This document summarizes a study examining the extent to which out-of-pocket health expenditures induce poverty among urban households in India. The study uses data from 1995-1996 and 2004 to measure pre-payment and post-payment poverty headcounts and gaps. Key findings include that adjusting for health spending increases poverty rates and that vulnerability varies significantly across states, religious groups, and consumption levels. The methodology and findings are discussed in detail, with comments raising questions about certain analytical choices.

1. Public Retreat, Private Expenses and Penury
- A Study of Illness Induced Impoverishment
in Urban India
Samik Chowdhury
Institute of Economic Growth, India
Discussant: KK Tang
University of Queensland
1

2. Colour Scheme
• Material drawn from the paper is in black.
• Comments and questions are in blue.
2

3. Objective
• Research question: Does spending on health care push urban Indian
households into poverty and, if yes, to what extent?
• Empirically examine the degree and intensity of health expenditure
induced poverty in urban India
• If public health provisions or subsidies are insufficient, out-of-pocket
health spending could be large
• When hit by negative health shocks, HHs may need to sell assets,
borrow, receive transfer payment from friends and relatives, or to
reduce consumption on non-health items
• If non-health consumption falls below a poverty line, HHs are
deemed to be in health expenditure induced poverty
3

4. Motivation
• Why is health expenditure induced poverty an important
issue?
• There is ample evidence from developing countries that
health spending is a major cause of poverty
• “Medical poverty trap” (Wihtehead et al., Lancet 2001) or
“poverty ratchet” (Chamber, 1983)
• It is important to evaluating the performance of health and
welfare systems
• Most health consumption is considered a necessity, so is
certain certain non-health consumption (e.g. 2100 calories
per day food intake)
• So an implicit proposition of the paper: health and welfare
systems should not force people to trade-off between
these two types of necessity
4

5. Indian Context
• This topic is particularly relevant for urban
India
• Public provision of health care services and
subsides in India has declined over the years
• Currently India’s health spending is equal to
4% of GDP
• HHs account for 70% of total health spending,
almost all in the form of out-of-pocket
expenses
5

6. Methodology
• Start with the methodology first proposed by
Wagestaff & van Doorslaer (Health Economics
2003)
6

7. Consumption expenditure
intensity
weighted poverty
Cumulative proportion of population in
state S ranked by consumption expenditure
Poverty line (LS)
C
Head count poverty
a
Indian state (S)
specific

8. Consumption expenditure
Poverty line (LS)
Cpre = H + N
(health) (non-health)
a
pre-payment
consumption
Head count poverty (pre)
Cumulative proportion of population in
state S ranked by consumption expenditure

9. Consumption expenditure
Poverty line (LS)
Cpre = H + N
(health) (non-health)
(post)
Cpost = N
intensity weighted
poverty = a + b + c
a
b
c
Head count poverty (pre)
post-payment
consumption
Cumulative proportion of population in
state S ranked by consumption expenditure

10. Consumption expenditure
minimal non-health
consumption
pre = HS + NS
LS
Cpre = H + N
(health) (non-health)
(post)
Cpost = N
Head count poverty (pre)
minimal
health
consumption
Cumulative proportion of population in
state S ranked by consumption expenditure

11. Consumption expenditure
Cpre = H + N
Head count poverty (pre)
pre-payment
poverty line
pre = HS + NS
LS
post-payment
poverty line
(health) (non-health)
Cpost = N
LS
post = NS
(post)
Cumulative proportion of population in
state S ranked by consumption expenditure

12. Consumption expenditure
Cpre = H + N
(health) (non-health)
(post)
Cpost = N
Pre-payment poverty = a = d + e
Post-payment poverty = e + f + g
Head count poverty (pre)
pre = HS + NS
LS
LS
post = NS
e f
g
d
Cumulative proportion of population in
state S ranked by consumption expenditure

13. Methodology of Wagestaff & van
Doorslaer (2003)
19
• For HH i residing in state S:
Ci
pre = Hi + Ni LS
pre = HS + NS
Ci
post = Ni LS
post = NS
• If Ci
pre < LS
pre => it is in pre-payment poverty
• If Ci
post < LS
post => it is in post-payment poverty

