The Public Sector and its Contribution to the Economy
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Transcript The Public Sector and its Contribution to the Economy
WP8: Factors Influencing Health
Expenditures and Scenarios for
Health Expenditures - EU
Ehsan Khoman and Martin Weale
National Institute
of Economic and
Social Research
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:
THEPC 1GDPPC 2 AGE65_ 74 3 AGE75_ 4 MORTALITY etc (8)
• The coefficient If we assume that health care expenditure is driven
by ageing then, given that β1 is spending for the population aged
under 65, β2 is spending for the population aged between 65 and 74
and β3 is spending for the population aged 75 and over, then total
health spending is given by
11 2 2 3 3
THEPC log
GDPPC constant
1 2 3
(9)
Hypotheses about Determinants of Health
Care Spending
• The effect on the proportion of 65-74 year olds of a change in Π2 is
2
1 2 3
1 3
2
1
(10)
2
2
2
1 2 3 (1 2 3 )
(1 2 3 )
and the effect of the same change given the model of health care
expenditure is
2
THEPC
1
2
11 2 2 3 3 1 2 3
(11)
• So the semi-elasticity with respect to the proportion is given as
2 1 2 3
1
2 2 3 3
THEPC
1 1
(12)
2
1 3
1 2 3
1 2 3
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
THEPC log 1 2 MORTALITY
(13)
then
2
THEPC
exp(THEPC )
MORTALITY THEPC
(14)
• Thus if spending per death (or spending in the last year of life) is
known the associated semi-elasticity can be imposed.
Hypotheses about Determinants of Health
Care Spending
Long-Run Coefficients with Different GD Elasticities
GDPPC
1.078
1.000
1.100
1.200
AGE0_5
0.007
0.005
0.003
0.000
AGE65_74
-0.030
-0.013
-0.015
-0.218
AGE75_
-0.082
-0.056
-0.060
-0.065
AVELE65
0.144
0.113
0.121
0.131
UNEMP
-0.010
-0.021
-0.015
-0.008
ALCCON
-0.023
-0.020
-0.025
-0.030
PUHES
0.005
0.008
0.008
0.008
SALARYGP
0.206
0.208
0.200
0.197
CAPGP
0.271
0.253
0.247
0.246
GLOBALHO
-0.005
-0.006
0.021
0.039
CASEHO
0.000
-0.013
0.014
0.032
COPAYGP
0.025
0.000
0.000
0.000
COPAYHO
-0.112
-0.083
-0.084
-0.085
FREEGP
0.350
0.304
0.316
0.328
FREEHO
-0.012
-0.009
-0.006
-0.003
BEDS
0.018
0.000
0.000
0.000
MORTALITY
0.742
0.717
0.710
0.714
Hypotheses about Determinants of Health
CareDerivation
Spending
of the Restrictions of the Long-Run Values of the Age Terms when Health
Expenditure is Age-Related
Country
Health Expen.
as of GDP
GDP (2003)
Health Expen.
Proportion of Health Expen. by Age
0-64
65-74
75+
Austria
8.65%
226243
19570
0.559
0.149
0.292
Belgium
9.28%
274658
25495
0.515
0.167
0.319
Denmark
8.68%
188500
16363
0.640
0.129
0.231
Finland
6.48%
145938
9463
0.575
0.123
0.301
France
9.58%
1594814
152734
0.550
0.138
0.312
Germany
11.37%
2161500
245799
0.538
0.172
0.290
Greece
10.22%
155543
15892
0.446
0.252
0.302
Eire
7.47%
138941
10378
0.645
0.128
0.227
Italy
8.50%
1335354
113504
0.531
0.187
0.282
Luxem.
5.94%
25607
1522
0.565
0.164
0.270
Nether.
8.71%
476945
41539
0.617
0.140
0.242
Portugal
9.60%
137523
13198
0.619
0.156
0.224
Spain
6.91%
782531
54073
0.531
0.173
0.296
Sweden
9.01%
269548
24292
0.516
0.114
0.370
U.K.
7.76%
1604497
124549
0.423
0.148
0.429
Total
9.10%
9518142
868369
Hypotheses about Determinants of Health
Care Spending
Mortality-Related Costs as a Proportion of Total Health Expenditure
Country
Mortality-related
costs (% of total)
Health Spending
Mortality-related
spending
Austria
24.0%
19579
4699
Belgium
28.0%
25495
7139
Denmark
23.0%
16363
3770
France
43.0%
152734
65675
Germany
25.3%
245799
62070
Italy
28.0%
113504
31781
Netherlands
27.0%
41539
11216
Portugal
36.8%
13198
4853
Spain
45.4%
54073
24529
Sweden
23.0%
24292
5587
706575
221320
EU as a whole
Mortality Rest.
0.313229
Hypotheses about Determinants of Health
Care Spending
Scenarios of Different Restrictions On the Demographic Parameters
Model
GDP Elasticity
AGE65-74
AGE75+
AVELE65
MORTALITY
0
1
0
2
0.313
3
0
0
0
0.313
4
0.009
0.020
0
0
5
0.009
0.020
0
0
6
1
7
1
8
1
9
1
0
0
0
0.313
10
1
0.009
0.020
0
0
11
1
0.009
0.020
0
0
0
0.313
Hypotheses about Determinants of Health
CareLong-Run
Spending
Coefficients with Demographic Restrictions, GDP Elasticity Restricted
Unrest.
