Flat of the Curve Medicine – A New Perspective
Johannes Schoder and Peter Zweifel*
---Preliminary version---
May 28, 2008
Abstract:
Until now, economists have solely focused on the determinants of the expected value of
health status as a function of medical inputs on one side and nonmedical inputs on the
other. However, this study suggests that both medical and nonmedical inputs may have an
additional benefit, viz. enabling individuals to better stabilize their health status. The
hypothesis is tested empirically using OECD health data for 24 countries between 1970
and 2004. First evidence suggests that a one percent increase of medical and nonmedical
factors contribute to a reduction of the variability of life expectancy, reflected in the Gini
coefficient of the distribution of length of life. Surprisingly, the effect of nonmedical
factors has been somehow higher than the effect of medical factors on the Gini coefficient,
around 0.08 percent (death numbers are 0.08 percent more concentrated compared to 0.06
percent).
*University of Zurich, Socioeconomic Institute, Hottingerstr. 10, CH-8032 ...Zurich. Phone: +41 44
6340614, email: johannes.schoder@soi.uzh.ch (J. Schoder), pzweifel@soi.uzh.ch (P. Zweifel).
1. Introduction and motivation
Industrialized countries have spent a rising share of total economic resources on
healthcare. Healthcare expenditure (HCE) in percent of GDP increased from 3.8 percent
in 1960 to 8.9 percent in 2004 for the OECD countries. Over the same period, health has
improved. The average life expectancy of the OECD countries increased from 68.4 in 1960
to 78.5 years in 2004. However, the increase of life expectancy slowed down during the
past years. E.g. in the U.S,. the annual growth rate of life expectancy slowed down to less
than 0.19 percent between 1980 and 2004 compared to an annual growth rate of 0.3
percent between 1960 and 1980. Since HCE increased at an annual growth rate of 7.7
percent between 1980 and 1998 this is often interpreted that modern medicine has to fight
against decreasing marginal returns (‘flat of the curve medicine’).
However, the literature provides further explanations for this observation (see ZWEIFEL
and BREYER, 1997). Maybe individuals reduced health enhancing efforts or changed their
preferences over time in favor for unhealthy products therefore countervailing the positive
effect of medical care on life expectancy. Another explanation may be that especially for an
aging population the effects of medicine on prolonging life might be limited although
productivity in relative terms (compared to nonmedical inputs) might be high. Finally, the
choice of output indicator is of crucial importance. Since neither life expectancy nor
mortality rates provide direct information on health status the real effects of medical inputs
are not captured entirely and therefore their effect may be over- or underestimated.
The following study is devoted to the latter explanation. Constructing a variable that
reflects the uncertainty surrounding individuals health status we investigate whether
medical factors generate an additional benefit, viz. reducing the fluctuation of one’s health
capital stock. The remainder of this paper is organized as follows. In section 2, a review of
the relevant literature is provided. Section 3 is devoted to econometric specification, a
description of the data base, and variable selection. The new empirical evidence is
presented in Section 4. Section 5 contains a discussion and summery of our findings.
2
2. Survey of the literature
2.1 Effect of healthcare on different measures of health status
At the aggregate level the choice of output variable in a production function of health is
limited by the availability of statistical data. Traditionally, studies have focused on the
influence of medical care and other ‘inputs’ on mortality rates or life expectancy as a proxy
for the healthcare status.
The seminal article continues to be AUSTER, LEVESON and SARACHEK (1969), who related
age- and sex-adjusted mortality rates of U.S. states of 1960 to medical inputs (viz. number
of physicians, pharmaceutical outlay, capital stock of hospitals and medical auxiliary staff),
economic factors (income, years of schooling and degree of urbanization), factors related
to consumption (alcohol consumption, smoking) and organizational factors (share of group
practices and medical schools). Schooling and income was negatively related to mortality,
but both variables were not significantly different from zero. Only medical auxiliary staff
was reducing mortality rates. The variable number of physicians even increased the
mortality rate. However, the effect of this variable maybe due to reverse causality because
physicians tend to work in areas where people are exposed to an increased risk of death.
Controlling for reverse causality the authors performed a two-stage least squares
estimation. Medical inputs now got the expected sign but all effects were not significant
from zero.
THORNTON (2002) refined the approach of AUSTER, LEVESON and SARACHEK (1969) using
U.S. data from 1990. He chose HCE as the only medical input variable and included
additional health-related factors such as married households, race and crime rates. Instead
of choosing years of schooling he included the share of individuals with higher education.
