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Asia’s Dependency Transition: Intergenerational Transfers, Economic Growth, and Public Policy Optimal Age Profile of Pe...

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Asia’s Dependency Transition: Intergenerational Transfers, Economic Growth, and Public Policy

Optimal Age Profile of Per Capita Health Expenditure Ling Li Qiulin Chen Yu Jiang China Center for Economic Research November 1, 2007

Backgrounds Š 1. Health and health care have become

dominant economic and political issues worldwide. ]Rapid

increase in health care expenditure ]Increase share of personal income spent on health care ]Limited access to health care service ]Health Inequality

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Total Health Care Expenditure Share of GDP In Some Countries, 2003 15. 2

16 14 11. 5

11.1

12 9. 5

10 8 6

7. 3

6. 5

6. 2

5. 6

8. 4

8

7. 9

7. 6

10. 1

9. 9

4 2

US

d

y rl

an

an ze

Sw

it

an

rm Ge

li

ce

da Fr

Ca

na

a

y ra st Au

UK

al It

n pa

l Ja

zi

n

ba

ra Ba

Cu

ta

Af

gh

an

is

xi Me

Ch

in

a

co

0

Source: WHO,2006

Backgrounds Š 2. Health care burdens are increasing because of the Aging ] The

OECD’s most recent projections suggest that, in thirteen countries with available information, population aging will create a rise in age-related social expenditures from an average of under 19% of Gross Domestic product in 2000 to almost 26% of GDP by 2050, with old-age pension payments and expenditure on health care and long term care each responsible for approximately half of this increase (Dang, Antolin, & Oxley, 2001).

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Percent Elderly by Age: 2000 to 2030 Region

Year

EUROPE ………..………………………….

NORTH AMERICA ……...………………

OCEANIA ………..……...………………..

ASIA ………...……...………………………

LATIN AMERICA/CARIBBEAN …...

NEAR EAST/NORTH AFRICA ….….

SUB-SAHARAN AFRICA ………….…

65 years and older

75 years and over

80 years and older

2000

15.5

6.6

3.3

2030

24.3

11.8

7.1

2000

12.6

6.0

3.3

2030

20.3

9.4

5.4

2000

10.2

4.4

2.3

2030

16.3

7.5

4.4

2000

6.0

1.9

0.8

2030

12.0

4.6

2.2

2000

5.5

1.9

0.9

2030

11.6

4.6

2.4

2000

4.3

1.4

0.6

2030

8.1

2.8

1.3

2000

2.9

0.8

0.3

2030

3.7

1.3

0.6

Source: U.S. Census Bureau

Population, by Age and Sex: 1950 (In millions)

80+ 75-79 70-74 65-69 60-64 55-59 50-54 45-49 40-44 35-39 30-34 25-29 20-24 15-19 10-14 5- 9 0- 4 400

Developing countries Developed countries

Male

300

Female

200

100

0

100

200

300

400

Sources: United Nations 1999 and U.S. Census Bureau 2000.

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Population, by Age and Sex: 1990 (In millions)

80+ 75-79 70-74 65-69 60-64 55-59 50-54 45-49 40-44 35-39 30-34 25-29 20-24 15-19 10-14 5- 9 0- 4 400

Developing countries Developed countries

Male

300

Female

200

100

0

100

200

300

400

Sources: United Nations 1999 and U.S. Census Bureau 2000.

Population, by Age and Sex: 2030 (In millions) Developing countries Developed countries 80+ 75-79 70-74 65-69 60-64 55-59 50-54 45-49 40-44 35-39 30-34 25-29 20-24 15-19 10-14 5- 9 0- 4 400

Male

300

Female

200

100

0

100

200

300

400

Sources: United Nations 1999 and U.S. Census Bureau 2000.

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Seamus Hogan and Sarah Hogan, How Will the Ageing of thePopulation Affect Health Care Needs and Costs in the Foreseeable Future?, IHSPR D I S C U S S I O N PA P E R, 2002

Motivations THE

Š Demography ] Scale:

population ] Structure: Aging

= X

N

C

Š Health care Cost ] Scale:

Average cost ] Structure: Aging

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Motivations Š Many literatures have focused on the increasing total health care expenditure. e.g. Health Care Expenditure Projection ]

Cutler & Sheiner, 1998; Reese, 2000; Dang,Antolin, & Oxley, 2001; Miller,2001; Jonan J.Polder, Luc Bonneux, Willem Jan Meerding, Paul J.Van Der Maas, 2002; Monika Riedel and Maria M. Hofmarcher, 2003

Š R. Busse (2001): When examining factors responsible for health expenditure, the "usual suspects" are: ] ] ] ] ]

Demography/ageing Economic growth/rising GDP Health care resources (hospital beds, staff, high technology, etc.) New technologies and medical progress Health care system (especially Bismarck vs. Beveridge)

Š Alastair Gray (2005) reviews the evolution of research in Population Ageing and Health Care Expenditure

Motivations Š But more are focused on the demographical changing and the rising health expenditure of the elderly people. Š What’s more, The age profiles are assumed given. Š Fewer researches have paid attention to the age profile of per capita health expenditure itself.