14. Methodology
• Pre-payment poverty => post-payment poverty
• Health consumption is at the minimal level or larger
• Hi ≥ HS
• No pre-payment poverty => post-payment poverty
• non-health consumption is small but health consumption is
large
• Ni < NS but Hi > HS
• Pre-payment poverty => no post-payment poverty
• non-health consumption is large but health consumption is
small
• Ni > NS but Hi < HS
20

15. Methodology of the Current Paper
22
• Pre-payment poverty is defined differently:
• Pre-payment poverty if Ci
pre < LS
post
post < LS
• Post-payment poverty if Ci
post
post ≤ Ci
• Because Ci
pre, post-payment poverty is
bounded to be at least as severe as pre-payment
poverty (and very likely to be higher)
• Make the analysis less interesting?
• What are the rationales of using LS
post instead of
LS
pre in measuring pre-payment poverty?

16. Poverty Indicators
• Poverty head count: in or out of poverty
• Pi = 1 if Ci < LS; = 0 otherwise
• Poverty gap (current Rs): head count adjusted for absolute
deviation from the poverty line
• Gi = Pi (LS – Ci)
• Normalized poverty gap: head count adjusted for %
deviation from the poverty line
• NGi = Pi (LS – Ci)/LS
• Useful for cross-state and intertemporal comparison, because of
variation in living cost across states and over time
• Each indicator is applied to pre- & post-payment data,
respectively
• Population-wide indicators are HH size weighted averages
23

17. Data
• National Social Survey Office (NSSO) unit record
data on morbidity and health care.
• Health expenditure: out-of-pocket medical
expenses
– Does it include health insurance spending?
• Year coverage: 1995-96 and 2004
• Distinction between 15 states (out of 29)
• Distinction between social groups by: religion,
caste, gender, employment, consumption
24

18. Table 1: A Summary of the data source
1995-96 2004
Number of urban households surveyed 49658 26566
% reporting ailment in last 15 days 5.4 9.9
% of persons hospitalised during a year 2 3.1
% of non-hospitalised treatment from government sources 20 19
% of hospitalisation treatment from government sources 43.1 38.2
Average expenditure (Rs.) per non-hospitalised treatment 175 306
Average expenditure (Rs.) per hospitalisation 3921 8851
25
• Morbidity increased
• Public provision reduced
• Nominal OOP health spending increased
• Useful to stratify by states and social groups to see if there
are differences amongst them

19. Poverty Line adjustment
• The adjustment of the poverty line depends on
the value of HS (minimal health spending)
• How to obtain the value for HS?
• HHs in each state are grouped by consumption
into deciles
• One of the decile contains the unadjusted
poverty line (LS
pre)
• The average health spending of that decile is used
as HS
26

20. Poverty Line adjustment
• Nation-wise taking out the minimal health
spending (Hs) lower the poverty line by about
8% in both periods
• Large variation in the adjustment across states
and over time, ranging 0.8-10% in 1995/96
and 4.4-21.4% in 2004
27

21. Table 3: Poverty Head Count (%) (selected states)
1995-96 2004
Pre-payment
Post-payment
Difference
Pre-payment
Post-payment
Difference
Assam 7.42 9.39 1.97 1.85 6.34 4.49
Haryana 12.01 17.62 5.61 9.24 14.78 5.55
Karnataka 39.52 41.56 2.04 33.1 37.05 3.95
Kerala 26.33 30.97 4.64 11.09 22.67 11.58
All India 23.52 27.01 3.49 19.35 25.47 6.12
• Post-payment poverty head count increases in all
15 states in both periods as expected due to the
methodology
• Nation-wise health spending increases poverty
head count by 1.14 times in 1995-94 and 1.3
times in 2004
29

