Health Financing in South Asia

Health System Objective & Socioeconomic Overview: Achievements & Challenges

South Asia Regional Forum on Health Financing

Maldives, June 2-4, 2010

Pablo GottretGeorge SchieberWorld BankSouth Asia Region

Key Messages

1. Underlying demographics, epidemiology, and economic situations determine underlying ‘needs’ and ability to meet those needs

2. Reforms must be tailored to individual country socioeconomic, political, and geographic circumstances – no magic bullets or one size fits all solutions exist

3. Governments should design policies consonant with the economic principles of equity, efficiency, affordability and sustainability, which underlie the revenue collection, risk pooling, and purchasing functions of health financing

4. Policy instruments must line up with the objectives to be achieved, but policy reforms are often more politically than technically driven

5. Financing reforms must also be accommodated within a country’s current and future ‘fiscal space’

6. Financing changes must be coordinated with all other health systems reform efforts

Outline of Presentation

Pablo’s Presentation• SAs Underlying Socioeconomic Situation• SAs Current Health Financing Baseline George’s Presentation• Health Financing Functions, Objectives, and Models

– Revenue collection and fiscal space• Revenue collection and tax policy• Fiscal space and macroeconomic management

– Risk pooling– Purchasing

• Global Experiences• Key Health Financing Decision Parameters

SAR growth performance has been strong. Recent performance continues

to be close to East Asian growth…GDP growth (annual %)

Source: World Development Indicators, World Bank, 2010

-2.00

0.00

2.00

4.00

6.00

8.00

10.00

12.00

1980 1985 1990 1995 2000 2005 2007

East Asia & Pacific Europe & Central Asia Latin America & Caribbean

Middle East & North Africa South Asia Sub-Saharan Africa

Labor Demand

All Countries have experienced Real GDP Growth but Somewhat Tempered by High Population Growth

2510

025

010

0050

0025

000

GD

P p

er c

apita

, con

stan

t 200

0 U

S$

1955 1965 1975 1985 1995 2005Year

Afghanistan Bangladesh Bhutan

India Sri Lanka Maldives

Nepal Pakistan

Source: WDINote: y-scale logged

Real GDP per capita, 1960-2008

But growth has not been inclusive enough to reduce the number of poor

Poverty rates have declined, but not fast enough to reduce the number of the poor…

South

Asia

East

Asia

Sub-

Sahara

n Africa

200

400

600

800

1000

1981 1993 2005

No. of poor people in millions

Source: World Development Indicators, World Bank 2009.

Thus poverty, despite decline, remains a significant problem

Source: WB SA Strategy Update

Large part of the work remains in the agricultural sector

0

10

20

30

40

50

60

70

80

90

100

90s 00s 90s 00s 90s 00s 90s 00s 90s 00s 90s 00s 90s 00s

Nepal India Bangladesh Bhutan Pakistan Sri Lanka Maldives

Distribution of Workforce by Sector

Agriculture Industry Services

Source: WDI and GDF databases; Bosworth, Maertens, 2008

And therefore SA is Largely Rural(Percent of Population Living in Rural Areas, 2008)

0

10

20

30

40

50

60

70

80

90

Source: WDI

Labor Market Informality

Country % of Total Employmentthat is Informal

Afghanistan N/A

Bangladesh 79%

Bhutan N/A

India 92%

Maldives N/A

Nepal 80%

Pakistan 70% *

Sri Lanka 66%

Source: Marty Chen and Donna Doane. “Informality in South Asia: A Review.” 2008.

* World Bank. http://go.worldbank.org/MYR3I96L80

There is increasing evidence of important migration flows both within and across countries

Source: UNDP Human Development Report, 2009; IBRD 37115, October 2009. NSS 55th round from “South Asia’s Poorest Half Billion” (Ghani, 2009).

0.0

2.0

4.0

6.0

8.0

10.0

12.0

0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0

Emig

rati

on

Rat

e,

20

00

-20

02

Immigrants as % of Population, 2005

Afghanistan

Bangladesh

SSA BhutanIndia

Maldives

Arab States

Central & Eastern Europe & CIS

LACSri Lanka

Nepal

India

PakistanSAREAP

International movements in South Asia are low by regional standards, but is significant for particular countries.

