Work detail

Innovation Indicator Analysis in the European Union: A Machine Learning Approach

Author: Bc. Jan Malecha
Year: 2019 - summer
Leaders: Petr Pleticha
Consultants:
Work type: Bachelors
Language: English
Pages: 60
Awards and prizes:
Link: https://is.cuni.cz/webapps/zzp/detail/202930/
Abstract: The European Commission annually publishes a European Innovation Scoreboard (EIS) as
a tool to measure the innovation performance of the EU Member States. This thesis extends the
analysis published in the EIS 2018 in two different manners. The first part, a clustering analysis,
examines the partition of the EU Member States to innovation performance groups. The thesis
comes with a unique scheme of partition created by using hierarchical clustering. A comparison
with the existing scheme shows that the general trends are similar in both schemes. The only
main exception is the differentiation of the British Isles and Luxembourg apart from the other
high performing countries. The proposed scheme provides insight about the within-cluster
similarities, such as the similarity of Finland, Sweden and Denmark and their relative
distinction from France, although they belong to one cluster. The second part, a regression
analysis, attempts to examine the impact of innovations on real labour productivity. Contrary
to existing literature, we do not find a statistically significant relationship between productivity
and the components of the EIS. Additionally, the analysis is extended by the lasso estimation
that provides a variable selection. The latter approach improves our findings and identifies four
EIS indicators with positive impact on labour productivity

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