In addition, he treated HCE and income as endogenous variables. HCE, income,
education, married households, and urbanization had a negative but not significant impact
on mortality. Only education and married household were significantly different from zero.
Cigarette and alcohol consumption, the crime rate and the degree of manufacturing had an
positive effect on mortality whereas only the first two factors were significant. In all, he
confirmed the flat of the curve medicine hypotheses
On the other side, studies based on OECD health data provide some evidence on the
effect of medical inputs on the production of health. In a cross-section study MILLER and
FRECH (2000) investigated the production of health in 21 industrialized countries for the
3
year 1996 using OECD health data. They related life expectancy at birth, at age 40 and age
60 to pharmaceutical and non-pharmaceutical healthcare outlays, cigarette and alcohol
consumption, animal fat consumption, and the share of women. They found evidence on
the effectiveness of medical inputs on life expectancy. A doubling of pharmaceutical
expenditure results in a 1.7 percent increase of life expectancy at the age of 40 and 4
percent at the age of 60. SHAW, HORRACE and VOGEL (2005) confirmed the results of
MILLER and FRECH (2000) using a panel of 29 countries between 1960 and 1999 based on
OECD health data from 2000.
However, the uncertainty surrounding the expected level of life expectancy or point of
death has not been analyzed to our knowledge. The following studies provide preliminary
evidence that in industrialized countries both medical and nonmedical inputs may have an
additional benefit, viz. enabling individuals to better control their health status. Negelcting
this dimension may under- or overestimate the effect of medical inputs on the production
of health.
2.2 Evidence on increased control over health status
HELIGMAN and POLLARD (1980) analyzed Australia’s age-specific mortality and its
development over time. They split this mortality into components due to infant mortality,
excess mortality among young adults, and a ‘pure’ age-related factor. They identified a
variance parameter in the infant mortality and traced the development of this parameter
over time. This method was also applied by the FEDERAL STATISTICAL OFFICE (1996) to
Swiss data. The result is that the variance parameter decreased more strongly from 1881 to
1993 than did general age-related mortality. One can therefore infer that uncertainty
reduction over time may have developed differently from expected health status.
Disparities at the aggregate level can be interpreted as a reflection of underlying individual
variance in health status. Analyzing Swiss data, BOPP and GUTZWILLER (1999) find
considerable differences in standardized mortality rates between cantons and regions.
While these disparities have not changed much over time, relative rankings of cantons did.
Another aggregate indicator of health status variability is excess mortality of certain
professional groups. E.g., after controlling for age and sex, Swiss construction workers are
estimated to have a rate of mortality due to respiratory disease that exceeds the average by
56 percent, followed by farmers with 31 percent (GASS and BOPP, 1997).
4
GERDTHAM and JOHANNESSON (2000) relate individual disparities of life expectancy and
quality-adjusted life years (QALYs) to income disparities and age groups in Sweden. The
difference in QALYs between the highest and the lowest income decile turns out to be
marked, up to 3.6 years for the age group of 20 to 29. This confirms that income
differences are associated with differences in healthy life years (expected value reduction
hypothesis). However, the evidence does not show that e.g. higher income results in a
decrease of health status uncertainty, reflected by a decrease in variance of QALYs between
individuals (variance reduction hypothesis). Also with higher age, disparities in QALYs
among individuals of the same age group are still low but increase markedly compared to
younger individuals. According to this study, variability in life years therefore tends to rise
with age and decreases with income. To what extent these results are affected by the
availability of high-quality medical care remains to be seen.
SHKOLNIKOV, ANDREEV and BEGUN (2003) use life table information from several
industrialized countries to estimate the difference in longevity characterizing individuals of
a given country or region. In analogy to applications to income distribution, they favor the
so-called Gini coefficient, which represents the concentration of the distribution of life
years. The lower the Gini coefficient, the more equal this distribution, which implies that
death must be heavily concentrated (among the aged) within a given population.
In the following we extent the work of SHKOLNIKOV, ANDREEV and BEGUN (2003) in
analyzing the factors driving the decrease of the Gini coefficient. First we calculate the Gini
coefficient for 24 OECD countries. Then we relate the development of the Gini coefficient
over time to medical and nonmedical inputs. Section 3 and 4 are devoted to the
specification and estimation of this model.
5
3. Measuring inequality of the distribution of length of life
Usually the Gini coefficient is used for the analysis of inequality in income distribution.
Following Hanada (1983) we show how the standard concept can be applied to the
distribution of length of life.