>>> Š What is the optimal distribution of the age profile? Š What should and can we do something to change the structure?

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Stylized facts Š Health expenditures are strongly age dependent Š Average health expenditures by age group are relatively high for young children; they decrease and remain stable for most of the prime-age period, and then start to increase rapidly at older ages Health expenditure Š like a “J curve” ] ]

The high costs at birth The Cost-of-Dying

Š Literatures Š NTA Project Results Age

Health

14

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(“Aggregate studies of age and health expenditures”, http://www.pc.gov.au/study/ageing/finalreport/technicalpapers/technicalpaper05.pdf)

(“Projecting OECD Health and Long-term Care Expenditures: What are the Main Drivers?”, Economics Department Working Papers No. 477)

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NTA Results: Private Health Expenditure 10 9 8 7 6 5 4 3 2 1 0

US Thiland Philipine Costa Rica Chile China urban China rural

1 8 15 22 29 36 43 50 57 64 71 78 85 Source:NTA Project, 2007. Average consumption of 20-29 years is normalized to 1

NTA Results: Public Health Expenditure 30

US Thiland Philipine Costa Rica Chile China urban China rural

25 20 15 10 5 0 1 8 15 22 29 36 43 50 57 64 71 78 85 Source:NTA Project, 2007. Average consumption of 20-29 years is normalized to 1

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What shapes the “J Curve ” Š Epidemiological Reasons ] Expenditures

mainly associated with birth and pregnancy cause a difference in spending between young men and women. ] Higher expenditures on male compared to female senior citizens ]the

average cost per individual in oldest age groups should fall over time: Š Longevity gains are assumed to translate into additional years of good health (“healthy ageing) Š Major health costs come at the end of life and such “costs of dying” are usually lower for the oldest age groups compared to the middle aged. (Monika Riedel and Maria M. Hofmarcher, 2003)

(Monika Riedel and Maria M. Hofmarcher, “Australian health expenditures exhibit an age profile”, Vienna Yearbook of Population Research 2003, Vol. 1, pp. 197-213)

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Projecting OECD Health and Long-term Care Expenditures: What are the Main Drivers?”, Economics Department Working Papers No. 477

What shapes the “J Curve ” Š Technological and Economical Reasons ]Technological

change has obviously been an important driver of both health expenditures and health outcomes. ]There is a rather high correlation between income per capita and healthcare expenditure per capita for the EU15 countries as well as the EU11 countries. (AHEAD Policy Brief June 2007 “Work Package VIB on Determinants of Aggregate Health Expenditure Focusing on Age Composition” )

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Brigitte DORMONT, Michel GRIGNON and Helene HUBER, “Health expenditure growth: reassessing the threat of ageing”

What shapes the “J Curve ” Š Institutional reasons ]The

design of the system

Š Bismarck vs. Beveridge (R. Busse, 2001) ]Special

projects

Š Nominal cost increase among women is higher than for men due to more institutionalized care, especially in the long-term care sector (nursing homes and elderly homes) and other care (maternity services). (Jonan et al, 2002) Š Medicare in the US ]Fang

Hanming(2007)

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Projecting OECD Health and Long-term Care Expenditures: What are the Main Drivers?”, Economics Department Working Papers No. 477

Brigitte Dormont, Joaquim Oliveria Martins, Florian Pelgrin, “Health expenditures, longevity and growth”

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China Case Š Data ] ]

China Household Income Survey (1995) ; 50000 individuals The 2nd and 3rd National Healthcare Service Survey Š 2nd(1998): 215668 individuals Š 3rd(2003): 193695 individuals

Š Methodology ] ] ]

Regression Iteration Smooth with the width=0.1

Rapid increase in health care expenditure (1978-2004) NHE per capita

NHE as % of GDP

6

700

600

5

500 4 Yuan 300 2 200 1

100

0

0

19 78 19 79 19 80 19 81 19 82 19 83 19 84 19 85 19 86 19 87 19 88 19 89 19 90 19 91 19 92 19 93 19 94 19 95 19 96 19 97 19 98 19 99 20 00 20 01 20 02 20 03 20 04

%

400 3

Source: Health Statistic Yearbook. The statistic method of GDP in 2004 and 2005 was different.

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Aging in China Š China is experiencing rapid population aging. The onechild policy accelerate the aging process and make China’s aging problem more serious than any other countries in the world. Š According to a World Bank report, China’s aging population will reach the peak by 2030 (The World Bank, 1994). There will be 0.3 billion people over 60, which will account for 22% of the total population. Š Old age dependency ratio is expected to rise from currently 3.65 workers for every retired person to only two workers for a retired by 2030.

