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Health Expenditures, Longevity and Growth

IX European Conference of the Fondazione RODOLFO DE BENEDETTI “Health, Ageing and Productivity”

Limone sul Garda, 26 May, 2007

Brigitte Dormont, Joaquim Oliveira Martins,Florian Pelgrin and Marc Surcke

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Outline of the presentation1. From Ageing to Longevity. Health ageing offers a

potential to translate longevity into active life

2. Determinants of Health spending: ageing & technological progress. Health spending and health outcomes. Optimal health spending

3. Determinants of Health spending: income growth. Is health a luxury good?

4. Projections of total (public+private) health expenditures 2005-2050

5. Health, productivity & growth. Do health status and health spending affect growth? R&D, innovation and global competition for the “health market”

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1. From Ageing to Longevity: Health ageing offers a potential to translate longevity into active life

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A major shift in population structure(shares by age group in % total population)

EU15 United States

Japan

0.0

2.0

4.0

6.0

8.0

10.0

12.0

04 5910

1415

1920

2425

2930

3435

3940

4445

4950

5455

5960

6465

6970

7475

7980

8485

8990

94 95+

0.0

2.0

4.0

6.0

8.0

10.0

12.0

04 5910

1415

1920

2425

2930

3435

3940

4445

4950

5455

5960

6465

6970

7475

7980

8485

8990

94 95+

0.0

2.0

4.0

6.0

8.0

10.0

12.0

04 5910

1415

1920

2425

2930

3435

3940

4445

4950

5455

5960

6465

6970

7475

7980

8485

8990

94 95+

20002000

2000

20502050

2050

Working age population Working age population

Working age population

?

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Are we underestimating longevity gains?(A) average gains

1960-2000(B) projected gains

2000-20501

United States 1.7 1.4Europe

Austria 2.4 1.4Belgium 1.8 1.6Czech Republic 1.1 1.3Denmark 1.1 1.1

Finland 2.2 1.5France 2.2 1.8Germany 2.0 1.2Greece 2.1 0.8

Hungary 0.9 1.6Ireland 1.7 0.9Italy 2.4 1.8Luxembourg 2.2 1.1

Netherlands 1.1 0.5Poland 1.5 2.0Portugal 3.1 1.1Slovak Republic 0.7 1.5

Spain 2.3 0.8Sweden 1.7 0.9United Kingdom 1.8 1.6

EU15 average 2.0 1.2Japan 3.4 0.8Memo item: OECD average 2.2 1.2

years/decade

Source: National projections

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Impact of indexing US working-age population on longevity gains

0.0

50.0

100.0

150.0

200.0

250.0

300.019

70

1975

1980

1985

1990

1995

2000

2005

2010

2015

2020

2025

2030

2035

2040

2045

2050

Mill

ions

15-29 30-4950-64 Additional WAPTotal With longevity indexation

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…and on EU-15 working-age population?

0.0

50.0

100.0

150.0

200.0

250.0

300.0

350.0

1970

1975

1980

1985

1990

1995

2000

2005

2010

2015

2020

2025

2030

2035

2040

2045

2050

Mill

ions

15-29 30-4950-64 Additional WAPTotal With longevity indexation

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Impact of longevity indexing on US dependency ratios (65+/15-64)

United States

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

50.020

00

2005

2010

2015

2020

2025

2030

2035

2040

2045

2050

%

With indexation

With indexation

Labour force

Working age population

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…and on EU-15 old-age dependency ratio?

EU15

20.0

30.0

40.0

50.0

60.0

70.0

80.0

2000

2005

2010

2015

2020

2025

2030

2035

2040

2045

2050

With indexation

With indexation

Labour force

Working-age Population

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Indexing the old-age threshold in line with longevity gains would only contribute to solve the ageing problem if aged workers…

(1) Remain in good health (“Healthy ageing”)

(2) Participate in the labour force and are employed

(3) Pension systems are reformed in order to remove incentives for early retirement

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Road-map of the next sections

Health spending/

Investment

Health statusLongevity

GDP

Welfare

Income elasticity

R&D/Innovation

Technologicalprogress

s2 s2 s2s5

s5

s3

s4

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2. Determinants of Health spending: -Ageing & technological progress -Health spending and health outcomes-Optimal health spending

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2.1 The main driver of health expenditure growth: changes in practices

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16

18

20

22

24

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0500

1000150020002500300035004000

0 10 20 30 40 50 60 70 80Age group

€uros 2000

Population ageing France, 2005-2050

Health expenditure Per capita & age group, France

Why ageing impacts health expenditures

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Profile drift between 1992 and 2000

0500

1000150020002500300035004000

0 10 20 30 40 50 60 70 80

Age group

€uro

s 19922000

Non demographic effects

The main part of the story:

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The role of the proximity of death The idea of a boom in health expenditures linked to

population ageing is not supported by macro-econometric estimations

A non significant influence of age on health expenditures is found (Getzen, 1992; Gerdtham et al.,1992,1998, etc.)