The Gini coefficent is defined as an area between the diagonal and the Lorenz curve,
divided by the whole area below the diagonal is based on the Lorenz curve. The Lorenz
curve in turn, represents the cumulative income share as a function of the cumulative
population share. Let f(x) be a population-density function of income x. Then the
cumulative share of the population with income less or equal to x is,
x
F ( x) = ∫ f ( y )dy , where y = income
(1)
0
and the share of the total income received by this part of the population is,
Φ( x) =
1
µ
x
∞
0
0
∫ yf ( y)dy , where µ = ∫ yf ( y)dy
(2)
The Lorenz curve as a function varies from 0 to 1 and is defined on the interval of
variation of F(x) values [0,1]. In a situation of perfect equality for any income x Φ(x) =
F(x), the Lorenz curve is simply a diagonal, connecting points (0,0) and (1,1). The higher
the variability in income across a population, the greater the divergence between the
diagonal and the Lorenz curve. Following the definition of the Gini coefficient above we
receive,
1
G0 = 1 − 2∫ Φ ( p )dp , where p = F(x)
(3)
0
Applying this framework to mortality-by-age schedules, one can imagine a person’s years
lived from birth to death to be ‘income’ and cumulative death numbers to be ‘population’.
The density and the distribution functions can be redefined as,
f ( x) = d ( x) / l (0) , where d(x) = number f death, l(0) = number of survivors of year 0
(4)
F ( x) = 1 − l ( x) / l (0) , where l(x) = number of survivors year x
(5)
And therefore we receive,
6
x
Φ( x) =
1
td (t )dt , where e(0) = life expectancy at birth
e(0)l (0) ∫0
(6)
Using (4) to (6) together with (3) we finally receive the Gini coefficient for the distribution
of length of life,
G0 = 1 −
∞
1
⋅ [l ( x)]2 dx
e(0)[l (0)] 2 ∫0
(7)
The Gini coefficient varies between the limits of 0 (perfect equality) and 1 (perfect
inequality). For a length of life table distribution it is equal to zero if all individuals die at
the same age, and equal to 1 if all people die at age 0 and one individual dies at an infinitely
old age. The mortality by age schedules are obtained from the Human Mortality Database.
Using equation (4) we calculated Gini coefficients for 24 countries between 1960 and 2004.
Table 1 shows the development of the Gini coefficients over time. Obviously, they tend to
decrease over time. The highest effect can be found for Portugal. The Gini coefficient
decreased from 0.21 in 1960 to 0.1 in 2003, confirming our presumption that death became
heavily concentrated among the aged. In countries like Sweden or Norway the decrease has
been less distinct. However, if one considers life table information earliest available
decrease has been substantial. The Gini coefficient declined from 0.36 in Sweden and 0.39
in Norway down to around 0.09 in both countries.
Table 1: Development of the Gini coefficient over time for 24 countries
country
Austria
Australia
Belgium
Canada
Suisse
CZ
Germany
Denmark
Finland
France
Hungary
Iceland
Italy
Japan
LUX
1960
0.1478
0.1315
0.1368
0.1388
0.1144
0.1264
0.1377
0.1211
0.1337
0.1377
0.1524
0.1185
0.1528
0.1509
0.1401
1970
0.1332
0.1294
0.127
0.1311
0.1105
0.1281
0.1292
0.1183
0.1239
0.1292
0.1403
0.1185
0.1325
0.1186
0.1362
1980
0.1199
0.1161
0.116
0.1181
0.1048
0.1193
0.1196
0.1142
0.112
0.1196
0.1351
0.1099
0.1135
0.1015
0.1153
7
1990
0.1068
0.1061
0.1066
0.1067
0.0982
0.1166
0.1116
0.111
0.1098
0.1116
0.1364
0.1045
0.1034
0.0942
0.1096
2003
0.0956
0.0941
0.0973
0.097
0.0878
0.102
0.1008
0.0998
0.0971
0.1008
0.12
0.0828
0.0895
0.0923
0.0961
NL
Norway
NZ
Portugal
Slovak
Spain
Sweden
UK
US
0.1162
0.1188
0.1243
0.2071
0.1356
0.1542
0.1144
0.1255
0.1438
0.1143
0.1127
0.1242
0.1749
0.1351
0.1302
0.1105
0.1213
0.1409
0.1063
0.106
0.1176
0.134
0.1278
0.1099
0.1048
0.1127
0.1264
0.1002
0.1047
0.1125
0.1181
0.1255
0.1071
0.0982
0.1053
0.1204
0.0925
0.0918
0.0934
0.1013
0.1127
0.0942
0.0878
0.0964
0.1128
4. Econometric Model, Estimation Methods, and Data Base
Using macro-panel data one has to decide between different methods of estimating the
model. In general panel models are of the following form
yit = xit β + ci + u it
(8)
where β measures the partial effect of xit in period t for country i. The most fundamental
idea of modeling panel data is to view the unobserved factors affecting the dependent
variable yit as consisting of two types. Those that are constant over time but specific to the
individual cross sectional unit (here the country) ci, and those that vary over time uit.. One
may simply estimate equation (8) with pooled ordinary least squares (OLS), treating
vit=ci+uit as the composite error. However, to receive consistent estimators the exogenous
variables xit have to be uncorrelated with the error term uit and the country specific effect ci.