In big countries, internal mobility between lagging and leading regions is also important.

South Asia has the highest incidence of conflict in the world

Global conflict & unrest Terrorist Incidents and Fatalities per Million Population

in Leading and Lagging Regions 1998-2007

0.00

0.50

1.00

1.50

2.00

2.50

0.00

0.25

0.50

0.75

1.00

1.25

1.50

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Incidents (leading) Incidents (lagging)

Fatalities (leading) Fatalities (lagging)

South Asia, MIPT

Source: Economist Intelligence Unit. Note: A lower score indicates a more peaceful country. Number of incidents per million population is on the left vertical axis,

and the number of fatalities per million population is on the right vertical axis.Source: MIPT data was collected by the National Memorial Institute for the

Prevention of Terrorism (MIPT), in collaboration with the National Counter Terrorism Center (NCTC).

1 1.5 2 2.5

EAP

ECA

LAC

MENA

Africa

SAR

Index 1 to 5.

Between 2004-07, five of eight SAR countries were in the top ten contributors to direct conflict deaths worldwide. Three of the eight are in the top ten in terms of death rates from direct conflict.

SA Economic and Demographic Snapshot

Economic Indicators Health Outcomes

Per Capita

GDP (current

US$), 2008

Average GDP

growth per

capita, 2008

Under-five

mortality rate

(deaths per

1000 births),

2008

Life expectancy at

birth, 2008

Annual

population

growth rate (%),

2008

Infant Mortality,

2008

Afghanistan 366 -0.4 257 44 2.7 165

Bangladesh 497 4.7 54 66 1.4 43

Bhutan 1869 12 81 66 1.6 54

India 1017 4.7 69 64 1.3 52

Maldives 4135 3.7 28 72 1.4 24

Nepal 438 3.4 51 67 1.8 41

Pakistan 991 -0.16 89 67 2.1 72

Sri Lanka 2013 5.2 15 74 0.73 13

South Asia 951 4.1 76 64 1.5 58

Source: WHO & WDI

A rapidly growing labor force increases the pressure to create more jobs

Source: U.S. Census Bureau Population Projections, 2010.

-100 -80 -60 -40 -20 0 20 40 60 80 100

0-4

7-14

20-24

30-34

40-44

50-54

60-64

70-74

80-84

90-94

100+

Population (Million)

Age

Gro

up

(ye

ars)

The Changing Population Pyramid in South Asia2000-2025-2050

Male Female200020252050

Changing BOD will Pose a Serious SA Challenge

40.3

13.2

46.5

Comunicable diseases Injuries

Non-Communicable dieases

2008

19.1

14.5

66.4

2030

Source: WHONote: Disease burden measured as % of standard DALYs(3% discounting, age weights) - baseline scenario

Burden of Disease in South Asia, Projected 2008 & 2030

Source: World Bank

0%

10%

20%

30%

40%

50%

60%

70%

80%

SA

R

Afg

ha

nis

tan

Ba

ng

lad

esh

Bh

uta

n

Ind

ia

Ma

ldiv

es

Ne

pa

l

Pa

kist

an

Sri

La

nka

Change in total pop. + aging factor Change in total pop.

Aging Will Affect Total Health Expenditures in SA Countries(increase in health spending between 2020 and 2000 due only to changing population composition)

Creating Fiscal Space…Domestic resource mobilization, public expenditure restructuring including subsidies,

and efficiency and effectiveness of government expenditures will be key...

FISCAL DEFICITS

Source: Global Economic Prospects and Selected country authorities

-10

-8

-6

-4

-2

0

2

4

1996-2000 2001-2005 2005-2008 2009-2011

Post Crisis

Projection

In percent of GDP

SAR EAP LAC ECA

22

SAs Health Financing Baseline

Health Financing Reforms are One Important Aspect of the Broader Health System Reform Agenda

Efficiency

Source: Modified from World Bank, WHO 2008

Health System Functions and Goals

Purchasing

Benefits

Health system

goals

Equity in finance

(Intermediate) objectives

of health finance policy

Revenue

collection

Health system functions

Pooling

Financial

protection

Health gain

Equity in utilization

and resource

distribution

Quality

Service delivery

Equity in health

ResponsivenessTransparency and

accountability

Efficiency

Health financing

system

Ste

wa

rds

hip

Resource

generation

Source: Kutzin et al., Implementing Health Financing Reform: Lessons from Countries in Transition, WHO, forthcoming.

