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
1
Outline of the presentation
1. 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”
2
1. From Ageing to Longevity:
Health ageing offers a potential
to translate longevity into active life
3
A major shift in population structure
(shares by age group in % total population)
EU15
United States
12.0
12.0
?
10.0
10.0
2000
8.0
8.0
2050
6.0
2000
6.0
4.0
4.0
2.0
2.0
2050
Working age population
Working age population
59
10
14
15
19
20
24
25
29
30
34
35
39
40
44
45
49
50
54
55
59
60
64
65
69
70
74
75
79
80
84
85
89
90
94
95
+
04
59
10
14
15
19
20
24
25
29
30
34
35
39
40
44
45
49
50
54
55
59
60
64
65
69
70
74
75
79
80
84
85
89
90
94
95
+
0.0
Japan
12.0
10.0
8.0
2000
6.0
2050
4.0
2.0
Working age population
59
10
14
15
19
20
24
25
29
30
34
35
39
40
44
45
49
50
54
55
59
60
64
65
69
70
74
75
79
80
84
85
89
90
94
95
+
0.0
04
04
0.0
4
Are we underestimating longevity gains?
(A) average gains
1960-2000
United States
Europe
Austria
Belgium
Czech Republic
Denmark
1.7
(B) projected gains
2000-20501
years/decade
1.4
2.4
1.8
1.1
1.1
1.4
1.6
1.3
1.1
Finland
France
Germany
Greece
2.2
2.2
2.0
2.1
1.5
1.8
1.2
0.8
Hungary
Ireland
Italy
Luxembourg
0.9
1.7
2.4
2.2
1.6
0.9
1.8
1.1
Netherlands
Poland
Portugal
Slovak Republic
1.1
1.5
3.1
0.7
0.5
2.0
1.1
1.5
Spain
Sweden
United Kingdom
2.3
1.7
1.8
0.8
0.9
1.6
EU15 average
2.0
1.2
Japan
Memo item:
OECD average
3.4
0.8
2.2
1.2
Source: National projections
5
Impact of indexing US working-age population on
longevity gains
15-29
50-64
Total
30-49
Additional WAP
With longevity indexation
250.0
200.0
150.0
100.0
50.0
2050
2045
2040
2035
2030
2025
2020
2015
2010
2005
2000
1995
1990
1985
1980
1975
0.0
1970
Millions
300.0
6
…and on EU-15 working-age population?
15-29
50-64
Total
300.0
30-49
Additional WAP
With longevity indexation
250.0
200.0
150.0
100.0
50.0
2050
2045
2040
2035
2030
2025
2020
2015
2010
2005
2000
1995
1990
1985
1980
1975
0.0
1970
Millions
350.0
7
Impact of longevity indexing on US dependency
ratios (65+/15-64)
% 50.0
United States
45.0
40.0
With indexation
35.0
Labour force
30.0
25.0
20.0
With indexation
Working age population
15.0
2050
2045
2040
2035
2030
2025
2020
2015
2010
2005
2000
10.0
8
…and on EU-15 old-age dependency ratio?
80.0
EU15
70.0
With indexation
60.0
50.0
Labour force
40.0
With indexation
30.0
Working-age Population
2050
2045
2040
2035
2030
2025
2020
2015
2010
2005
20.0
2000
%
9

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
10
Road-map of the next sections
s5
R&D/Innovation
Welfare
Health
spending/
Investment
s2
Technological
progress
s2
Health status
Longevity
s2
s5
GDP
s4
Income elasticity
s3
11
2. Determinants of Health spending:
-Ageing & technological progress
-Health spending and health
outcomes
-Optimal health spending
12
2.1 The main driver of health expenditure growth:
changes in practices
Why ageing impacts health expenditures
28
26
€uros
24
22
20
18
16
14
4000
3500
3000
2500
2000
1500
1000
500
0
2000
0
05 010 015 020 025 030 035 040 045 050
0
2
2
2
2
2
2
2
2
2
2
Population ageing
France, 2005-2050
10
20
30
40
50
60
70
80
Age group
Health expenditure
Per capita & age group, France13
Profile drift between 1992 and 2000
The main part of the story:
4000
3500
€uros
3000
Non demographic
effects
2500
1992
2000
2000
1500
1000
500
0
0
10
20
30
40
50
Age group
60
70
80
14
The role of the proximity of death





