Analyzing Health Equity Using
Household Survey Data
Lecture 14
Who Benefits from Health Sector Subsidies?
Benefit Incidence Analysis
“Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and
Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity
Pro-poor public spending on
health care
• is an important objective of governments
and international agencies.
• This may derive from distributional
concerns and/or from human
capital/economic growth strategy.
• So, are public subsidies targeted on the
poor?
“Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and
Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity
Which benefit incidence analysis?
• BIA describes distribution of public spending, e.g. on health
care, across population ordered by living standards or other
socioeconomic /geographic characteristic.
• Simple BIA determines who receives how much of public
spending $.
• Behavioral BIA seeks to establish extent to which public
spending changes the distribution of income.
– Requires estimating behavioral responses e.g. crowd-out of private
health care
• Marginal BIA seeks to establish who gains from marginal
increases in public spending.
• Here confine attention to distribution of average spending
and abstract from behavioral responses.
“Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and
Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity
Measure of living standards
• Here we focus on the distribution of public health
care in relation to living standards and not
location, ethnicity, gender, etc
• Any measure of living standards discussed in
lecture 6 could be used
• If use ordinal measure, e.g. wealth index, then can
only determine whether distribution is pro-poor, or
pro-rich
• With a cardinal measure, e.g. income, can
establish extent to which public spending is propoor
“Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and
Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity
Three steps of BIA
1. Estimate distribution of utilisation of
public health services in relation to
measure of living standards
2. Weight units of utilisation by value of
subsidy and aggregate across health
services
3. Evaluate by comparing the distribution of
subsidies with some target distribution
“Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and
Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity
Data for estimating the distribution of
public health care utilisation should
• Be at household level from health /socioeconomic
survey
• Give health care utilisation and living standards
measure for same observations
• Distinguish between use of public and private care
(only interested in former)
• Distinguish (at least) between:
– Hospital inpatient care
– Hospital outpatient care
– Non-hospital care (visits to doctor, health centre,
polyclinic, antenatal)
• Vary recall periods with frequency of use of service
“Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and
Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity
Distribution of Public Health Care
Utilization in Vietnam, 1998
Cumulative shares
poorest 20%
Hospital care
Outpatient
Inpatient
visits
days
8.90%
10.29%
Commune
Health Centre
visits
22.65%
Polyclinic
visits
22.91%
Other public
health
services
13.22%
(standard error)
(0.9949)
(1.2141)
(1.8860)
(5.7815)
(2.9644)
poorest 40%
23.45%
27.74%
47.83%
32.81%
47.09%
(1.6629)
(2.0465)
(2.4084)
(6.2628)
(6.3806)
43.58%
47.66%
77.86%
59.29%
59.00%
(2.3987)
(2.4772)
(1.9943)
(6.8524)
(6.0599)
66.07%
70.36%
90.60%
78.24%
79.63%
(2.7376)
(2.5702)
(1.4456)
(6.5783)
(4.5689)
poorest 60%
poorest 80%
test of dominance
against 45o line
Concentration index
-
-
+
0.2436
0.1784
-0.1567
0.0401
0.0056
(robust standard error)
(0.0368)
(0.0370)
(0.0335)
(0.1042)
(0.0777)
“Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and
Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity
The poor’s share of public health
care in Asia (Equitap)
Figure 2a: Poorest quintiles' shares of public health care utilisation and total
consumption
50%
40%
30%
household consumption
hospital inpatient care
hospital outpatient care
20%
non-hospital care
10%
H G Ban
ei a g
lo ns la
ng u de
jia (C sh
ng hi
n
H (Ch a)
on in
g a)
K
on
g
In Ind
do ia
M nes
al i a
ay
s
N ia
Sr ep
i L al
Th ank
ai a
V land
ie
tn
am
0%
“Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and
Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity
Computation of the public health subsidy
• Value utilisation to allow for variation in subsidy across
services, facilities, regions and individuals, and to
aggregate across services
• Service-specific subsidy received by individual (i)
Ski  qki ckj  f ki
where qki is utilisation of service k, ckj is the unit cost of
k in region j where i resides and f is the fee paid.
