Chapter 2

CEIS Health Report 2006
the phenomenon of supply induced demand, which can be summarised in the risk of
inappropriateness, a “supply variable” was introduced, taking as proxy the number of
medical employees (doctors and nurses) per 1000 inhabitants.
1.2.3 Database and model estimated
The model was estimated on a panel of data relating to Italian Regions, obtained from
ISTAT, for the period 1995-2003.
From an econometric point of view, a model with both fixed effects and random effects
was estimated.
The following acronyms were used.
SST = Total health expenditure per capita
SD = % of public expenditure operated directly
% INT_PUB = % public provision out of the total
PIL = GDP per capita
POP_65+ = % of the population aged over 65
M = generic death rate
PD = medical employees per 1000 population
The econometric model estimated is therefore:
SST = f (SD; % INT_PUB; PIL; POP_65+; M; PD) 4 .
Initially the analysis as been performed at national level and subsequently according to
geographical areas, considering the North 5 and the Centre-South separately.
The model chosen for Italy, according to the Hausman test, is the fixed effect one.
This explains 64,32% of the variability in total health expenditure per capita. The time
effect is practically completely explained by the model as demonstrated by the R 2 within
of 95,92%; the effect of variability between Regions is also explained more than satisfactorily
(R 2 between = 81,09%).
Table 1 – Regression coefficients on Italy panel
data 1995-2003
Variables
% direct expenditure
Coefficients P>ItI
of total expenditure -11,128 0,000
% public intervention 6,474 0,000
GDP per capita 0,078 0,000
Percent pop 65+ 50,661 0,002
Generic death rates 29,298 0,125
Medical employee rate 0,636 0,204
Constant -1.123,728 0,000
4 For simplicity, the subscripts for year and Region have been omitted.
5 The “North” includes Toscana, as that Region is organized similarly to other northern Regions.
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