure
0-300
300-1000
9 7 (14.9)
9.7
(14 9)
21 0 (21
21.0
(21.4)
4)
31.5 (30.2)
24.8 (17.5)
11.4 (2.5)
33.1 (54.7)
5.4 (8.5)
20.2 (18.6)
13.1 (14.0)
14.0 (13.1)
18.1 (20.3)
29.1 (23.1)
16.2 (12.9)
19.3 (32.1)
7.6 (8.7 )
40.0 (43.6)
15.4 (18.5)
16.3 (23.5)
8.2 (8.2 )
18.5 (8.2 )
18.0 (27.5)
30.7 (27.2)
16.5 (21.8)
29.9 (30.5)
6.0 (31.0)
31.7 (27.0)
6 0 (13.3)
6.0
(13 3)
23 8 (16
23.8
(16.6)
6)
18.6 (18.6)
9.3 (10.5)
28.0 (18.6)
25.8 (46.9)
35.9 (22.3)
20.1 (30.4)
9.0 ((11.7))
39.3 ((41.8))
9.5 (3.8 )
26.7 (10.9)
3.1 (12.4)
21.0 (18.5)
16.2 (8.1)
30.6 (21.6)
23.4 (23.4)
9.1 (9.1)
14.1 (7.4 )
36.2 (46.3)
7.8 (5.4 )
12.7 (29.5)
17.3 (17.3)
17.3 (17.3)
1000-4000
14 0 (13
14.0
(13.8)
8)
4.0 (16.8)
16.5 (19.9)
41.1 (37.2)
24.3 (38.3)
11.2 (26.4)
20.0 (23.3)
18.6 (10.2)
37.0 (26.6)
29.6 (51.9)
12.6 (21.1)
5.9 (14.0)
21.0 (24.3)
36 4 (51
36.4
(51.0)
0)
17.4 (46.5)
11.4 (11.7)
2.7 (20.1)
21.9 ((20.1))
37.4 (59.2)
22.2 (15.4)
8.1 (29.7)
15.6 (27.3)
16.8 (16.8)
40.4 (30.7)
14.7 (34.7)
Above 4000
0 4 (1
0.4
(1.5
5)
7.4 (11.4)
12.3 (3.0 )
14.0 (16.3)
30.8 (16.8)
3.6 (2.2)
8.8 (4.8)
5.8 (9.8)
6.2 (5.6)
28.2 (17.8)
2.4 (5.3)
2.8 (10.4)
6.7 (3.0)
4 6 (4
4.6
(4.6)
6)
39.5 (14.0)
4.0 (7.8)
4.4 (3.8)
3.0 ((7.4))
12.3 (10.0)
40.7 (40.7)
9.9 (9.9)
11.7 (18.2)
12.8 (18.8)
19.3 (19.9)
36.0 (16.0)
Methods

Four-part model estimation using data from 2005 to 2007
(Manning et al. 1981,1987 and Duan et al. 1983, 1984).
◦ Model estimates based on demographic and lagged expenditure

Non-parametric method for simulation of lifetime distribution of
individual health expenditures and payment sources
sources.
(Eichner et al. 1998)
◦ Simulate individual health expenditures in a life time based on the
four part model estimates
◦ Calculate payment from IHAs and end-of-life IHA balances
◦ Calculate expenditure paid by out-of-pocket
Four--part Model Specification
Four
Prob. of health expenditure:
Pr MED i  0     x1i  1  ,
Prob. of inpatient expenditure: PrINPi  0 MEDi  0  x2i 2  ,
Outpatient expenditure:
Inpatient expenditure:
logOUTPi MEDi  0  x1i  3  vi ,
logINPi INPi  0  x2i  4  wi ,

X1i include Age,
g , Gender,, Interaction between age
g and g
gender,,
Annual contribution, and lagged expenditures in year t-1 and t-2