22. Table 3: Normalized Poverty Gap (%) (selected states)
1995-96 2004
Pre-payment
Post-payment
Difference
Pre-payment
Post-payment
Difference
Assam 1.2 3.4 2.3 0.1 3.7 3.5
Haryana 2.3 903 7 1.3 5.5 4.3
Karnataka 10.1 11.9 1.8 7.5 10.2 2.7
Kerala 5.2 7.6 2.4 2.2 11.9 9.7
All India 4.9 7.7 2.7 4.4 10 5.6
• Nation-wise health spending increases
normalized poverty gap by 1.57 times in 1995-
94 and 2.27 times in 2004
• Large variation across states
30

23. Comment
• Kerala has the largest increase in poverty head
count and normalized poverty gap after adjusting
for health spending in 2004
• Imply that health spending is a very important
factor in determining poverty in Kerala
• But at the same time it has the highest life
expectancy (and education attainment) amongst
all states
• Does it suggest substitution between health &
non-health poverty?
31

24. Table 4: 2004 results by Religious Group
Poverty Head Count Poverty Gap (Rs)
Pre-payment
Post-payment
Difference
Pre-payment
Post-payment
Difference
Hindu 17.8 23.5 2.7 20 45.7 25.7
Muslim 28.7 37.3 8.6 31.7 64.5 32.8
Christian 10.4 14.7 4.3 8.6 45 36.4
Others 17 22.6 5.6 23.5 58.6 35.1
32
• No 1995-96 data
• Muslim is most vulnerable with/out adjusting for
health spending
• Relatively lesser Christians were pushed into poverty,
but those affected had their spending on non-health
items being reduced by the largest margin

25. Nation-wise Post-payment Poverty
Line
• What is the population (sample) share of each
religious/social group?
• A nation-wise post-payment poverty line
(LIND
post) instead of group-specific Lpost is used
to measure poverty for all social groups
• Given we know in which state each HH is
living, can we measure each HH’s poverty
status using LS
post before aggregating them up
based on religious/social grouping?
33

26. Table 4: Poverty Gap (current Rs) by Consumption
Quintiles
1995-96 2004
Pre-payment
Post-payment
Difference
Pre-payment
Post-payment
Difference
Poorest 74.8 84.6 9.9 85.1 121.1 36
Poor 4.4 16.5 12.1 2.2 28.5 26.3
Middle 0 6.1 6.1 0 22.3 22.3
Rich 0 4.6 4.6 0 13.4 13.4
Richest 0 10.8 10.8 0 40.5 40.5
34
• For the richest to be in post-payment poverty, it
requires Ni < NS
• They could not use savings/borrow/transfers to cover a
poverty gap of Rs 10.8-40.5 on average (less than $1)
• What is the consumption level of each group?

27. Comment: Concept
• HHs may scarify some health consumption (e.g.
for chronic but non-acute illness) and thus their
long-term health to meet the immediate need for
non-health consumption (e.g. food and shelter)
• This is more like to be the case for the poorest as
they are lack of savings and borrowing capacity
• Then even post-payment poverty measures
understate the true impacts of poverty on them
36

28. Comment: Measurement
• A lot of figures (treatment costs and poverty gap)
in the paper are expressed in current Rs
• Normalized poverty gap results are not always
provided
• Because of high inflation, India's CPI nearly
doubled over 1995-2004.
• Nominal changes overstated the real changes
• Need to adjust for inflation
• Any need to consider variation in the inflation
rate across states?
37

29. Comment: Application
• Wagestaff & van Doorslaer (2003) also look at
the relative contributions of hospital and non-hospital
costs to health expenditure induced
poverty
• It is worth to do the same for the current
paper
38

30. Minor Comments
• The finding that renters and pensioners are
more affected by health spending than other
HH groups (Table 5) may be due to the fact
that they are of relatively older ages and
require more health care.
• Appendix shows data for 20 states, but the
text show results for 15 states (?!)
39

31. Conclusion
• Objective: To examine how health spending
affect urban Indian’s capacity to maintain a
minimal amount of non-health consumption
• Policy implication: The health and welfare
system should not force people to trade-off
health and non-health necessities
• Motivation: An important issue in India as
HHs bear 70% of total health spending
40

32. Conclusion
• Finding:
– Adjusting for health consumption has significant
implication to poverty status of urban Indian
– There is large variation in the adjustment and
consequential poverty measure outcomes across
states and across various religious and social
groups
41