Abstract
This paper contributes, with a dynamic approach, to the research on the creation of comparable composite indicators by presenting a proposal for an exploratory factor analysis protocol to enable a comparative trend analysis. The originality of the study lies in the three dimensions of information for analysis: observations, variables and units of time. The proposal involves various stages of analysis with the ultimate, albeit not exclusive, aim of obtaining what is known as a Global Dynamic Indicator. The analysis process begins by structuring the data into a three-dimensional global matrix, thereby conditioning, while also, and primarily, enriching the later stages. A combination of multiple factor analysis and a clustering technique is the selected approach for successfully meeting the challenges involved. The appropriateness and versatility of the proposal are validated through the analysis of the trends of the EU member states towards the targets set by the 2020 Strategy. The study period runs from 2009 to 2018. The empirical work enables the visualisation and quantification of trend differences and similarities across member states collectively and individually, and across all the variables and years selected for analysis. The relevant findings will be quantified by means of a synthetic indicator for each unit of time and a global indicator for the period as a whole. Some of the conclusions reached by this paper are consistent with those already published by various authors.
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Notes
Unless specifically indicated otherwise, in this paper, EU refers to the EU 28.
For further information on the Europe 2020 Strategy see, for example, the following link: https://ec.europa.eu/eurostat/web/europe-2020-indicators.
The national objectives of the 2020 Strategy can be found at: http://ec.europa.eu/eurostat/documents/4411192/4411431/Europe_2020_Targets.pdf. The EU Statistics Office (Eurostat) periodically publishes target progress reports.
Most R&D investment in the EU comes from the private sector, although investment by Universities and Governments has been slowly on the rise since 2002.
Or, failing that, last available information.
Also known as “groups”.
Since different units are used to measure the variables, typification is recommended. The factor analysis will therefore be performed on the correlation matrices.
As already stated, the removal of the variable for greenhouse gas emissions (GGE) was due to the limitations of the common target of all EU28 states member (null variance variable).
Specifically, those developed by Catell, Kayser and Anderson’s confidence intervals (Lebart et al. 2006, pp. 96–100).
The paragon is the closest country to the center of gravity (middle) of the class and, consequently, its most typical representative (Aluja and Morineau 1999, pp. 103–104).
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Landaluce-Calvo, M.I., Gozalo-Delgado, M. Proposal for a Dynamic Composite Indicator: Application in a Comparative Analysis of Trends in the EU Member States Towards the Europe 2020 Strategy. Soc Indic Res 154, 1031–1053 (2021). https://doi.org/10.1007/s11205-020-02547-w
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DOI: https://doi.org/10.1007/s11205-020-02547-w