Possible explanation: high cost of dying. The correlation between age and health expenditures might be spurious due to the fact that the probability of dying increases with age

Once proximity to death is controlled for, age would not influence health expenditures

Micro-econometric evidence by Zweifel et al., Seshamani & Gray, etc.

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Yang et al. (2003): Health expenditures and proximity to death

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Health expenditures by age group : decedents versus survivors

For survivors, the expenditure profile is increasing with age

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The role of time to death: current consensus (i) Both age and time to death have an influence on

health expenditures.

(ii) Health expenditure predictions have to include time to death in their modelisation in order to be relevant.

This last point is now widely accepted. On US data, Stearns and Norton (2004) show that omitting time to death leads to an overstatement of 15 % for health expenditures, when using projected life tables for 2020.

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The predominant impact of changes in medical practices

Retrospective analysis for France 1992-2000 (Dormont-Grignon-Huber, 2006)

Sample of 3,441 and 5,003 French individuals

Micro-simulation methods to evaluate the components of the upward drift in the age profile of health expenditures– Role of changes in morbidity at a given age– Role of changes in practices for given levels of morbidity and age

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Micro-simulation results(Pharmaceuticals, unconditional consumption)

1992

2000

Changes in practices

Changes in morbidity

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Retrospective decomposition of changes in expenditures

(Pharmaceuticals, France 1992-2000)

Variation 1992-2000 (%) 67.27 Total demographic change 7.63

-part of structural change 4.61 -part of growing size of the population 3.02

 Changes in morbidity -9.24

Changes in practices for a given morbidity 52.24

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Main results

Ageing explains a small part of the rise in health expenditures

Changes in practices are the most important driver

Evidence of health improvements which induce savings

These savings are large enough to offset the increase in costs due to ageing

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2.2 Innovation and product diffusion in health care

The research leading to innovation does not necessarily take place in biomedical sector : lasers, ultrasounds, magnetic resonance spectroscopy, computer, nanotechnology. (Gelijns & Rosenberg, 1994)

Two mechanisms : substitution (gain in efficiency) and extension (increasing use of the new technology). – Growth in treatment costs results entirely from diffusion of innovative

procedures (Cutler & McClellan, 1996) – Example: treatment of heart attack with bypass surgery and angioplasty.– Other examples: cataract surgery, hip replacement, knee replacement, etc.

The orientation of technological progress is not neutral: certain type of innovations will be favoured, depending on the design of the health insurance and on the payment systems implemented by the payers (Weisbrod, 1991)

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Are medical innovations worth the additional costs?

What is the impact of health care on longevity and health?

Is the value of the gains in longevity and health larger than the additional costs?

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The impact of health care on longevity and health

Robert Fogel (2003) on 45,000 US veterans: average age of onset of chronic conditions increased by 10 years, while life expectancy increased by 6.6 years.

Murphy & Topel: gain in life expectancy in the US: +9 years between 1950 and 2000, of which– + 3.7 years for reduced mortality in heart disease– + 1 year for reduced mortality due to stroke

Cutler et al. (2006): between 1984 and 1999 improved medical care for CVD in the US explains– 70 % mortality reduction– 50 % reduction in disability caused by CVD

Progress in hip replacement and other surgeries explains decline in disability due to musculoskeletal problems (Cutler, 2003)

There is empirical evidence, at least for some conditions, that a quality adjusted price index would not rise but decrease over time

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Three possible scenarios for future changes in morbidity at a given age

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2.3 The value of health and the optimal allocation of resources to health expenditures

It is important to take into account the value of health for two reasons:

to improve the measure of economic growth and welfare

public expenditures account for a large share of health expenditures efficient decisions need an appropriate valuation of: – health improvements linked to expenditures– collective preferences for better health and additional years of life.

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Using the value of life to assess the gains in welfare due to health care

The value of a statistical life (VSL) is inferred from risk premiums in the job market or by analysing the markets prices for products that reduce the probability of death from $ 2 millions to 9 millions (Viscusi & Aldy 2003)

Value of a year of life : $100,000 (Cutler, 2004) VSL can be used to evaluate the return on new technologies in health

care: positive for treatment of heart attack ($70,000/$10,000), depression ($6,000/$1,000), cataract surgery ($95,000/$3,000)

VSL can also be used to evaluate global improvements in health. Murphy & Topel (JHE, 2006, Kenneth J. Arrow Award for best paper in health economics published in 2006) assess the value of gains in longevity due to health expenditures .

The results is striking: for the US between 1970 and 2000, gains in life expectancy added to wealth a gain equal to about 50 % of the GDP each year. Subtracting the costs due to rising medical expenditures lead to a return equal to 32 % GDP.

