Post on 17-Dec-2015
Measuring inequalities in health
Adam WagstaffAbdo Yazbeck
Today’s menu
Concentration curves and indices (AW) Combining levels and inequalities into a
single achievement index (AY) Benefit incidence analysis (AY) Inequalities in financial burden of health
care payments (AW)
Concentration curves and indices
Adam Wagstaff
Which country is less equal?
0
50
100
150
200
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300
India Mali
U5M
R p
er
1000 liv
e b
irth
s
Poorest"quintile"2nd poorest"quintileMiddle "quintile"
2nd richest"quintile"Richest"quintile"
0%
25%
50%
75%
100%
0% 25% 50% 75% 100%
Cum. % births, ranked by "wealth"
Cum
. %
of
und
er-fi
ve d
eath
s L(s) India
L(s) Mali
CI = 2 x area between 450 line and concentration curve
CI < 0 when variableis higher amongst poor
U5MR concentration curves
Setting data up for CC chart
Equality L(s) India L(s) Mali0% 0% 0%
23% 23% 30%45% 45% 59%66% 66% 79%85% 85% 93%
100% 100% 100%0% 0%
21% 25%42% 49%63% 69%83% 89%
100% 100%
Computing CI: grouped data
Wealth No. of rel % cumul % U5MR No. of rel % cumul % Conc.group births births births per 1000 deaths deaths deaths Index
0% 0%Poorest 29939 23% 23% 154.7 4632 30% 30% - 0.00082nd 28776 22% 45% 152.9 4400 29% 59% - 0.0267Middle 26528 20% 66% 119.5 3170 21% 79% - 0.05924th 24689 19% 85% 86.9 2145 14% 93% - 0.0827Richest 19739 15% 100% 54.3 1072 7% 100% 0.0000Total/average 129671 118.8 15419 - 0.1694
)(...)()( 1123321221 TTTT LpLpLpLpLpLpC
Computing CI: micro-data
Can use where variable of interest (y) defined and measured at individual level—not case with U5MR
Use “convenient covariance” result Compute: mean of y—call it Generate individual’s fractional rank in SES
distribution—call it R Then compute CI = 2 cov(y,R) / If data are weighted,
– generate a weighted fractional frank, and – compute a weighted covariance
Computing std errors for CIs
Grouped data case:– Are variances of group means known? If
they are, can get a more precise estimate– Use formulae in TN #7—compute in Excel;
spreadsheet available from Bank team Micro-data case
– Estimate in regression below using Newey-West estimator in Stata: equals CI; std error is robust std error of CI
iii
R uRy
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Health care payments
Adam Wagstaff
Different concerns over health care payments Health care payments affect HHs’ ability
to purchase other things that matter to their well being—food, shelter, etc.
But what’s an equitable distribution?– One where payments don’t absorb more
than x% of income—i.e. aren’t catastrophic– One where payments don’t push HHs into
poverty or further into poverty if already there?
– Or one where payments are proportional to ability to pay?
Rural China—payments relative to income
Out-of-pocket payments Rural Hebei and Liaoning, 1995-97
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10000
0 200 400 600 800
Households ranked by income (N=790)
Annual pre
- and p
ost
-OO
P
consu
mpti
on (
Yuan)
Out-of-pocket payments Rural Hebei and Liaoning, 1995-97
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
0 200 400 600 800
Households ranked by income (N=790)
Annual pre
- and p
ost
-OO
P
consu
mpti
on (
Yuan)
85% of prepayment income
Rural China—payments relative to 15% threshold
Rural China—payments relative to poverty line
Out-of-pocket payments Rural Hebei and Liaoning, 1995-97
0
1000
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7000
8000
9000
10000
0 200 400 600 800
Households ranked by income (N=790)
Annual pre
- and p
ost
-OO
P
consu
mpti
on (
Yuan)
Poverty line
How much catastrophe?
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
110%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
cumul % pop
OO
Ps
as
% s
pendin
g
All spending
Food spending
Vietnam case study
18% of Vietnamese population in 1993 had out-of-pocket expenditures in excess of 25% of non-food consumption
How much catastrophe?
1. Incidence of catastrophic costs can be measured as proportion (headcount) exceeding threshold level zcat : Hcat
2. Intensity of catastrophic costs can be measured as the average excess (or gap) : Gcat
3. If , in addition, we want to take into account that the incidence of catastrophic costs matters more for the poor, we can use the rank-weighted intensity, defined as
where CO is the concentration index of the “overshoot” spending.Clearly, if excesses concentrated amongst the poor, CO
will be negative and
1-Ocat cat OW G C
Ocat catW G
Catastrophe in Vietnam
threshold level z
Out-of-pocket health expenditure 5% 10% 15% 25%
as % of total expenditure per cap
Headcount (H) 38.19% 18.40% 9.26% -
Mean gap (G) 2.85% 1.51% 0.84% -
Mean positive gap (MPG) 7.47% 8.21% 9.06% -
as % of non-food expenditure per cap
Headcount (H) 67.17% 46.52% 33.25% 17.88%
Mean gap (G) 9.95% 7.14% 5.17% 2.70%
Mean positive gap (MPG) 14.81% 15.36% 15.55% 15.11%
How much poverty impact?
Cum % sample
Poverty line
Pre-payment income
Income
A = pre-payment poverty gap
Pre-payment headcount
How much poverty impact?
Cum % sample
Poverty line
Pre-payment income
Income
Post-payment income
A = pre-payment poverty gap
Pre-payment headcount
Post-payment headcount
C
Bdeepening poverty of pre-payment poor
addition to poverty gap from the new poor
0
1
2
3
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9
10
1 500 999 1498 1997 2496 2995 3494 3993 4492 4991 5490 5989
Households ranked by expend w/out hc payments
HH
exp
endi
ture
as
mul
tiple
of
PL
Pov line = VND 1.8m/year Expend w/out hc payments
0
1
2
3
4
5
6
7
8
9
10
1 500 999 1498 1997 2496 2995 3494 3993 4492 4991 5490 5989
Households ranked by expend w/out hc payments
HH
exp
endi
ture
as
mul
tiple
of
PL
Pov line = VND 1.8m/year Expend w/out hc paymentsHC payments
Out-of-pocket payments for health care pushed 2.6m Vietnamese into poverty in 1998.
Increased headcount by 23% and poverty gap by 25%
Impoverishment in Vietnam
How progressive?
Regressive: OOPs larger (as a % of income) at lower income levels; less inequality in OOPs than in pre-payment income; Cf. progressive
0%
25%
50%
75%
100%
0% 25% 50% 75% 100%cumul % sample, ranked by income
cum
ulat
ive
% in
com
e an
d O
OPs
Equality
Lorenz curve, pre-payment income
OOPs concentration curve
Lorenz curve shows income inequality; concentration curve shows OOPs inequality
Gini is twice area between Lorenz curve & 450 line; concentration index is twice area between CC and 450 line
Kakwani index is twice area between CC and Lorenz curve, or ; positive when progressive
How regressive are OOPs?
-0.50
-0.40
-0.30
-0.20
-0.10
0.00
0.10
Chin
a
Peru
Bulg
ari
a
Ghana
Vie
tnam
Bangla
desh
Moro
cco
Cote
d'I
voir
e
Egyp
t
Zam
biaK
akw
ani pro
gre
ssiv
ity index
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Sources: Wagstaff, van Doorslaer, et al. (1998), authors’ calculationsSources: Wagstaff, van Doorslaer, et al. (1998), authors’ calculations