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

1. Health Expenditures, Longevity and GrowthIX 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

2. Outline of the presentation • From Ageing to Longevity. Health ageing offers a potential to translate longevity into active life • Determinants of Health spending: ageing & technological progress. Health spending and health outcomes. Optimal health spending • Determinants of Health spending: income growth. Is health a luxury good? • Projections of total (public+private) health expenditures 2005-2050 • Health, productivity & growth. Do health status and health spending affect growth? R&D, innovation and global competition for the “health market”

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

4. A major shift in population structure(shares by age group in % total population) ? 2000 2000 2050 2050 Working age population Working age population 2000 2050 Working age population

5. Are we underestimating longevity gains? years/decade Source: National projections

6. Impact of indexing US working-age population on longevity gains

7. …and on EU-15 working-age population?

8. Impact of longevity indexing on US dependency ratios (65+/15-64) With indexation Labour force With indexation Working age population

9. …and on EU-15 old-age dependency ratio? With indexation Labour force With indexation Working-age Population

10. 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

11. Road-map of the next sections s5 s2 s2 s2 s5 s4 s3

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

13. 2.1 The main driver of health expenditure growth: changes in practices Why ageing impacts health expenditures Health expenditure Per capita & age group, France Population ageing France, 2005-2050

14. Profile drift between 1992 and 2000 The main part of the story: Non demographic effects

15. 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.

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

17. Health expenditures by age group : decedents versus survivors For survivors, the expenditure profile is increasing with age

18. 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.

19. 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

20. Micro-simulationresults(Pharmaceuticals, unconditional consumption) 2000 Changes in morbidity Changes in practices 1992

21. Retrospective decomposition of changes in expenditures(Pharmaceuticals, France 1992-2000)

22. 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

23. 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)

24. 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?

25. 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

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

27. 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.

28. 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.

29. 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

30. 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

31. 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.

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

33. 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

34. Empirical evidence on the income elasticity

35. 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?

36. A simple econometric test NB: 30 OECD countries, for the period 1970-2002. Including one-way fixed-effects.  On average, the share of Health expenditures to GDP tends to grow at around 1.7% per year

37. 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

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

39. The projection framework is based on health care public expenditure profiles by age-groups(normalised GDP p.c. 1999) Age groups Source: ENPRI-AGIR and OECD

40. Public vs. Private Health expenditure profiles in the US Age groups

41. 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

42. Demographic drivers illustrated

43. Non-demographic drivers push expenditure curves up Income + technology residual

44. 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

45. Several projection scenarios 2005-2050(in % of GDP) • Healthy ageing: 1 year gain in life expectancy = 1 year in good health

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

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

48. 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.

49. 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)

50. 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.