• Total subsidy to individual: S 
 S
ki
i

k
ki
k
where  k adjust for differences in recall periods
“Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and
Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity
Calculation of unit costs
• Units costs derived from total public recurrent expenditure
on health care
• Disaggregate this down to geographic region, then to facility
(hospital, health centre etc.), then by service (inpatient,
outpatient, etc)
• Ideally National Health Accounts are available to do this
• If accounts data do not allow disaggregation by region and
facility, all units of a given service must be weighted equally.
Then aggregation across services is only purpose served by
application of unit subsidies.
• Service specific cost data can be difficult to obtain given
joint use of many health care resources. Facility-level cost
surveys can be useful.
Taking account of user fees
• Simplest method - divide aggregate official user fee
revenue by estimate of total utilization and assign
average to all users
• If net public expenditure available by regionfacility-service, then get variation in fee payments
at that level
• If survey provides data on payments, then can have
individual variation in fees
• If survey only gives amount paid for all services,
then compute subsidy to indv. by si    k qki ckj  fi
k
“Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and
Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity
Discrepancies between reported and
official user fees
• can be substantial and due to revenue being kept locally either
officially or unofficially
• Appropriate treatment of user fees then depends on objective:
• If to identify distribution of central govt. net expenditure, then
payments in excess of official revenue can be ignored
• But if seek distribution of net benefits, then payments made
by indv. are relevant irrespective of whether official
• If payments made to finance costs not covered by govt.
budget, then cancel out from net benefit calculation
• If payments are rent to providers, then should be
subtracted in net benefit calculation
In practice
• survey data do not identify whether
payments are centrally remitted, or if are
rent extraction
• Can estimate the distribution of official
payments by scaling all payments by a
constant equal to ratio of official to
reported user fee revenue
• Can test sensitivity of estimated subsidy
distribution to this scaling of payments as
opposed to subtracting all reported fees
“Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and
Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity
Public Health Expenditure, Unit Costs
and Subsidies, Vietnam 1998
Recurrent
Total
public exp.
D million
2704424
utilisation
'000s
D
52779 (days)
35388 (visits)
43520 (visits)
3973 (visits)
49320
2865
6183
8572
Hospital care
Inpatient
Outpatient
Comm. Health Centres 269101
Regional ployclinics
34062
Total Allocated
3007587
Notes: D
a.
b.
Unit cost
Total user fees
Mean unit subsidy
Official Reported
D m.
D m.
429128
2464000
1154000
48762
7152
17039
436280 3634960
Scaled user fees Reported user fees
D
D
a
42988
1990
6183
7916
b
23800
1690
5393
6402
Dong
Calculated from user fees reported in VLSS scaled to sum to official user fee revenue.
Calculated from actual user fees reported in VLSS (not scaled).
Source: Authors' calculations from World Bank (2001) and VLSS.
“Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and
Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity
Evaluation of public health subsidy
distribution against a target
• implies choice of an objective.
• Is subsidy pro-poor?
– Compare subsidy shares with population shares check dominance of concentration curve against 45o
– Summarise by concentration index; positive if prorich, negative if pro-poor.
• Does the subsidy reduce inequality?
– Compare subsidy shares with income shares – check
dominance of concentration curve against Lorenz
curve
– Summarise by Kakwani index (CI – Gini); positive if