The Aging Problems % of people above 65

12000 7.7

million people

10000

7 6.2

8000 6000

5.6 4.9

4000 2000 0 1982

1990

1995

2000

9 8 7 6 5 4 3 2 1 0

%

Population

2005

Source:《中国统计摘要》

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Age Structure

31 Source: NBS, China,

China Case China's J Curve, National 9

1995

1998

2003

8 7

RMB

6 5 4 3 2 1 0 0

4

8

12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76 80 84 88 Age

Average expenditure of 0-10 years old normalized to 1

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China Case China's J Curve,1995, smoothed 2500

2000

Urban

Urban Male

Urban Female

Rural

Rural Male

Rural Female

RMB

1500

1000

500

0 0

4

8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76 80 84 88

Age

China Case China's J Curve,1998, smoothed 700

Urban

Rura

Urban Male

Urban Female

Rural Male

Rural Female

600 500

RMB

400 300 200 100 0 1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 Age

17

China Case China's J Curve,2003, smoothed 2500 Urban Rural National

2000

Urban Male Rural Male

Urban Female Rural Female

RMB

1500

1000

500

0 0

4

8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76 80 84 88 Age

Time-Series China's J Curve, urban

30

1995 urban

1998 urban

2003 urban

25

RMB

20 15 10

5 0 0

4

8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76 80 84 88 Age

Average expenditure of 0-10 years old normalized to 1

18

Time-Series China's J Curve, Rural

5 1995 Rural

1998 Rural

2003 Rural

4.5 4 3.5

RMB

3 2.5 2 1.5 1 0.5 0 0

4

8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76 80 84 88 Age

Average expenditure of 0-10 years old normalized to 1

Time-Series China's J Curve, Male 8

RMB

7

1995 Male

6

1998 Male

5

2003 Male

4 3 2 1 0 0

4

8

12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76 80 84 88 Age

Average expenditure of 0-10 years old normalized to 1

19

Time-Series China's J Curve, Female 7 1995 Female

1998 Female

2003 Female

6

5

RMB

4

3 2

1 0 0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54 57 60 63 66 69 72 75 78 81 84 87

Age Average expenditure of 0-10 years old normalized to 1

Optimal allocation of the individual lifetime health care expenditure Š Assumptions ] Health

care resources are scarce. One’s total health expenditure is constant. ] Health status is only related to per capital health expenditure of current year and before. ] Health Utility Function is well performed.

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Optimal Allocation-A Theoretical Model n

Find a optimal distribution of F ( X ) to Max(∑U i ) i =1

While

⎧U i = U ( f ( X i ), f ( X i −1 )... f ( X 1 )) for i = 1, 2...n ⎪ n ⎨ ⎪∑ f ( X i ) = const ⎩ i =1

f3

f(x)

f2

f1

0

100

x

A tentative results– Numerical Simulation Suppose n = 100 U i = U ( f ( X i ))

U i = U ( f ( X i ))

F ( X ) follows a F ( X ) follows a linear distribution quadratic distribution U i = U ( f ( X i ), f ( X i −1 )) U i = U ( f ( X i ), f ( X i −1 ))

F ( X ) follows a F ( X ) follows a linear distribution quadratic distribution

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A tentative results– Numerical Simulation Ui =

f ( X i ) F ( X ) follows a linear distribution f ( X ) = aX + b

A smaller a means a flatter distribution of lifetime health expenditure

RESULT

f (X ) ×104

100

0

The Change of Total Health Utility

X

A tentative results– Numerical Simulation Ui =

f ( X i ) F ( X ) follows a quadratic distribution f ( X ) = aX 2 − 10aX + c

A smaller a means a flatter distribution of lifetime health expenditure

RESULT ×104

f (X)

5

100

The Change of Total Health Utility

X

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A tentative results– Numerical Simulation 3

1 4

U i = [ f ( X i )] 4 [ f ( X i −1 ) ] F ( X ) follows a linear distribution f ( X ) = aX + b A smaller a means a flatter distribution of lifetime health expenditure

RESULT f (X )

×104

100

0

The Change of Total Health Utility

X

A tentative results– Numerical Simulation 3

U i = [ f ( X i )] 4 [ f ( X i −1 ) ]

1 4

F ( X ) follows a quadratic distribution f ( X ) = aX 2 − 10aX + c

A smaller a means a flatter distribution of lifetime health expenditure

RESULT f (X)

The Change of Total Health Utility

5

100

X

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Policy implications Š Adjust the projection of THE Š Rethink the government investment Š Rethink the distribution of lifetime health care consumption ] Health

care delivery system

Š 医防结合、重心下移 ] Medical

Model

Š “3Ps” Š “圣人不治已病,治未病;不治已乱,治未乱,此之谓也。”--《黄 帝内经》

Discussions Š The NTA project provides a great

opportunity to do empirical analysis on this important issue based on the international cooperation. Š The challenges for international comparison ]Companionability

of data ]Definition of institutional variables

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Thanks! Comments are very welcome!

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