<abstract xmlns="http://www.w3.org/1999/xhtml"><bold>Research background:</bold> The article attempts to include the accuracy of statistical data in a synthetic evaluation and classification of EU countries in terms of innovation.<bold>Purpose:</bold> The aim of the article is to evaluate an influence of the accuracy of statistical data on a classification of EU countries in terms of innovation.<bold>Research methodology:</bold> The research employed diagnostic variables determining the innovation of EU countries and a methodology proposed by the European Commission in the European Innovation Scoreboard 2019. The influence of the uncertainty of the measurement of the diagnostic variables on the Summary Innovation Index of EU countries was evaluated. In order to do this, a procedure employing the Monte Carlo method was proposed.<bold>Results:</bold> Taking into account the uncertainty of the measurement of variables in the evaluation of the innovation of EU countries resulted in qualifying one of the countries to another innovation group.<bold>Novelty:</bold> The article draws attention to an important but often neglected problem related to the accuracy of statistical data used in research, and the evaluation of their influence on the calculation of a value of synthetic measure (based on the innovation of EU countries).</abstract>

Research background: The article attempts to include the accuracy of statistical data in a synthetic evaluation and classification of EU countries in terms of innovation.Purpose: The aim of the article is to evaluate an influence of the accuracy of statistical data on a classification of EU countries in terms of innovation.Research methodology: The research employed diagnostic variables determining the innovation of EU countries and a methodology proposed by the European Commission in the European Innovation Scoreboard 2019. The influence of the uncertainty of the measurement of the diagnostic variables on the Summary Innovation Index of EU countries was evaluated. In order to do this, a procedure employing the Monte Carlo method was proposed.Results: Taking into account the uncertainty of the measurement of variables in the evaluation of the innovation of EU countries resulted in qualifying one of the countries to another innovation group.Novelty: The article draws attention to an important but often neglected problem related to the accuracy of statistical data used in research, and the evaluation of their influence on the calculation of a value of synthetic measure (based on the innovation of EU countries).

The Influence of the Accuracy of Statistical Data on the Results of a Classification of Eu Countries in Terms of Innovation

Department of Quantitative Methods and Economic Informatics

Folia Oeconomica Stetinensia

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Research background: The article attempts to include the accuracy of statistical data in a synthetic evaluation and classification of EU countries in terms of...

The Influence of the Accuracy of Statistical Data on the Results of a Classification of Eu Countries in Terms of Innovation

Online veröffentlicht: 20. Aug. 2020

Seitenbereich: 408 - 420

Eingereicht: 30. Okt. 2019

Akzeptiert: 19. März 2020

DOI: https://doi.org/10.2478/foli-2020-0024

Schlüsselwörter
innovation of EU countries, Summary Innovation Index, accuracy of statistical data, uncertainty of statistical data, the Monte Carlo method

© 2020 Małgorzata Stec, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The Influence of the Accuracy of Statistical Data on the Results of a Classification of Eu Countries in Terms of Innovation

Online veröffentlicht: 20. Aug. 2020

Seitenbereich: 408 - 420

Eingereicht: 30. Okt. 2019

Akzeptiert: 19. März 2020

DOI: https://doi.org/10.2478/foli-2020-0024

Schlüsselwörterinnovation of EU countries, Summary Innovation Index, accuracy of statistical data, uncertainty of statistical data, the Monte Carlo method

© 2020 Małgorzata Stec, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
innovation of EU countries, Summary Innovation Index, accuracy of statistical data, uncertainty of statistical data, the Monte Carlo method