inequality-increasing, negative if inequality reducing
“Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and
Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity
Distribution of public health
subsidies in Vietnam, 1998
Equivalent
household
consumption
Outpatient
poorest 20%
8.78%
(standard error)
(0.0429)
Cumulative shares
poorest 40%
poorest 60%
poorest 80%
Hospital care
Polyclinic
Inpatient
Commune
Health
Centre
Other public
health
services
10.21%
10.98%
22.65%*
23.18%*
13.22%
12.29%*
14.81%*
(1.3456)
(1.3099)
(1.886)
(5.9155)
(2.9644)
(1.1219)
(1.5426)
21.38%
24.75%
29.44%
(0.0880)
(2.1043)
(2.1703)
37.19%
45.50%*
(0.1360)
(3.0206)
*
*
47.83%
*
*
Total subsidy
Scaled user Reported user
fees
fees
31.87%
*
37.70%*
33.48%
47.09%
(2.4084)
(6.3918)
(6.3806)
(1.8559)
(2.4110)
50.12%*
77.86%*
59.88%*
59.00%*
53.11%*
60.43%*
(2.5461)
(1.9943)
(6.8763)
(6.0599)
(2.1498)
(2.5184)
73.02%
*
58.17%
67.65%
90.60%
(0.1793)
(3.2196)
(2.5157)
(1.4456)
-
+
+
+
*
78.52%
*
(6.6011)
79.63%
*
74.88%
*
81.25%*
(4.5689)
(2.1076)
(2.0504)
+
+
+
+
test of dominance
- against 45o line
- against Lorenz curve
Concentration Indexa
(robust standard error)
Kakwani Index
(robust standard error)
0.3229
0.2160
0.1444
-0.1567
0.0298
0.0056
0.1106
0.0115
(0.0083)
(0.0450)
(0.0378)
(0.0335)
(0.1035)
(0.0777)
(0.0319)
(0.0343)
-0.1069
-0.1785
-0.4797
-0.2932
-0.3174
-0.2124
-0.3115
(0.0506)
(0.0427)
(0.0376)
(0.1031)
(0.0792)
(0.0365)
(0.0379)
0.0213
0.8688
0.1010
0.0088
Subsidy shares (scaled user fees)
1.0000
“Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and
Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity
0
.2
.4
.6
.8
1
Concentration curves for health
sector subsidies in Vietnam, 1998
0
.2
.4 population proportion
.6
Cumulative
Lorenz(y)
outpatient
total subsidy
.8
inpatient
health centre
45 degree line
1
Poor’s share of public health subsidy in Asia
Poorest quintiles' shares public health subsidy
50%
household expenditure
public health subsidy
40%
30%
20%
10%
G
H
B
an
g
la
an de
sh
s
ei
lo u (C
ng
jia hin
ng a )
(C
h
H
on ina
)
g
K
on
g
In
d
In ia
do
ne
s
M ia
al
ay
si
a
N
ep
Sr al
iL
an
k
Th a
ai
la
n
V d
ie
tn
am
0%
Rich’s share of public health subsidy in Asia
Richest quintiles' shares public health subsidy
household expenditure
public health subsidy
50%
40%
30%
20%
10%
H
G
B
an
g
la
an de s
s
h
ei
lo u (C
ng
jia hin
ng a )
(C
h
H
on ina)
g
K
on
g
In
d
In ia
do
ne
s
M ia
al
ay
si
a
N
ep
Sr al
iL
an
k
Th a
ai
la
n
V d
ie
tn
am
0%
“Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and
Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity
With a few exceptions, public health subsidies
in Asia are pro-rich but inequality-reducing
Table 4: Tests of Dominance of Concentration Curves for Public Health Service Utilisation and Subsidy
Relative to Lorenz Curve and 45-Degree Line
Utilisation
Subsidy
Hospital inpatient Hospital outpatient Non-Hospital
O
O
O
Lorenz
45
Lorenz
45
Lorenz 45
Bangladesh
Gansu (China)
Heilongjiang (China)
Hong Kong SAR
India
Indonesia
Malaysia
a
Nepal
Sri Lanka
Thailand
Vietnam
Notes:
+
+
+
+
+
+
+
+
+
-
-
+
+
+
+
+
x
+
N/A
+
+
+
N/A
+
x
-
+
N/A
N/A
+
+
+
+
+
+
+
+
N/A
N/A
+
+
+
+
+
+
Hospital inpatient Hospital outpatient Non-Hospital
O
O
O
Lorenz
45
Lorenz
45
Lorenz 45
+
+
+
+
+
+
+
+
+
-
-
+
+
+
+
+
+
N/A
+
+
+
+
N/A
+
-
+
N/A
N/A
+
+
+
+
x
N/A
+
+
N/A
N/A
+
+
+
+
N/A
+
+
Total
O
Lorenz 45
+
+
+
+
+
+
x
+
+
+
+ indicates the health care utlisation/subsidy is more concentrated on the poor than
o
household consumption per equivalent adult (Lorenz) or an equal per capita distribution (45 ) at 5% significance level.
- indicates the health care utlisation/subsidy is less concentrated on the poor than
o
household consumption per equivalent adult (Lorenz) or an equal per capita distribution (45 )
x indicates that the curves cross and blank that there is no statistically significant difference
a - The Equity
results inUsing
the hospital
inpatient
columns
referOwen
to bothO’Donnell,
inpatient andEddy
outpatient.