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Assessing the optimal allocation of resources to health expenditures

Hall & Jones (2007): the optimal allocation of resources maximizes the expected lifetime utility subject to the budget constraint and the health production function.

Budget constraint: the income can be spent on consumption or health

Theoretical prediction: the optimal share of income devoted to health care s increases if the value of one year of life rises faster than income.

This condition is fulfilled for preferences characterised by a specification of the utility function, with a key parameter γ >1 .

A large empirical literature suggests that γ =2. Thus, the rising share of health expenditures is likely to fit collective preferences

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Simulations: optimal health share increases (Hall & Jones)

For γ=1.01 the marginal utility of consumption falls more slowly than the diminishing returns in the reduction of health

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Summing-up Technological progress, instead of ageing, is the main driver of

health expenditure growth. Two mechanisms are involved in technological progress in health

care, substitution and extension. The growth in health expenditures is entirely explained by the

extension effect: more goods are available and consumed. The diffusion of technologies has led to additional costs but also to

more value in terms of longevity and better health it has probably contributed to an increase in welfare.

Evaluating the level of health expenditures that maximizes social welfare, one finds that social preferences appear to be in favour of a continuous increase in the share of income devoted to health.

Maximizing social welfare requires the development of institutions consistent with the predicted increase in health spending.

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3. Determinants of Health spending: -Income growth -Is health a luxury good?

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Is health care a luxury or necessity?

Is health care a luxury or a necessity? (Getzen, 2000). The answer depends on the level of analysis: health is a necessity at the individual level and a luxury at the aggregate level

Omitted variables typically lead to an overestimation of the income elasticity (Dreger and Reimers(2005), AHEAD, 2006) When additional variables are added (age, time trends) the income elasticity is close or below one

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Individual (micro) Income elasticity Insured

Newhouse and Phelps (1976) ≤0.1

Hahn and Lefkowitz (1992) ≤0

less insured/uninsured

Falk et al (1933) 0.7

Andersen and Benham (1970) - dental 1.2

AHCPR (1997) - dental 1.1

Regions (intermediate)Fuchs and Kramer (1972) – 33 states, 1966 0.9

Di Matteo and Di Matteo (1998) – 10 Canadian provinces, 1965-91 0.8

Freeman (2003) – US states, 1966-98 0.8

Nations (macro)Newhouse (1977) – 13 countries, 1972 1.3

Getzen (1990) – US, 1966-87 1.6

Schieber (1990) – seven countries, 1960-87 1.2

Gerdtham and Löthgren (2000, 2002) - 25 OECD countries, 1960-97 Co-integrated

Dreger and Reimers (2005) – 21 OECD countries Unitary elasticity not rejected

Empirical evidence on the income elasticity

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Econometric estimation issues Time-series, cross-section or panel analysis?

– Evidence is now based on time-series and panel data– Omitted variables, endogeneity, heterogeneity?– Unit root tests and co-integration tests: GDP and Health care

expenditure are characterised by unit-roots and are co-integrated.

– Cross-sectional dependence (countries are not independent)– Convergence of health expenditures across countries

Existence of a third factor?– Co-integration results can be driven by the existence of one or

more common factors (technology, population, ...). As seen in section 2, technology is a main driver of health expenditures, but how to capture such an effect?

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A simple econometric test

NB: 30 OECD countries, for the period 1970-2002. Including one-way fixed-effects.

Dependant variable: log of health expenditures per capita

Model I Model II

Log GDP per capita 1.58*** 0.937***

Time trend -- 0.017***

On average, the share of Health expenditures to GDP tends to grow at around 1.7% per year

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Econometric approach We provide an extensive empirical test:

– By decomposing health expenditures (private, public and total)

– Use of different country groupings– Include time trends, age structure and some

institutional variables– Test for different specifications: pooled, one-way, two-

way fixed effects, and random-weight estimators

A unitary income elasticity seems the most reasonable assumption to project health expenditures. But this is not small!

This implies that the increase in the share of health to GDP is due other factors

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4. Projections of total (public & private) health expenditures 2005-2050

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The projection framework is based on health care public expenditure profiles by age-groups

(normalised GDP p.c. 1999)

Source: ENPRI-AGIR and OECD

0.0

5.0

10.0

15.0

20.0

25.0

% o

f GD

P pe

r cap

ita

Austria

Belgium

Denmark

Finland

France

Germany

Greece

Ireland

Italy

Luxembourg

Netherlands

Portugal

Spain

Sweden

UK

Australia

United States

Age groups

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Public vs. Private Health expenditure profiles in the US

Age groups

0.0

1000.0

2000.0

3000.0

4000.0

5000.0

6000.0

7000.0

2 7 12 17 22 27 32 37 42 47 52 57 62 67 72 77 82 87 92 97

HE per capita excluding LTCpublic

HE per capita excluding LTCprivate

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The drivers of expenditure The pure demographic effect : constant expenditure profiles and

applied to the change in demographic structures… but this implicitly assumes an “expansion of morbidity” when longevity increases