Even if there is no correlation pooled OLS exibits serial correlation, since ci is fixed over
time (Boes, 2008).
Less restrictive asumptions are required for the estimation of β using the FE (fixed effects)
and RE (random effects) model. In the FE model one includes a dummy for each country
characterizing their heterogeneity. These dummys guard against the bias discussed above.
But instead of explicitly using dummy variables for each country, FE is applied on
transformed data. The data is transformed by subtracting for each country the averages of
all the observations for that country. Applying OLS on the transformed data delivers the
desired estimator. For the FE model it is possible that the unobserved country specific
effect ci, and and the observed explanatory variables xit are corrleated. However, for the RE
model we need the country specific effect ci to be uncorrleated with the xit. The country
specific effects are treated as random. Using the RE model one can avoid the loss of
observation due to the implicit use of dummy variables in the FE model. Therefore the RE
8
model produces more efficient estimator than the FE model. Furthermore, the technique
used for the RE estimation procedure does not wipe out the explanatory variables that are
time invariant (ci).
Using a log-log functional form (in analogy to MILLER and FRECH, 2000) we receive the
following specification which will be estimated with the methods described above,
giniit = hce / gdpit β 1 + hospbed it β 2 + gdp it β 3 + pop 65 it β 4 + tabacit β 5 + bmiit β 6 + ci + u it (9)
The variables are defined as follows,
•
GINI: Gini coefficient of the distribution of length of life
•
HCE/GDP: HCE in percent of GDP
•
HOSPBED: Number of beds per 1000 inhabitants
•
GDP: GDP per capita in purchasing power parity
•
POP65: Percent of population over 65
•
SMOKERS: Regular smokers in percent of the population
•
BMI: Percent of population having a body mass index (bmi) higher than 25kg/m2
Since the present study focuses on industrialized countries, data is obtained from the
OECD health data base 2007 (for the exogenous variables) and the Human Mortality data
base 2008 (for the endogenous variable). However, the OECD health data is known for
some difficulties. One of them is national differences with regard to the delimitation of the
healthcare sector, resulting in different baskets of benefits, another, the lack of
comparability and precision of healthcare deflators. In view of the second difficulty HCE
as percent of GDP is used, whereas the first difficulty cannot be avoided. Spanning the
years 1970 to 2004 and including 30 countries it results in a macro-panel with 1,020
observations. Due to limitations of the human mortality data base and missing values in the
OECD health data base six countries had to be excluded (viz. Greece, Ireland, Mexico,
Poland, South Korea, and Turkey) resulting in a maximum of 417 observations. The
descriptive statistics is summarized in table 2. The variable of interest, the Gini coefficient
of the distribution of length of life, varies around 0.226 and 0.083 and already indicates a
high level of concentration with a mean of 0.115. Healthcare expenditure in percent of
GDP represents the medical input variable. It should be expected that the higher the
9
healthcare expenditure the more concentrated the death numbers should be due to
improved control over health status. However, we have to consider reverse causality. High
spending of healthcare expenditure can also be a result of high variability of death numbers
over time. As can be seen in table 2 the average share of healthcare expenditure for the
OECD countries is around 7,12 percent over the time period of 1970 to 2004. The
standard deviation of 2.04 percent is divided into the within standard deviation and
between standard deviation. Here the within deviation is higher than the between standard
deviation, suggesting that the variation in healthcare expenditure as a percent of GDP is
higher for one country followed over time than the variation between the countries to a
given point of time.
Table 2: Descriptive Statistics
Variable
GINI
overall
between
within
Mean
Std. Dev.