Model 0
Model 1
Model 2
Model 3
Model 4
Model 5
GDPPC
1.078
1.095
0.923
1.055
0.202
1.017
1.190
AGE0_5
0.007
0.003
-0.019
-0.046
0.414
-0.011
0.149
AGE65_74
-0.030
-0.015
0.024
0.034
0.000
0.009
0.009
AGE75_
-0.082
-0.060
0.000
0.013
0.000
0.020
0.020
AVELE65
0.144
0.120
0.089
-0.110
0.000
0.039
0.000
UNEMP
-0.010
-0.016
-0.014
0.093
0.254
0.012
0.757
ALCCON
-0.023
-0.025
-0.029
-0.045
0.201
-0.030
0.048
PUHES
0.005
0.008
0.016
0.015
0.197
0.015
0.007
SALARYGP
0.206
0.201
0.277
0.145
0.630
0.236
-0.092
CAPGP
0.271
0.247
0.285
0.185
0.583
0.258
-0.037
GLOBALHO
-0.005
0.020
-0.048
0.228
-3.121
0.125
0.454
CASEHO
0.000
0.013
-0.133
0.104
-4.566
0.037
0.404
COPAYGP
0.025
0.000
0.000
0.000
0.000
0.000
0.000
COPAYHO
-0.112
-0.084
-0.057
-0.132
0.964
-0.090
0.012
FREEGP
0.350
0.316
0.268
0.271
-0.918
0.306
0.352
FREEHO
-0.012
-0.006
-0.033
0.005
-1.184
0.025
-0.176
BEDS
0.018
0.000
0.000
0.000
0.000
0.000
0.000
MORTALITY
0.742
0.710
0.713
0.313
0.313
0.649
0.000
Hypotheses about Determinants of Health
CareLong-Run
Spending
Coefficients with Demographic Restrictions, GDP Elasticity Restricted
Unrest.
Model 6
Model 7
Model 8
Model 9
Model 10
Model 11
GDPPC
1.078
1.000
1.000
1.000
1.000
1.000
1.000
AGE0_5
0.007
0.005
-0.022
-0.045
-1.615
-0.011
0.157
AGE65_74
-0.030
-0.013
0.024
0.036
0.000
0.009
0.009
AGE75_
-0.082
-0.056
0.000
0.015
0.000
0.020
0.020
AVELE65
0.144
0.113
0.093
-0.117
0.000
0.039
0.000
UNEMP
-0.010
-0.021
-0.009
0.088
0.415
0.011
1.015
ALCCON
-0.023
-0.020
-0.034
-0.042
-0.0660
-0.029
0.054
PUHES
0.005
0.008
0.016
0.015
-1.379
0.015
0.001
SALARYGP
0.206
0.208
0.274
0.149
-3.784
0.237
-0.098
CAPGP
0.271
0.253
0.282
0.187
-3.777
0.259
-0.036
GLOBALHO
-0.005
-0.006
-0.028
0.215
0.216
0.119
0.508
CASEHO
0.000
-0.013
-0.117
0.090
0.654
0.032
0.501
COPAYGP
0.025
0.000
0.000
0.000
0.000
0.000
0.000
COPAYHO
-0.112
-0.083
-0.057
-0.132
-0.312
-0.089
-0.062
FREEGP
0.350
0.304
0.276
0.263
0.383
0.304
0.404
FREEHO
-0.012
-0.009
-0.032
0.003
0.294
0.025
-0.178
BEDS
0.018
0.000
0.000
0.000
0.000
0.000
0.000
MORTALITY
0.742
0.717
0.707
0.313
0.313
0.651
0.000
Results
Summary Results of Health Care Projections for the EU15
Country
2003
Value
Model 0
Model 1
Model 6
Model 7
Model 3S
Model 5S
AWG
Scenario
Austria
8.7%
11.52%
19.75%
11.05%
21.12%
11.56%
12.95%
9.5%
Belgium
9.3%
11.32%
15.55%
10.68%
16.68%
10.84%
12.27%
10.9%
Denmark
8.7%
8.87%
11.62%
8.48%
12.28%
9.16%
10.20%
9.6%
France
6.5%
7.11%
8.83%
6.62%
9.52%
5.50%
5.96%
9.6%
Germany
11.4%
13.46%
23.81%
13.16%
25.06%
15.15%
15.83%
9.2%
Italy
8.5%
Netherlands
8.7%
9.22%
16.09%
8.93%
17.08%
10.63%
11.09%
9.9%
Portugal
9.6%
9.72%
19.56%
7.95%
24.40%
12.99%
20.00%
10.4%
Spain
6.9%
8.61%
16.84%
8.50%
17.71%
10.27%
12.32%
13.0%
Sweden
9.0%
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%
10.3%
Results
Classifications of Model and AWG Projections
More than 1% point
above AWG
Within 1% point of AWG
More than 1% point
below AWG
Model 0
Austria, Germany
Belgium, Denmark, Eire,
Netherlands, Portugal
Finland, France, Spain,
Sweden, U.K.
Model 3S: Mortality-related Costs
Austria, Belgium,
Germany, Portugal
Denmark, Netherlands
Finland, Eire, Spain,
Sweden, U.K.
Model 5S: Age-related Costs
Austria, Belgium,
Germany, Netherlands,
Portugal
Denmark, France, Eire,
Spain
Finland, Sweden, U.K.
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.