58
3.5.3. Model Speci? cation I – First Differences
We use OLS regressions to test the existence of a statistically signi? cant relationship
between drug expenditure and political interests. We hypothesise the following relationship:
. (3.1)
Thus we assume that the amount of drug expenditure in year t per insured person
(Expt) is dependent on the age structure of the insurants (Age??t), the basic wage total
per SHI member (BWaget), the share of co-payments on total drug expenditure (CPayt)
and politicians’ interests (PDumt). Since the estimations are based on annual values
(from 1983 to 2004) we presume static relationships. All monetary variables represent
real values de? ated with the pharmaceutical price index (2000 = 100 %). Starting in
1993, the data for the new German Laender are included.
Before we can start the regression analysis, it is ? rst necessary to check the time
series for stationarity, i.e. if their means and variances are constant over time. If the
time series is non-stationary, the problem of spurious regressions arises and OLS results will be inef? cient. Therefore we use the Augmented Dickey-Fuller (Dickey and
Fuller 1979) and the Philips-Perron (Philips and Perron 1988) tests to verify the existence of unit roots. It can be supposed that the time series of drug expenditure, age ratios and total wages are probably trend dependent. Accordingly, a trend variable has
been included into the stationary tests. Only co-payments reveal no consistent time
trend and hence only the constant term has been considered.
Table 3.1: Stationarity Tests.
Augmented
Dickey-Fuller
Philips-Perron
Variable Regression t-Statistic t-Statistic
log (Exp) Level
1st diff erence
c, t
c
-3,2
-4,612***
-2,887
-5,835***
log (Age??) Level
1st diff erence
c, t
c
-0,781
-2,851*
-1,047
-2,811*
log (BWage) Level
1st diff erence
c, t
c
-0,715
-3,247*
-0,715
-3,215*
log (CPay) Level
1st diff erence
c
c
-1,67
-3,944***
-1,67
-4,312***
Note: *** Non-stationarity rejected at a 1% critical level; * at a 5% or 10% critical level. c = constant,
t = trend; t-statistics based on MacKinnon critical t-values.
As table 3.1 shows, it is not possible to reject the existence of a unit root for any of the
time series. However, non-stationarity can be rejected for all variables in ? rst differences. So we can assume that the variables are I(1), i.e. they are integrated of order
one, and follow a random walk.
59
Since the variables are I(1) we run the model in ? rst differences (? indicates differenced values). The theoretical equation is speci? ed in a double-log functional form
including no constant but an error term:
(3.2)
.
3.5.4. Results
The results of the estimated regressions are presented in table 3.2. The age dependency ratio (Age??) and the share of co-payments (CPay) are the only variables which
are highly signi? cant in all regressions. Thus at ? rst glance we can already state that
the ageing population enforces drug consumption whereas higher co-payments entail
demand reductions. For further interpretations we must look at the models in more
detail.
Table 3.2: Regression results.
Variable Model 1
(Basic)
Model 2
(Opportunistic)
Model 3
(Partisan)
Model 4
(Partisan &
Opportunistic)
Model 5
(Partisan &
Opportunistic &
Corporatistic)
Age?? 1,228**
(2,63)
1,049**
(2,135)
1,378***
(3,404)
1,477***
(3,145)
1,466***
(3,29)
BWage 0,921**
(2,651)
0,658
(1,57)
0,44
(1,267)
0,915**
(2,759)
0,906**
(2,828)
CPay -0,184***
(-5,052)
-0,177***
(-4,813)
-0,196***
(-6,193)
-0,198***
(-5,533)
-0,197***
(-5,763)
PDum -0,025
(-1,11)
0,023**
(2,717)
0,014
(1,668)
0,016*
(2,05)
Adj. R2 0,476798 0,483401 0,613737 0,595365 0,622468
Durbin/Watson 1,808 1,838 1,967 2,142 2,362
Note: *** indicates signi? cance at the 1% level, ** at the 5% level, and * at the 10% level.
We start with the basic model 1, which contains no political dummy variable. With less
than 50 %, the value of the coef? cient of determination, the adjusted R2, shows a quite
unsatisfying ? t, which indicates that at least one additional explanatory variable might
be missing. Including opportunistic behaviour (model 2) only marginally improves the
results. Neither the political dummy variable (PDum) nor wages (BWage) are signi? cant. Additionally, the dummy has a negative sign, which is extremely implausible. It
must be assumed that the purely opportunistic model is not adequately speci? ed. In
contrast, all estimations that consider partisan behaviour (models 3, 4 and 5) clearly
deliver improved outcomes: the adjusted R2 rises to around 60 %. Compared to all
other regressions, the purely partisan model 3 provides the highest degree of signi? cance of the dummy variable, but still exhibits problems with the relevance of wages.
Model 4 overcomes this obstacle, but in turn the political dummy loses signi? cance.
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References
Zusammenfassung
Der Arzneimittelsektor der Gesetzlichen Krankenversicherung stand wiederholt im Fokus zahlreicher Gesundheitsreformen. Dennoch ist es bislang nicht gelungen, den Trend steigender Ausgaben nachhaltig zu bremsen. Die vorliegende Untersuchung leistet einen Beitrag dazu, die Ursachen dieser Entwicklung zu erklären und Lösungsansätze aufzuzeigen. Mittels Hauptkomponenten- und Cluster-Analyse wurden Gruppen von Arzneimitteln mit vergleichbaren Konsumeigenschaften gebildet. Jede Gruppe wurde auf den Einfluss der Altersabhängigkeit und des technologischen Fortschritts hin analysiert. Aufbauend auf diesen Ergebnissen wurde eine Prognose der zukünftigen Ausgabenentwicklung bis zum Jahr 2050 erstellt. Obwohl die Hauptkostenfaktoren exogen sind, steht der Gesetzgeber dem vorhergesagten ansteigenden Kostenpfad nicht hilflos gegenüber. Im Gegenteil: Anhand ökonometrischer Tests wird gezeigt, dass die Gesundheitspolitik in der Vergangenheit durch wahl- und klientelorientierte Interessendurchsetzung geprägt war. Mehr Effizienz in der Arzneimittelversorgung könnte durch die Einführung individueller Gesundheitssparkonten erzielt werden. Dies bestätigen die Resultate eines vertikal differenzierten Wettbewerbsmodells.