Min
Max
Observations
0.11521
0.016
0.010
0.013
0.083
0.101
0.072
0.226
0.140
0.202
N=1112
N=24
T=46.3
HCE/GDP overall
between
within
7.116
2.040
1.200
1.650
1.500
5.272
2.244
15.300
9.791
12.625
N = 852
n=
24
T = 35.5
HOSPBED overall
between
within
6.926
2.526
2.463
1.309
3.200
4.150
2.732
15.600
14.838
10.731
N = 456
n=
21
T = 21.714
GDP
overall
between
within
13740.72
10348.88
2668.054
10012.53
728
8177.739
-5596.757
70600
21910.48
62430.24
N = 999
n=
24
T = 41.625
POP65
overall
between
within
12.495
2.712
1.887
1.971
5.700
9.893
7.334
20.000
15.787
21.634
N = 1078
n=
24
T = 44.917
SMOKERS overall
between
within
32.439
9.050
6.120
6.290
15.9
19
14.753
61
43.686
53.440
N = 439
n=
24
T = 18.292
BMI
40.658
10.869
7.891
18.600
21.841
66.300
59.980
N=
n=
overall
between
10
185
24
4.140
within
21.893
52.318
T = 7.708
Another medical input variable is reflected in the number of beds per 1,000 inhabitants.
The higher the density of hospitals the faster individuals should be treated and the lower
should be the mortality rates. The average of hospital beds per one thousand inhabitants is
around 6.9. Nonmedical factors are included in the variable GDP per capita and percent of
population over 65. GDP should contribute to a reduction of variability whereas a higher
share of aged people should increase variability of life expectancy. The overall average
GDP per capita is 13,740 with a high standard deviation of 10,348. The standard deviation
is mainly due to the variation of GDP over time. The average population over 65 in our
sample is around 12.5 percent. Behavioural risk factors are reflected in the variables
SMOKERS
and BMI. Unhealthy people are supposed to die earlier and therefore both
variables should increase the variability of the point of death.
In the following we estimate our model with pooled OLS, FE and RE. The Hausman
(1978) test is applied to select the preferred model.
5. Estimation results and discussion
Following a general to specific approach (CAMPOS, ERICSSON and HENDRY 2005) we first
estimate equation (1) including all variables with pooled OLS, FE and RE. The results are
shown in Table 3.
Table 3: Determinants of the Gini coefficient for 24 countries between 1970 and 2006a)
COEFFICIENT a)
HCE/GDP
HOSPBED
GDP
POP65
SMOKERS
BMI
Constant
Observations
Pooled OLS
0.176***
(0.0370)
0.0946***
(0.0198)
-0.129***
(0.0218)
-0.118***
(0.0359)
-0.00885
(0.0362)
0.175***
(0.0379)
-1.848***
(0.377)
103
FE
0.00767
(0.0267)
0.147***
(0.0247)
-0.0693***
(0.0184)
-0.0257
(0.0299)
-0.0343
(0.0268)
-0.0426
(0.0335)
-1.549***
(0.246)
103
11
RE
0.0243
(0.0274)
0.106***
(0.0222)
-0.107***
(0.0163)
-0.0581*
(0.0301)
-0.0392
(0.0271)
0.0113
(0.0313)
-1.211***
(0.233)
103
R-squared
0.653
Number of v1
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
a)
Natural logarithm
0.024
20
0.189
20
Due to missing values the number of observations is reduced to only 103 observations.
The overall fit indicated by the overall R2 of the estimated models is high for pooled OLS
but low for FE and RE. In the following the results are discussed in detail.
In all three models HOSPBED and GDP are significant on the one percent level. However
only the variable GDP has the expected negative sign in every model. A one percent
increase of GDP e.g. reduces the Gini coefficient about 0.07 per cent in the FE estimation
meaning that more people die around one certain point of time. A one percent increase of
the number of hospital beds increase the Gini coefficient by around 0.1 percent in all three
models. Surprisingly the number of hospital beds increase the Gini coefficient implying
that less people die at the same point of time. The effect of the variable HCE/GDP points
into the same direction, but the values are only significant for the pooled OLS estimation.
Here a one percent increase of the share of healthcare expenditure increases the Gini
coefficient about 0.2 percent.
Only one of the two behavioural factors has the expected sign and is significant. A one
percent increase of the share of obese individuals increases the Gini coefficient around 0.18
percent in the pooled OLS model. This effect is considerable high compared to the effects
of the other variables. However, the effect of the variable BMI fades away in the FE and
RE estimation.