The pure demographic effect has to be adjusted for:

– The possibility for different health status [Grunenberg(1977); Fries(1980); Manton(1982)], including a dynamic equilibrium between good health and longevity ("Healthy ageing“)

– Which is coherent with the hypothesis that major health costs are concentrated in the proximity to death [eg. Batjlan and Lagergren, 2004]

Project expenditures for survivors and non-survivors

Non-demographic drivers are the most important

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Demographic drivers illustrated(1) Pure ageing effect

Health expenditure per capita

Young OldAge groups

(2) Ageing effect adjusted for death-related costs and healthy longevityHealth expenditure per capita

Young OldAge groups

Average in 2050

Average in 2000

Pure demographic effect

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Non-demographic drivers push expenditure curves up

(3) Non-ageing driversHealth expenditure per capita

Young OldAge groups

Non-demographic effects

Income + technology residual

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Additional exogenous assumptions

National population projections (N) [cf. Oliveira Martins et al. (2005)]

Labour force projections (L/N) [Burniaux et al. (2003)] Labour productivity (Y/L) growth is assumed to converge

linearly from the initial rate (1995-2003) to 1.75% per year by 2030 in all countries, except former transition countries and Mexico where it converges only by 2050.

Projected GDP per capita: Y/N = Y/L x L/N

The projections allow for a certain convergence of expenditures across-countries

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Several projection scenarios 2005-2050(in % of GDP)

Healthy ageing: 1 year gain in life expectancy = 1 year in good health

(Level 2005)

Scenario Iη=1

residual=1% p.a.Healthy ageing

Scenario II η=1.5

residual=1% p.a.Healthy ageing

Scenario IIIη=1

residual=2% p.a.Healthy ageing

Scenario IV η=1

residual=1% declining to 0 by

2050Expansion of

morbidity

US (14%)

19% 23% 26% 18%

EU-15 (8%)

13% 17% 20% 11%

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Decomposition of the expenditure change 2005-2050 for EU-15 (in % GDP)

Scenario Iη=1

residual=1% p.a.Healthy ageing

Scenario II η=1.5

residual=1% p.a.Healthy ageing

Scenario IIIη=1

residual=2% p.a.Healthy ageing

Scenario IV η=1

declining residualExpansion of

morbidity

Death-related costs

0.2 0.2 0.2 0.2

Pure age effect

1.5 1.5 1.5 1.5

Adj. healthy ageing

-0.7 -0.7 -0.7 --

Income effect

-- 2.5 -- --

Tech. residual

4.4 4.4 11.4 1.9

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5. Health, productivity & growth: Do health status and health spending affect growth? R&D, innovation and global competition for the “health market”

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Health and the economy: main channels Labor productivity: healthier individuals could

reasonably be expected to produce more per hour worked Labor supply: Good health increases the number of days

available for either work or leisure; Health may influence labour supply (wages, preferences and expected life horizon, but ambiguous effect which depends on substitution and income effects)

Education: better health contributes to more educated and productive people; longevity encourage people to invest in education

Savings and Investment: health affects savings behavior and willingness to undertake investment

R&D and Innovation: Good health enhances creativity and demand for new health goods & services.

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Empirical evidence

Positive impact for developing countries and world level; when measured as life expectancy or adult mortality, health is among very few robust predictors of subsequent economic growth (Levine and Renelt, 1992; Sala-I-Martin, 2004)

But mixed evidence for OECD countries (e.g. Rivera and Currais (1999) vs. Knowles and Owen (1995, 1997) regarding life expectancy in OECD countries)

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Possible explanations Lack of good measure of health status A non-linear relationship (diminishing returns to health) Pension systems and labour markets favoured early

retirement, thus the potential effect of better health on participation did not materialise

Efforts to increase life expectancy at older ages may have a negative impact on growth. The resources devoted to health care are at the expense of other factors (Aisa & Pueyo, 2005, 2006)

An increase of health status is likely to have only a level effect on total productivity, with little impact on labour productivity growth. Assuming contrasted individual age-productivity profiles have little impact at the macro level.

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Health and a growth strategy for the EU While EU is doing better in longevity and health status, this

potential resources have been wasted in low participation and early retirement of older workers

Increasing share of health expenditures to GDP is mainly driven by technological progress. Preferences for longer lives are driving up the optimal share of health spending. Current institutions are not suited to cope with this challenge.

There is a large market out there, but EU is lagging in terms of R&D and innovation. This is due to differences in regulation and market structure requiring appropriate product market reforms

There strong connections and complementarities across health, labour market, pension reforms, etc. A broad-reform strategy is needed

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Thank you !