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National Institute of Economic and Social Research WP8: Factors Influencing Health Expenditures and Scenarios for Health Expenditures - EU Ehsan Khoman.

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Presentation on theme: "National Institute of Economic and Social Research WP8: Factors Influencing Health Expenditures and Scenarios for Health Expenditures - EU Ehsan Khoman."— Presentation transcript:

1 National Institute of Economic and Social Research WP8: Factors Influencing Health Expenditures and Scenarios for Health Expenditures - EU Ehsan Khoman and Martin Weale

2 Hypotheses about Determinants of Health Care Spending We explore hypotheses about the long-run elasticity of health spending with respect to GDP, that spending is closely related to the age structure of the population and that it is substantially driven by death-related costs. Consider our model: (8) The coefficient If we assume that health care expenditure is driven by ageing then, given that β 1 is spending for the population aged under 65, β 2 is spending for the population aged between 65 and 74 and β 3 is spending for the population aged 75 and over, then total health spending is given by (9)

3 Hypotheses about Determinants of Health Care Spending The effect on the proportion of 65-74 year olds of a change in Π 2 is (10) and the effect of the same change given the model of health care expenditure is (11) So the semi-elasticity with respect to the proportion is given as (12)

4 Hypotheses about Determinants of Health Care Spending We can similarly impose the restriction that a proportion of health spending is determined by the number of deaths. If (13) then (14) Thus if spending per death (or spending in the last year of life) is known the associated semi-elasticity can be imposed.

5 Hypotheses about Determinants of Health Care Spending Long-Run Coefficients with Different GD Elasticities GDPPC 1.0781.0001.1001.200 AGE0_5 0.0070.0050.0030.000 AGE65_74 -0.030-0.013-0.015-0.218 AGE75_ -0.082-0.056-0.060-0.065 AVELE65 0.1440.1130.1210.131 UNEMP -0.010-0.021-0.015-0.008 ALCCON -0.023-0.020-0.025-0.030 PUHES 0.0050.008 SALARYGP 0.2060.2080.2000.197 CAPGP 0.2710.2530.2470.246 GLOBALHO -0.005-0.0060.0210.039 CASEHO 0.000-0.0130.0140.032 COPAYGP 0.0250.000 COPAYHO -0.112-0.083-0.084-0.085 FREEGP 0.3500.3040.3160.328 FREEHO -0.012-0.009-0.006-0.003 BEDS 0.0180.000 MORTALITY 0.7420.7170.7100.714

6 Hypotheses about Determinants of Health Care Spending Derivation of the Restrictions of the Long-Run Values of the Age Terms when Health Expenditure is Age-Related CountryHealth Expen. as of GDP GDP (2003)Health Expen.Proportion of Health Expen. by Age 0-6465-7475+ Austria8.65%226243195700.5590.1490.292 Belgium9.28%274658254950.5150.1670.319 Denmark8.68%188500163630.6400.1290.231 Finland6.48%14593894630.5750.1230.301 France9.58%15948141527340.5500.1380.312 Germany11.37%21615002457990.5380.1720.290 Greece10.22%155543158920.4460.2520.302 Eire7.47%138941103780.6450.1280.227 Italy8.50%13353541135040.5310.1870.282 Luxem.5.94%2560715220.5650.1640.270 Nether.8.71%476945415390.6170.1400.242 Portugal9.60%137523131980.6190.1560.224 Spain6.91%782531540730.5310.1730.296 Sweden9.01%269548242920.5160.1140.370 U.K.7.76%16044971245490.4230.1480.429 Total9.10%9518142868369

7 Hypotheses about Determinants of Health Care Spending Mortality-Related Costs as a Proportion of Total Health Expenditure CountryMortality-related costs (% of total) Health SpendingMortality-related spending Austria24.0%195794699 Belgium28.0%254957139 Denmark23.0%163633770 France43.0%15273465675 Germany25.3%24579962070 Italy28.0%11350431781 Netherlands27.0%4153911216 Portugal36.8%131984853 Spain45.4%5407324529 Sweden23.0%242925587 EU as a whole706575221320 Mortality Rest.0.313229

8 Hypotheses about Determinants of Health Care Spending Scenarios of Different Restrictions On the Demographic Parameters ModelGDP ElasticityAGE65-74AGE75+AVELE65MORTALITY 0 10 20.313 3000 40.0090.02000 50.0090.02000 61 710 810.313 91000 1010.0090.02000 1110.0090.02000