Since the assumptions for the pooled OLS estimation are too restrictive (see above) we
considered only FE and RE for further specification. The Hausman test is applied to test
whether the individual effects are correlated with the regressors. In this way it provides
information about which model, RE or FE, is more suitable for the further estimation. Its
basic idea is that under the null hypothesis of zero correlation, the RE estimator is
consistent and efficient. Under the alternative, the RE estimator is inconsistent. The FE
estimator is consistent under both alternatives, but inefficient under Ho The test statistics
are presented in the table below.
12
Table 4: Hausman test
---- Coefficientsa) ---(b)
(B)
fxd
rnd
HCE/GDP .0434927
.0536858
HOSPBED
.1230723 .0854148
GDP
-.0796382 -.1085391
POP65
-.0350072 -.0569391
TABAC
-.0321573 -.0238801
BMI
-.0559412 .0007109
(b-B)
sqrt(diag(V_b-V_B))
Difference
S.E.
-.0101931
.
.0376575
.0072914
.0289009
.0040858
.0219319
.
-.0082772
.
-.0566521
.0080816
b = consistent under Ho and Ha; obtained from xtreg
B=inconsistent under Ha, efficient under Ho; obtained from xtreg
Test: Ho:
difference in coefficients not systematic
chi2(6) = (b-B)'[(V_b-V_B)^(-1)](b-B)
=
23.32
Prob>chi2 = 0.0007 (V_b-V_B is not positive definite)
a)
Natural logarithm
Ho can be rejected on the 1 percent significance level suggesting that the individual effects
are correlated with the explanatory variables. This implies that the RE model does not give
us consistent estimators and therefore we take the FE model for further estimation. To
sum up the obtained results above differ depending on which model is used for estimation.
Furthermore the explanatory variables have only partially the expected sign.
To increase the number of observations and to test the sensitivity of our estimated model
we exclude stepwise several explanatory variables. Moreover we only take the FE
estimation, since the Hausman test prefers FE over RE estimation. Including different
explanatory variables we receive the following results shown below.
Table 5: Different specifications of our model estimated with FE
COEFFICIENTa)
HCE/GDP
HOSPBED
GDP
POP65
SMOKERS
(1)
0.00767
(0.0267)
0.147***
(0.0247)
-0.0693***
(0.0184)
-0.0257
(0.0299)
-0.0343
(2)
-0.103***
(0.0186)
0.141***
(0.0221)
-0.0381***
(0.0108)
-0.0428
(0.0283)
-0.0312
13
(3)
-0.0806***
(0.0132)
0.121***
(0.0122)
-0.0348***
(0.00737)
-0.119***
(0.0202)
(4)
-0.0954***
(0.0134)
0.125***
(0.0125)
-0.0493***
(0.00720)
(5)
-0.0559***
(0.0151)
-0.0781***
(0.00524)
0.0905***
BMI
Constant
Observations
Number of v1
R-squared
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
a)
Natural logarithm
(0.0268)
-0.0426
(0.0335)
-1.549***
(0.246)
103
20
0.878
(0.0216)
-1.697***
(0.148)
217
21
0.084
(0.0132)
-1.641***
(0.0787)
398
21
0.323
-1.788***
(0.0765)
402
21
0.251
-1.693***
(0.0799)
417
24
0.463
The first specification is equivalent to the FE model estimated in table 4. The second
specification excludes the variable with the least number of observations, the BMI. The
number of observations more than doubles up to 217 observations. Interestingly, the
variable healthcare expenditure has the expected significant negative effect. The value of
the variable HOSPBED is similar to the first specification and still has the positive impact on
the Gini coefficient. The effect of GDP reduces to 0.03 but is still significant with the
expected negative sign. The variables POP65 and SMOKERS are not significant as in the first
specification.
In the third specification we dropped the variable
SMOKERS
again leading again to a nearly
doubling of the sample size. Now the most important change can be found in the variable
POP65. A one percent increase of the share of people above 65 leads to 0.12 percent
decrease of the Gini coefficient. There is no significant change when POP65 is exluded in
the fourth specification.
Finally, the last specification focuses just on the basic determinants of the variability of life
expectancy. It includes healthcare expenditure as the medical input variable, the share of
regular SMOKERS as the socioeconomic variable, and the GDP as the nonmedical
variable. All variables have the expected sign and are significant on the one percent level.
This model provides evidence in favour of our hypotheses stated at the beginning, viz. that
medical factors as well as nonmedical factors contribute to a decrease of volatility of life
expectancy. The impact of the nonmedical factors is even higher than the impact of the
medical factors. However, the fit of the model is subject to further econometric inquiery.
14
5. Conclusion
To be written
15
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17