SA Health Financing BaselineHealth Expenditures, 2008 (country weighted)

Country or IncomeClass GDP per capita,

current US$

Health expenditures per

capita, current US$

Total health spending (%

GDP)

Public health spending (% Total health spending)

Out-of-pocket health spending

(% Health spending)

Total revenue (% GDP)

Social security health spending (%

public health spending)

External health resources (%

health spending)

Afghanistan 366 48 7.3 21 78 7.6 0 17

Bangladesh 497 17 3.5 36 63 11 0 5.5

Bhutan 1869 75 3.9 80 20 24 0 30

India 1017 43 4 28 64 15 16 1.7

Maldives 4135 462 11 70 22 44 3.9 1.2

Nepal 438 20 4.9 39 55 12 0 20

Pakistan 991 24 2.9 30 58 13 4.4 4.1

Sri Lanka 2013 81 4 43 49 14 0.14 0.75

Low-income countries 585 33 5.8 42 46 15 5.3 25

Lower middle-income countries 2459 142 6.1 57 37 28 16 10

Upper middle-income countries 7777 503 6.5 61 29 30 32 2

High-income countries 38191 3386 8 72 21 34 37 0.088

Source: WDI & WHO

Health Spending has not Increased Much Since 1995 in a Number of Countries

Public Health to GDP Ratio Total Health to GDP Ratio

Maldives

Afghanistan

Bhutan

Nepal

India

Sri Lanka

Pakistan

Bangladesh

2

4

6

8

10

To

tal he

alth s

pe

nd

ing a

s s

ha

re o

f G

DP

(%

)

1994 1996 1998 2000 2002 2004 2006 2008Year

Source: WDI, WHONote: y-axis log scale

Total Health Spending Share of GDP (%) in Selected Comparators (1995-2008)

Maldives

Bhutan

Afghanistan

Sri Lanka

Nepal

India

Bangladesh

Pakistan

.25

.75

1.25

1.75

2.25

3.25

4.25

5.256.257.25

Pu

blic

he

alth s

pe

nd

ing a

s s

ha

re o

f G

DP

(%

)

1994 1996 1998 2000 2002 2004 2006 2008Year

Source: WDI, WHONote: y-axis log scale

Public Health Spending Share of GDP (%) in Selected Comparators (1995-2008)

Total Spending in Most SA Countries is Below Income Comparators

Share of GDP

Pakistan

Bangladesh

Nepal

Bhutan

IndiaSri Lanka

Afghanistan

Maldives

0

5

10

15

20

To

tal he

alth s

pe

nd

ing (

% o

f G

DP

)

100 250 1000 2500 10000 25000GDP per capita (current US$)

Sources: WDI; WHONote: x-axis log scale

Total Health Expenditure as Share of GDP versus Income Per Capita (2008)

Per Capita

Pakistan

Bangladesh

AfghanistanBhutan

Nepal

India

Maldives

Sri Lanka

5

2030

50

100

250

1000

5000

To

tal he

alth s

pe

nd

ing p

er

cap

ita

(cu

rren

t U

S$)

100 250 1000 2500 10000 25000GDP per capita (current US$)

Sources: WDI; WHONote: Both axis log scale

Total Health Expenditure per Capita versus Income per Capita in Current US$ (2008)

As is the Case for Government Health Spending

Share of GDP

PakistanBangladeshAfghanistan

NepalBhutan

Maldives

Sri LankaIndia

5

10

15

Pu

blic

he

alth s

pe

nd

ing (

% o

f G

DP

)

100 250 1000 10000 25000GDP per capita (current US$)

Sources: WDI; WHONote: x-axis log scale

Public Health Expenditure as Share of GDP versus Income per Capita (2008)

Per Capita

Bhutan

India

NepalPakistan

Sri Lanka

Afghanistan

Bangladesh

Maldives

5

50

100

250

1000

5000

Pu

blic

he

alth s

pe

nd

ing p

er

cap

ita

(cu

rren

t U

S$)

