Content

Sascha G. Wolf, Results in:

Sascha G. Wolf

Pharmaceutical Expenditure in Germany, page 59 - 60

Future Development, Political Influence and Economic Impact

1. Edition 2009, ISBN print: 978-3-8329-4164-2, ISBN online: 978-3-8452-2005-5 https://doi.org/10.5771/9783845220055

Series: Neue Studien zur Politischen Ökonomie, vol. 6

Bibliographic information
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. 60 Model 5, which combines opportunistic, partisan and corporatistic behaviour, delivers the best results not only in respect to the adjusted, but also regarding the signi? cance of the exogenous variables. To recapitulate, we found an indication for partisan politics as well as for the combination of partisan, opportunistic and corporatistic behaviour. Furthermore, the age structure of the population and co-payments of the insurants obviously in? uence drug expenditure, whereas the effects of income on expenditure development are ambiguous. 3.5.5. Model Speci? cation II: Error Correction Model (ECM) Taking ? rst differences to correct for non-stationarity leads to a loss of valuable information, especially where a long-run trend in the variables is concerned. To overcome this problem and as an additional test of signi? cance, we also estimated an ECM. Beforehand, examining whether the variables are co-integrated, i.e. that there exists at least one linear combination that is integrated of order zero (I(0)), is recommended.47 If the variables are co-integrated, then the non-stationarity in the variables cancel each other out and it is possible to apply an ECM. Since we have more than two variables, we use Johansen’s method to test for cointegration (Johansen 1988). Table 3.3: Johansen Cointegration Test. Maximum Eigenvalue Statistics Trace Statistics None 36,521 (27,584)* 60,53204 (47,856)** At most 1 15,701 (21,132) 24,01055 (29,797) At most 2 6,827 (14,265) 8,309441 (15,495) At most 3 1,483 (3,841) 1,482590 (3,841) Note: The values in parentheses show the 5% critical value. Trend assumption: linear deterministic trend (unrestricted). *Maximum-Eigenvalue test indicates 1 cointegrating equation at the 5% level. **Trace test indicates 1 cointegrating equation at the 5% level. Both the maximum eigenvalue and the trace test indicate that there exists a single cointegration relationship.48 Thus we should apply the Engle-Granger two-step method for specifying an ECM.49 47 For more on cointegration, see Engle and Granger (1988). 48 Since the Johansen Cointegration Test indicates a single cointegration relationship, the residuals of the OLS regression have also been tested for a unit root (Engle and Granger 1987). The Augmented Dickey-Fuller as well as the Phillips-Perron test con? rms the existence of a cointegration relationship. 49 Whenever there exists a single cointegration relationship, the Engle-Granger two-step method delivers more robust results than the Johansen one-step method (Kennedy 2003, p. 340).

Chapter Preview

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.