X2i include X1i variables, Outpatient expenditure in year t, and
whether
h h outpatient
i
expenditure
di
reaches
h 1
1500
00 iin year t
Estimation Results of FourFour-Part Model
a
a
Gender=Male
First Part: Probit
Probability of
health expenditure
-0.489 (0.123)***
Second Part: Probit
Probability of
inpatient expenditure
0.530(0.139)***
Age < 30 in 2007
30<Age<40 in 2007
40<Age<50 in 2007
-0.785 (0.138)***
-0.853 (0.115)***
-0.411 (0.122)***
0.103 (0.191)
0.159(0.137)
0.491(0.137)***
50<Age<60 in 2007
-0.284 (0.125)**
Variables
0.022(0.151)
Third Part: OLS
Outpatient
expenditure
-0.296(0.076)
***
***
-0.418(0.094)
0.052 (0.071)
***
a
Fourth Part: OLS
Inpatient
expenditure
***
1.175 (0.187)
-0.246(0.261)
-0.127(0.205)
-0.303(0.073)
0.015 (0.074)
1.209 (0.180)
***
1.337 (0.232)
***
-1.452(0.405)
***
*
-1.710(0.304)
***
**
-2.176(0.275)
***
-1.914(0.280)
***
Age < 30 and Gender=male
0.096 (0.163)
-0.669(0.273)**
0.116 (0.125)
30<Age<40 and Gender=male
0.466 (0.133)***
-0.821(0.184)***
-0.174(0.090)
40<Age<50 and Gender=male
0.268 (0.139)*
-0.858(0.72)***
50<Age<60 and Gender=male
0.126 (0.145)
0.231(0.179)
0.222 (0.090)
-0.139(0.094)
DM1:Had expenditure in 2006 only
0.298 (0.155)*
-0.707(0.235)***
-1.022(0.103)
***
2.510 (0.407)
***
DM2:Had expenditure in 2005 only
0.459 (0.130)***
-1.080(0.455)**
-0.623(0.208)
***
-1.599(0.554)
***
DM3: Had expenditure 2005 & 2006
0.464 (0.184)**
-1.343(0.256)***
-2.080(0.122)
***
-2.971(0.370)
***
Log expenditure in 2006 if DM1=1
0 184 (0.025)***
0.184
(0 025)***
0 114(0 033)***
0.114(0.033)***
0 220 (0.015)
0.220
(0 015)
***
-0.468(0.050)
0 468(0 050)
***
Log expenditure in 2005 if DM2=1
-0.049 (0.020)**
-0.202(0.066)***
*
0.168 (0.067)
**
Log expenditure in 2006 if DM3=1
0.197 (0.025)***
-0.253(0.031)***
0.363 (0.016)
***
0.241 (0.036)
***
Log expenditure in 2005 if DM3=1
-0.068 (0.024)***
-0.046(0.028)*
0.051 (0.015)
***
Log outpatient expenditure in 2007
—
0.216(0.041)***
—
0.415 (0.073)
***
Outpatient expenditure in 2007 >1500
—
-0.074 (0.092)
—
-0.538(0.137)
***
-0.233 (0.049)***
—
0.219
6355
-0.010 (0.091)
—
0.127
3902
Log contributions to IHAs in 2007
Variance of random shocks
Pseudo R-square/R-square
Sample
a
0.064 (0.033)
***
0.192 (0.044)
1.224（0.027）
0.231
3902
a
0.041 (0.041)
***
0.911 (0.081)
0.652（0.006）
0.994
264
standard deviations are in parentheses, *** indicates significant at 0.01, ** indicates significant at 0.05, and * indicates significant at 0.10
Simulation

Generate a sample of 10,000 ages between 16 to 29, based on
actual demographic data in 2005 including age, gender, and
annual contribution.

Generate health expenditure in 2006 based on actual distribution
in 2005 and the 5x5 table.

Predict
P
di 200
2007 expenditure
di
using
i simulated
i l dd
data iin 200
2005 and
d 2006
and repeat the process to get lifetime expenditure till age 80.

A
Assumptions:
ti
◦
a 1% increase in annual contribution to IHAs. (0%, 3%)
◦ Men retire at 60 and women at 50; pension is calculated based on
contribution and a fixed formula.
Model Fit _ Distribution in 2007
Age < 30
All Age Groups
1
1
Actual expenditure
vs. Predicted
expenditure
distribution in 2007
.9
2007a
2007p
0.9
.8
0.8
Cumulative Probability
y

2007a
2007p
.7
.6
.5
0.7
0.6
0.5
.4
0.4
.3
.2
0
2
4
6
8
Log of Expenditure
10
12
14
0
2
4
30 <= Age < 40
2007a
2007p
.9
2007a
2007p
0.9
.8
0.8
.7
.6
.5
0.7
0.6
0.5
.4
0.4
.3
0.3
.2
0
2
4
6
8
Log of Expe nditure
10
12
0.2
14
0
2
4
50 <= Age < 60
6
8
Log of Expenditure
10
12
10
12
Age >= 60
1
1
2007a
2007p
.9
2007a
2007p
0.9
.8
0.8
.7
.6
.5
.4
0.7
0.6
0.5
0.4
.3
0.3
.2
0.2
.1
12
1
Cumulative Probability
Fits well for the total
sample and at
different age
categories
categories.
10
40 <= Age < 50
1
Cumulative Probability