100 250 1000 10000 25000GDP per capita (current US$)

Sources: WDI; WHONote: Both axis log scale

Public Health Expenditure per Capita versus Income per Capita in Current US$ (2008)

Government Health Spending is also Low as a Share of the Total and the Government Budget

Share of Total Share of Budget

BhutanNepal

Maldives

Sri Lanka

PakistanAfghanistan

IndiaBangladesh

0

10

20

30

Pu

blic

he

alth s

pe

nd

ing

(% tota

l g

overn

me

nt b

ud

ge

t)

100 250 1000 5000 25000GDP per capita (current US$)

Sources: WDI; WHONOTE: x-axis log scale

Public Health Expenditure as Share of Total Government Expenditureversus Income per Capita (2008)

Pakistan

Bangladesh

Bhutan

Nepal

Afghanistan

India

Maldives

Sri Lanka

0

20

40

60

80

100

Pu

blic

he

alth s

pe

nd

ing

(% tota

l h

ea

lth

sp

en

din

g)

100 250 1000 2500 10000 25000GDP per capita (current US$)

Sources: WDI; WHONote: x-axis log scale

Public Health Expenditure as Share of Total Health Expenditure versus Income per Capita (2008)

Private Spending is High

Share of Total Per Capita

Bhutan

Maldives

Pakistan

Bangladesh

India

Sri LankaNepal

Afghanistan

0

20

40

60

80

Pri

va

te h

ea

lth

sp

en

din

g(%

tota

l h

ea

lth

sp

en

din

g)

100 250 1000 4000 10000 25000GDP per capita (current US$)

Sources: WDI; WHONote: x-axis log scale

Private Spending as Share of Total Health Spending versus Income per Capita (2008)

Maldives

Bangladesh

Bhutan

Sri LankaAfghanistan

Nepal Pakistan

India

5

50

100

250

1000

5000

Pri

va

te h

ea

lth

sp

en

din

g p

er

ca

pita (

curr

ent U

S$

)

100 250 1000 5000 10000 25000GDP per capita (current US$)

Sources: WDI; WHONote: Both axis log scale

Private Health Expenditure per Capita versus Income per Capita in Current US$ (2008)

As is Out-of-Pocket Spending

Share of Total Per Capita

Afghanistan

India

Pakistan

Bhutan

Nepal Sri Lanka

Bangladesh

Maldives

0

20

40

60

80

Out-

of-

po

cket h

ea

lth

sp

en

din

g

(% tota

l h

ea

lth

sp

en

din

g)

100 250 1000 5000 10000 25000GDP per capita (current US$)

Sources: WDI; WHONote: x-axis log scale

Out-of-Pocket Spending as Share of Total Health Spending versus Income per Capita (2008)

AfghanistanIndia

Sri LankaMaldives

NepalBhutan

Bangladesh

Pakistan

5

50

100

250

1000

5000

Out-

of-

po

cket h

ea

lth

sp

en

din

g p

er

ca

pita

(curr

ent U

S$

)

100 250 1000 3500 10000 25000GDP per capita (current US$)

Sources: WDI; WHONote: Both axis log scale

Out-of-Pocket Health Expenditure per Capita versus Income per CapitaCurrent US$ (2008)

Higher Public Spending Means Lower OOP

Pakistan

AfghanistanNepal

Bhutan

India

BangladeshSri Lanka Maldives

10

30

70100

Out

-of-

pock

et e

xpen

ditu

res

on

hea

lth

(% o

f to

tal h

eal

th e

xpen

ditu

res)

.5 1 3 5 7 9 13Public health spending (% of GDP)

Sources: WDI; WHONote: both axes log scale

Out-of-pocket Health Expenditures as Shareof Total versus Public Health Expenditures (% GDP), 2008

The Numbers of Doctors and Hospital Beds have also Increased Over Time

Doctors Beds

Maldives

Nepal

Bhutan

Afghanistan

Bangladesh

Sri LankaIndia

Pakistan

.02

.05

.5

.1

1

1.5

2

Ph

ysic

ians p

er

1,0

00

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010Year

Source: WDI, WHONote: y-axis log scale

Physician to Population Ratio in Selected Comparators (1960-Most Recent Available Year)