“Analyzing Health
Household
Survey
Data”
van Doorslaer, Adam Wagstaff and
N/A
not
available
Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity
+
+
+
-
Public health subsidy is generally
pro-rich in Asia
Figure 4a: Concentration indices of public health subsidy
0.5
0.4
0.3
0.2
0.1
0
hospital inpatient
-0.1
hospital outpatient
-0.2
am
V
ie
tn
ka
ai
la
nd
Th
iL
an
Sr
N
ep
a
di
In
H
ei
lo
In
u
gl
ad
G
an
s
Ba
n
l
total subsidy
a
do
ne
sia
M
al
ay
sia
-0.4
(C
hi
ng
na
jia
)
ng
(C
hi
na
H
)
on
g
K
on
g
non-hospital care
es
h
-0.3
“Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and
Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity
But inequality-reducing
B
an
gl
ad
e
G
an sh
su
(C
H
h
ei
lo ina )
ng
ji
H
on a ng
g
(C
K
hi
on
In
na
g
di
)
a
In
do
ne
si
a
M
al
ay
s
N ia
ep
al
Sr
iL
an
Th ka
ai
la
nd
V
ie
tn
am
Figure 4b: Kakwani indices of public health subsidy
0.1
0
-0.1
-0.2
-0.3
-0.4
-0.5
hospital inpatient
hospital outpatient
-0.6
non-hospital care
-0.7
total subsidy
“Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and
Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity
In general, non-hospital care is more pro-poor than
hospital and outpatient less pro-rich than inpatient
Table 7: Tests of Dominance between Concentration Curves for different Public Health Services
Hospital inpatient
vs outpatient
Bangladesh
Gansu (China)
Heilongjiang (China)
Hong Kong SAR
India
Indonesia
Malaysia
a
Nepal
Sri Lanka
Thailand
Vietnam
Notes:
op>ip
op>ip
op>ip
op>ip
op>ip
Utilisation
Hospital inpatient
vs non-hospital
N/A
N/A
Hospital outpatient
vs non-hospital
Subsidy
Hospital inpatient Hospital inpatient
vs outpatient
vs non-hospital
N/A
N/A
non-h>ip
non-h>op
non-h>ip
non-h>op
non-h>ip
non-h>op
non-h>(ip+op)
op>non-h
non-h>ip
non-h>op
non-h>ip
non-h>op
op>ip
op>ip
Hospital outpatient
vs non-hospital
N/A
N/A
ip>non-h
non-h>ip
non-h>ip
non-h>ip
non-h>op
non-h>op
non-h>op
N/A
non-h>ip
non-h>ip
N/A
non-h>op
non-h>op
N/A
N/A
ip - inpatient, op - outpatient, non-h - non-hospital
> indicates dominance e.g. op>ip indicates outpatient services are more concentrated on the poor than inpatient.
Blank indicates that the null of non-dominance is not rejected at 5% significance level. N/A - not available.
a. Test is between all hospital care (inpatient and outpatient) and all non-hospital care.
“Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and
Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity
Cross-country differences in the distribution
of the public health subsidy in Asia
“Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and
Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity
Summary of findings from
Equitap BIA
•
•
•
•
•
Subsidy strongly pro-poor in Hong Kong
– Universal system with modest user charges and
exemptions for poor
– Private sector alternative allows better-off to opt out
Among low/middle income countries, subsidy is slightly
pro-poor in Malaysia & Thailand, neutral in Sri Lanka,
slightly pro-rich in Vietnam and very pro-rich elsewhere.
Pro-rich bias stronger for inpatient than outpatient
hospital care.
Non-hospital care is usually pro-poor.
But greatest share of subsidy goes to hospital care and this
dominates distribution of total subsidy.
“Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and
Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity
Is this good news or bad news?
• Findings strengthen evidence base showing health
subsidies are not pro-poor in developing
countries.
• If aim is to ensure poor get most of public health
services, then failing.
• But Malaysia, Thailand and Sri Lanka are
exceptions.
• If is part of wider policy to reduce relative
differences in living standards, then succeeding.
“Analyzing Health Equity Using Household Survey Data” Owen O’Donnell, Eddy van Doorslaer, Adam Wagstaff and
Magnus Lindelow, The World Bank, Washington DC, 2008, www.worldbank.org/analyzinghealthequity