9 Hypotheses about Determinants of Health Care Spending Long-Run Coefficients with Demographic Restrictions, GDP Elasticity Restricted Unrest.Model 0Model 1Model 2Model 3Model 4Model 5 GDPPC 1.0781.0950.9231.0550.2021.0171.190 AGE0_5 0.0070.003-0.019-0.0460.414-0.0110.149 AGE65_74 -0.030-0.0150.0240.0340.0000.009 AGE75_ -0.082-0.0600.0000.0130.0000.020 AVELE65 0.1440.1200.089-0.1100.0000.0390.000 UNEMP -0.010-0.016-0.0140.0930.2540.0120.757 ALCCON -0.023-0.025-0.029-0.0450.201-0.0300.048 PUHES 0.0050.0080.0160.0150.1970.0150.007 SALARYGP 0.2060.2010.2770.1450.6300.236-0.092 CAPGP 0.2710.2470.2850.1850.5830.258-0.037 GLOBALHO -0.0050.020-0.0480.228-3.1210.1250.454 CASEHO 0.0000.013-0.1330.104-4.5660.0370.404 COPAYGP 0.0250.000 COPAYHO -0.112-0.084-0.057-0.1320.964-0.0900.012 FREEGP 0.3500.3160.2680.271-0.9180.3060.352 FREEHO -0.012-0.006-0.0330.005-1.1840.025-0.176 BEDS 0.0180.000 MORTALITY 0.7420.7100.7130.313 0.6490.000

10 Hypotheses about Determinants of Health Care Spending Long-Run Coefficients with Demographic Restrictions, GDP Elasticity Restricted Unrest.Model 6Model 7Model 8Model 9Model 10Model 11 GDPPC 1.0781.000 AGE0_5 0.0070.005-0.022-0.045-1.615-0.0110.157 AGE65_74 -0.030-0.0130.0240.0360.0000.009 AGE75_ -0.082-0.0560.0000.0150.0000.020 AVELE65 0.1440.1130.093-0.1170.0000.0390.000 UNEMP -0.010-0.021-0.0090.0880.4150.0111.015 ALCCON -0.023-0.020-0.034-0.042-0.0660-0.0290.054 PUHES 0.0050.0080.0160.015-1.3790.0150.001 SALARYGP 0.2060.2080.2740.149-3.7840.237-0.098 CAPGP 0.2710.2530.2820.187-3.7770.259-0.036 GLOBALHO -0.005-0.006-0.0280.2150.2160.1190.508 CASEHO 0.000-0.013-0.1170.0900.6540.0320.501 COPAYGP 0.0250.000 COPAYHO -0.112-0.083-0.057-0.132-0.312-0.089-0.062 FREEGP 0.3500.3040.2760.2630.3830.3040.404 FREEHO -0.012-0.009-0.0320.0030.2940.025-0.178 BEDS 0.0180.000 MORTALITY 0.7420.7170.7070.313 0.6510.000

11 Results Summary Results of Health Care Projections for the EU15 Country2003 Value Model 0Model 1Model 6Model 7Model 3SModel 5SAWG Scenario Austria8.7%11.52%19.75%11.05%21.12%11.56%12.95%9.5% Belgium9.3%11.32%15.55%10.68%16.68%10.84%12.27%10.9% Denmark8.7%8.87%11.62%8.48%12.28%9.16%10.20%9.6% France6.5%7.11%8.83%6.62%9.52%5.50%5.96%9.6% Germany11.4%13.46%23.81%13.16%25.06%15.15%15.83%9.2% Italy8.5%10.3% Netherlands8.7%9.22%16.09%8.93%17.08%10.63%11.09%9.9% Portugal9.6%9.72%19.56%7.95%24.40%12.99%20.00%10.4% Spain6.9%8.61%16.84%8.50%17.71%10.27%12.32%13.0% Sweden9.0%8.70%8.55%8.11%9.10%8.58%9.05%11.0% U.K.7.8%8.65%9.80%7.99%10.60%7.59%8.93%13.4%

12 Results Classifications of Model and AWG Projections More than 1% point above AWG Within 1% point of AWGMore than 1% point below AWG Model 0 Austria, GermanyBelgium, Denmark, Eire, Netherlands, Portugal Finland, France, Spain, Sweden, U.K. Model 3S: Mortality-related Costs Austria, Belgium, Germany, Portugal Denmark, NetherlandsFinland, Eire, Spain, Sweden, U.K. Model 5S: Age-related Costs Austria, Belgium, Germany, Netherlands, Portugal Denmark, France, Eire, Spain Finland, Sweden, U.K.

13 Conclusions The aim of this work package is to present projections of health care expenditure in order to assess the impact of ageing populations on future spending levels. One of the key messages that emerges from this work is that a variety of variables seems to influence health spending, and the influence of factors such as the share of the public sector in the total could easily be omitted from more mechanical calculations. Thus, the results from this study provide a valuable insight into influences on health spending and also shed some light on the policy structures which governments can adopt to keep health spending in check.

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