The Position of the CR Among the EU States Based on Selected Measures of the Lisbon Strategy
Vostrá Hana, Jindrová Andrea, Dömeová Ludmila
Klíčová slova
European Union, Lisbon Strateg y, structural indicators, multiple dimensional statistical methods,
method of average distance from a fictive goal.
Abstrakt:
The Lisbon Strategy is a key document of the EU, which deals with the priorities and measures
for the stimulation of economic, social and ecological renewal of member states in the period
2000-2010. There are more than 130 structural measures from the fields of national development,
employment, research and innovations, economic reforms, social coherence and living
environment, which can be used for the evaluation and quantification as well as the modernization
of European economics and the improvement of its global competitiveness. The
structural measures for international comparisons have been developed by Eurostat and they
are used by the European Council as the basic data for the evaluation of the level of attainments
of the goals of the Lisbon Strategy. In the last few years, a list of 15 key indicators has
been developed. They are: GDP per head in parity of purchasing capacity, labour productivity
in parity of purchasing capacity, employment rate, employment rate of women, employment
rate of elderly people, number of persons with secondary school certificate from the ages
between 20 and 24, science and research expenditures as a share of GDP, investments as a
share of GDP, price level, exposure to poverty, level of long term unemployment, emission
of glasshouse effect, energy consumption as a share of GDP, goods traffic as a share of GDP
and regional dispersion of the unemployment rate. The goal of this paper is to propose some
methodological approaches that are suitable for analyzing these indicators. The proposal for
a correct methodology is highly complicated due to the miscellanea of the variables involved.
The multiple dimensional statistical methods seem to be useful especially because of its ability
to evaluate complex sets of variables.
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