Fiscal Burden as a Determinant of Innovation Performance in the CEE Countries

This article aims at finding a relationship between the degree of fiscalism and innovation in eleven countries in Central and Eastern Europe (CEE). The article uses clustering algorithms to divide countries into groups, with the most similar features in a multidimensional space generated by values o...

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Bibliographic Details
Published in:East European politics and societies 2023-08, Vol.37 (3), p.1084-1109
Main Authors: Felis, Paweł, Bernardelli, Michał, Jamroży, Marcin, Lipiec, Jacek, Szlęzak-Matusewicz, Joanna, Otczyk, Grzegorz
Format: Article
Language:English
Subjects:
Clustering
Econometrics
Economic policy
Entrepreneurs
Fiscal Politics / Budgeting
GDP
Gross Domestic Product
Indexes
Innovations
Labor costs
National Economy
Panel data
Political economy
Public Administration
Public Finances
Public policy
Socio-Economic Research
Taxation
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Summary:This article aims at finding a relationship between the degree of fiscalism and innovation in eleven countries in Central and Eastern Europe (CEE). The article uses clustering algorithms to divide countries into groups, with the most similar features in a multidimensional space generated by values of adopted indices, irrespective of fiscalism and innovation. Econometric modelling based on panel data was used to identify directions and the strength of the relationship between different variables. The article finds that fiscalism and innovation performance are closely interrelated. The article uses selected (limited) indices to exhibit the relation between fiscalism and innovation. The impact of tax competition may be used to give a broader perspective on this relation. The implication for the public policy is that high value of tax-to-GDP (gross domestic product) coexists with low labour costs and entrepreneurs’ attitude to investment in innovation. It is among the first articles to exhibit a relation between innovation and fiscalism in the CEE countries. In addition, the article uniquely uses clustering methods combined with the econometric panel model.
ISSN:0888-3254
1533-8371
DOI:10.1177/08883254221116802