The idea of a boom in health expenditures linked to
population ageing is not supported by macroeconometric 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.
15
Yang et al. (2003):
Health expenditures and proximity to death
16
Health expenditures by age group :
decedents versus survivors
For survivors, the expenditure profile is increasing with age
17
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.
18
The predominant impact of changes in
medical practices

Retrospective analysis for France 1992-2000 (DormontGrignon-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
19
Micro-simulation results
(Pharmaceuticals, unconditional consumption)
2000
Changes in morbidity
Changes in practices
1992
20
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
21
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
22
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)
23
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?
24
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 25
Three possible scenarios for future changes in
morbidity at a given age
26
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.
27
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
28
return equal to 32 % GDP.
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
29
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
30
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.
31
3. Determinants of Health spending:
-Income growth
-Is health a luxury good?
32
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
33
Empirical evidence on the income elasticity
Individual (micro)
Income elasticity
Insured
Newhouse and Phelps (1976)
Hahn and Lefkowitz (1992)
≤0.1
≤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
Dreger and Reimers (2005) – 21 OECD countries
Co-integrated
Unitary elasticity not rejected
34
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 cointegrated.
– 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?
35
A simple econometric test
Dependant variable: log of health
expenditures per capita
Model I
Model II
Log GDP per capita
1.58***
0.937***
--
0.017***
Time trend
NB: 30 OECD countries, for the period 1970-2002. Including one-way fixedeffects.
 On average, the share of Health expenditures to GDP
tends to grow at around 1.7% per year
36
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, twoway 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
37
4. Projections of total (public & private)
health expenditures 2005-2050
38
The projection framework is based on health care
public expenditure profiles by age-groups
% of GDP per capita
(normalised GDP p.c. 1999)
25.0
Austria
Belgium
Denmark
20.0
Finland
France
Germany
Greece
Ireland
Italy
Luxembourg
15.0
Netherlands
Portugal
Spain
Sweden
UK
Australia
United States
10.0
5.0
Age groups
95
+
85
-8
9
90
-9
4
80
-8
4
75
-7
9
70
-7
4
60
-6
4
65
-6
9
55
-5
9
50
-5
4
45
-4
9
35
-3
9
40
-4
4
30
-3
4
25
-2
9
20
-2
4
10
-1
4
15
-1
9
59
04
0.0
39
Source: ENPRI-AGIR and OECD
Public vs. Private Health expenditure profiles in
the US
7000.0
HE per capita excluding LTC
public
6000.0
HE per capita excluding LTC
private
5000.0
4000.0
3000.0
2000.0
1000.0
0.0
2
7
12
17 22
27
32 37
42
47 52
57
62 67
72
77 82
87
92 97
Age groups
40
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
41
Demographic drivers illustrated
(1) Pure ageing effect
Health expenditure per capita
Average in 2050
Average in 2000
Pure demographic effect
Young
Old
Age groups
(2) Ageing effect adjusted for death-related costs and healthy longevity
Health expenditure per capita
Young
42
Old
Age groups
Non-demographic drivers push expenditure
curves up
(3) Non-ageing drivers
Health expenditure per capita
Income + technology residual
Non-demographic effects
Young
Old
Age groups
43
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


44
Several projection scenarios 2005-2050
(in % of GDP)
Scenario I
Scenario II
η=1
η=1.5
residual=1% p.a. residual=1% p.a.
Healthy ageing
Healthy ageing
Scenario III
Scenario IV
η=1
residual=2% p.a.
Healthy ageing
η=1
residual=1%
declining to 0 by
2050
Expansion of
morbidity
(Level 2005)
US
(14%)
19%
23%
26%
18%
EU-15
(8%)
13%
17%
20%
11%

Healthy ageing: 1 year gain in life expectancy = 1 year in good health
45
Decomposition of the expenditure change 2005-2050 for EU-15
(in % GDP)
Scenario I
Scenario II
Scenario III
Scenario IV
η=1
residual=1% p.a.
Healthy ageing
η=1.5
residual=1% p.a.
Healthy ageing
η=1
residual=2% p.a.
Healthy ageing
η=1
declining residual
Expansion of
morbidity
Deathrelated
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
46
5. Health, productivity & growth:
Do health status and health spending
affect growth? R&D, innovation
and global competition for the
“health market”
47
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.
48
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)
49
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 ageproductivity profiles have little impact at the macro level.
50
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 broad51
reform strategy is needed
Thank you !
52