Sri Lanka

Nepal

Maldives

Bhutan

India

Pakistan

AfghanistanBangladesh

0

1

2

3

4

Ho

sp

ita

l b

ed

s p

er

1,0

00

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010Year

Source: WDI

Hospital beds to Population Ratio in Selected Comparators (1960-Most Recent Available Year)

Expenditure Performance also Depends on Health Sector Inputs

Physicians

Maldives

Sri LankaIndia

Nepal

Pakistan

Bangladesh

Bhutan

.1.2

5

15

15

Phy

sici

ans

per 1

000

250 1000 5000 25000GDP per capita, US$

Source: World Development Indicators, WHO, Royal Monetary Authority, & RGOB Annual HealthBulletin, 2009Note: log scaleData are for latest available year

Doctor Supply versus Income, 2000 - 2008

Including Hospital Beds

India

Maldives

Nepal

Pakistan

Sri Lanka

Bhutan

Afghanistan Bangladesh

.1.2

5

15

15

Hos

pita

l bed

s pe

r 10

00

250 1000 5000 25000GDP per capita, US$

Source: World Development Indicators, WHO, Royal Monetary Authority, & RGOB Annual HealthBulletin, 2009Note: log scaleData are for latest available year

Hospital Bed Supply versus Income, 2000 - 2008

IMR and LE have Improved Over Time

Infant Mortality Life Expectancy

5

25

100

250

Infa

nt m

ort

alit

y (

pe

r 1,0

00

liv

e b

irth

s)

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010Year

Afghanistan Bangladesh Bhutan

India Sri Lanka Maldives

Nepal Pakistan

Source: WDINote: y-axis log scale

Infant Mortality Rates in Selected Comparators (1960-2008)

Afghanistan

BhutanNepal

Bangladesh

India

Maldives

Pakistan

Sri Lanka

30

40

50

60

70

80

Life

expe

cta

ncy (

yea

rs)

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010Year

Source: WDI, WHONote: y-axis log scale

Life Expectancy in Selected Comparators (1960-2008)

But Performance on Specific Health Outcomes is More Mixed in Terms of Efficiency

Skilled Birth Attendents

Afghanistan

Maldives

Nepal

Sri Lanka

Bhutan

Bangladesh

India

Pakistan

Wor

se th

an a

vera

geBe

tter t

han

aver

age

Better than averageWorse than average

Perfo

rman

ce re

lativ

e to

hea

lth s

pend

ing

per c

apita

Performance relative to income per capita

Source: World Development Indicators (2010), WHO (2010), & Royal Monetary Authority (2009)Note: both axes log scaleData are latest available year 2003-2008

Skilled Birth Attendance Relative to Income and Spending, 2003-2008

And Also Outcomes

Infant Mortality Maternal Mortality

Bangladesh

Pakistan

Bhutan

Afghanistan

India

Maldives

Nepal

Sri Lanka

Be

tter

than

avera

ge

Wors

e tha

n a

ve

rag

e

Worse than averageBetter than average

Pe

rfo

rman

ce r

ela

tive

to h

ealth

sp

en

din

g

Performance relative to income

Global Comparisons of Infant Mortalityversus Income and Total Health Spending, 2008

BangladeshPakistan

Sri Lanka

India

NepalBhutan

Afghanistan

Maldives

Be

tter

than

avera

ge

Wo

rse

tha

n a

ve

rag

e

Worse than averageBetter than average

Pe

rform

an

ce

rela

tive

to h

ea

lth

sp

en

din

g p

er

ca

pita

Performance relative to income per capita

Source: World Development Indicators, WHO, & Royal Monetary Authority, 2009Note: both axes log scale

Global Comparisons of Maternal Mortality Relative to Income and Spending, 2005

So money is not a sufficient condition for improved outcomes. The challenge is:

• Achieve universal coverage• Improve financial protection • Increase health system efficiency

In the context of:

• High out-of-pocket payments• Large number of poor and large informal sector• Fragmented financing systems• Limited revenue-raising capacity• Inefficient purchasing arrangements• Unpredictable/Insignificant donor aid• Decentralized environments & variable local

management capacitySource: Gottret and Schieber 2006