6
8
Log of Expenditure
0
2
4
6
8
Log of Expe nditure
10
12
14
0.1
0
2
4
6
8
Log of Expenditure
Model Fit _ Distribution at Selected Ages
30 <= Age < 40
Age < 30
1
1
Distributions of lifetime
predicted expenditure
vs. distributions of
actual expenditure at
selected ages
0.8
.8
.7
.6
.5
0.6
0.5
.4
0.3
0
2
4
6
8
Log of Expenditure
10
12
0.2
14
0
2
4
6
8
Log of Expenditure
40 <= Age < 50
0.8
0.6
0.5
0.4
0.6
0.5
0.4
0.2
0
2
4
6
8
Log of Expenditure
10
12
14
Age >= 60
1
2007a
predicted value
.8
.7
.6
.5
.4
.3
3
.2
.1
0
0.7
0.3
0
3
0.3
0
2007a
predicted value
0.9
0.7
9
.9
12
1
2007a
predicted value
0.8
0.2
10
50 <= Age < 60
1
Cumulative Probability
Fit well for
f all different
ff
age categories.
0.7
0.4
0.9

2007a
predicted value
0.9
Cumulative Probability

2007a
predicted value
.9
2
4
6
8
Log of Expenditure
10
12
14
0.1
0
2
4
6
8
Log of Expenditure
10
12
Simulation Results_IHA
Results IHA Balance

Most people retain a zero or low level of end of life IHA balance
◦ 58% end up with nothing in IHAs at the end of life
◦ 78% end up with balances less than 1000 Yuan
◦ 87% end up with balances less than 10% of total contribution
Di t ib ti off EOL IHA b
Distribution
balance
l
and
d It
Its Sh
Share off T
Total
t lC
Contribution
t ib ti
IHA balance
(%)
<1,000
<2,000
,
<5,000
<10,000
<50,000
,
<80,000
<100,000
78.1
84.3
87.5
88.5
96.7
99.1
99.3
IHA balance as a %
of total contribution
<0%
<10%
<20%
<50%
<70%
<90%
<100%
(%)
58.0
87.1
88.3
89.8
90.2
90.2
100.0
Simulation Results
Results_Financial
Financial Sources

52% of lifetime health expenditure is from individual financial
sources (IHAs and Out-of-pocket).

Out-of-pocket expenditure accounts for 59% of the individual
financial burden
Percent Distribution of sources for expenditure, cumulative by age
Age
30
45
5
55
65
80
IHA
(I)
38.9
34.6
3
6
29.6
25.0
21.3
OOP
(II)
38.5
39.9
39
9
37.5
33.1
30.5
SRP SCRP
(III)
(IV)
22.3
0.0
24.1
1.4
27.5
5.5
33.5
8.4
39.2
9.1
Individual Burden
(I+II）
77.4
74.5
5
67.0
58.1
51.8
Social Risk Pooling
（III+IV）
22.6
25.5
55
33.0
41.9
48.3
Simulation Results_OutResults_Out
_
-of
of--p
pocket Expenditure
p

Large variation in out-of-pocket spending in the form of
deductibles and co-insurance

Average out-of-pocket expenditures for people with a IHA
balance 5,000 or less is about 50 times those for people with a
IHA balance greater than 5,000
Average out-of-pocket expenditure by end-of-life IHA balance
IHA balance
0
<=1,000
<=2,000
< 5 000
<=5,000
>5,000
Average OOP
expenditure
64,900
64
900
60,800
59,700
59 200
59,200
1,200
IHA balance as a
percentage of total
contribution
<=1%
<=3%
<=5%
< 10%
<=10%
>10%
Average OOP
expenditure
63,300
63
300
61,600
60,600
59 500
59,500
1,300
Conclusions

Relatively equal low level of IHA balance at the end of life
suggests that IHAs primarily serve as self-insurance purpose
rather than as saving
g accounts.

The equality of IHA balance accumulation at the end of life
comes at the cost of large variation in out-of-pocket
out of pocket spending in
the form of deductibles and coinsurances.

Under Kunshan’s
Kunshan s current health insurance system,
system most people
will deplete their IHA’s and pay a large fraction (30-40%) of
lifetime expenditure
p
out-of-pocket.
p
Limitations

Health expenditure data are only those captured in the system
system,
expenditure on pharmaceuticals from undesignated stores are
not taken account.

Only have three years of actual data to estimate the four-part
model.
model

Strong assumptions were made while simulating lifetime
expenditure; sensitivity analysis were conducted to provide a
robust check.

Total
T
t l contribution
t ib ti is
i used
d as a proxy ffor salary
l
iincome b
butt other
th
component of income are not included in the model.

Analysis was based on data for employees and retirees.
Policy Discussion

The effect of IHA scheme on equality of income distribution is
complicated and has to be examined under specific health
insurance schemes.

Under current health insurance system in Kunshan, the
demand-side
demand
side cost sharing should be reduced
reduced, or subsidies may
have to be provided to the low-income to ensure access to care.

IHA s integrated with various types of insurance mechanisms
IHA’s
may lead to different outcomes on equity, efficiency, and quality.
Deliberations are needed in various aspects
p
of the financing
g
and payment system to reach optimum performance.