On the robustness of composite indicators: evidence from the Global Innovation Index
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2023-12-18
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Universidad de Deusto
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This dissertation introduces a methodology to assess the robustness of the Global Innovation Index (GII), by comparing the rankings provided in it with those achieved using alternative data-driven methodologies such as Data Envelopment Analysis (DEA) and Principal Component Analysis (PCA), using the data provided by the 2019 edition of the GII. With it, the dissertation aims to reduce the level of subjectivity in the construction of composite indicators (CIs) concerning weight generation and indicator aggregation. The dissertation relies on PCA as a weighting-aggregation scheme to reproduce the 21 sub-pillars of the GII before the application of the DEA Benefit-Of-the-Doubt approach to calculate the relative efficiency score for every country. By using the PCA-DEA model, a final ranking is produced for all countries. The paper uses Random Forests (RF) classification to examine the robustness of the new rank. The comparison between our rank and that of the GII suggests that the countries positioned at the top or the bottom of the GII rank are less sensitive toward the modification than those in the middle of the GII, the rank of which is not robust against the modification of the construction method. The PCA-DEA model introduced in this dissertation provides policymakers with an effective tool to monitor the performance of national innovation policies from the perspective of the relative performance of their respective countries.
Moreover, the dissertation employs a multi-dimensional innovation-driven clustering methodology to analyze the data provided by the 2019 edition of the GII. The K-means Cluster Analysis technique is applied to uncover and analyze distinct innovation patterns. As a result, it classifies 129 countries into four clusters: Specials, Advanced, Intermediates, and Primitives.
Each cluster exhibits strengths and weaknesses in terms of innovation performance. Specials excel in the areas of institutions and knowledge commercialization, while the Advanced cluster demonstrates strengths in education and ICT-related services but shows weakness in patent commercialization. Intermediates show strengths in venture-capital and labor productivity but display weaknesses in R&D expenditure and higher education quality. Primitives exhibit strength in creative activities but suffer from weaknesses in digital skills, education, and training. Additionally, the study has identified 17 indicators that have negligible variance contributions across countries. The findings contribute to the field of innovation performance benchmarking by identifying appropriate benchmarking clusters and exploring learning opportunities and integration directions. To this end, it highlights the innovation structural differences among countries and provides tailored innovation policies.
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Materias
Ciencias económicas
Economía del cambio tecnológico
Economía del cambio tecnológico