Abstract
This paper measures and monitors EU Member States’ change in social inclusion using a set of statistical indicators as commonly endorsed by the Heads of State and Government in the Europe2020-program and employed by Social OMC. In particular, for each EU Member State a composite policy performance index is constructed using Van Puyenbroeck and Rogge (Eur J Oper Res, 2017) ‘indirect’ geometric benefit-of-the-doubt-method. Using their multi-factor decomposition, changes in social inclusion in the global EU-region and the individual EU Member States during the period 2005–2014 are analyzed and explained. Results showed that old EU Member States generally outperformed new EU Member States in social inclusion in both 2005 and 2014. In addition, results pointed out a general trend of increase in progress and cohesion in the EU. However, whereas the increase in social progress and social cohesion in the EU was more outspoken in the pré-crisis period, this increase was only small and more dispersed across EU Member States in the post-crisis period.
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Notes
The Europe 2020 Strategy focuses on five headline targets comprising in total eight sub-targets, as agreed upon by the Heads of States, that are to be achieved by 2020. These 2020-targets are considered to be of equal importance and obligation for each of the Member States.
As to the task of comparing and ranking country performances on complex, multi-faceted policy phenomena, the well-known problem is that one cannot rank them unless one aggregates the country performance values on the multiple sub-indicators measuring the different policy aspects. Of course, all reasonable CIs would return the same logical ordering of the performances in the trivial case where a multi-dimensional dominance relation at the level of the sub-indicators existed. But settings in which a complete ordering can be achieved in such an uncontested manner are rare, if they exist at all. The present case study with social inclusion sub-indicators for the EU Member States is not different, with Member States outperforming other Member States on one social inclusion sub-indicator and vice versa. Note, however, that the use of CIs and the approach of just looking at the four single indicators are not mutually exclusive.
While the timing of the first effects of the economic and financial crisis (and the first impacts on social inclusion) somewhat differed among the EU Member States, empirical evidence (i.e., the financial and economic indicators) shows that for the EU-region the first consequences of the financial and economic crisis took place around 2008–2009–2010 (European Commission 2009). Therefore, we opted to consider the period 2005-2009 as the pre-crisis period and the period 2010-2014 as the post-crisis period in the present analysis.
Ideally, weight bound values should be specified by experts and/or stakeholders. However, practical experience teaches us that strong consent, even between experts thoroughly acquainted with the object of study, is unlikely to come about on this matter [on social inclusion within the EU context, see e.g. Cherchye et al. (2004); for an illustration with real data for the Technology Achievement Index see Cherchye et al. (2008)]. In the current illustrative application we lack such expert information, but still defined the lower weight bound value of 5% so as to avoid (quasi-)zero BoD-weights. Stated otherwise, we take it that our social inclusion CI cannot be constructed while disregarding at least one of its constituent sub-indicators, a minimalist position which we take to reflect the underlying idea that all dimensions are considered as providing at least some valuable information to the European Commission’s dashboard of key social inclusion indicators. As a robustness check we computed the BoD-model with lower weight bound values set equal to 10%. Overall, this implied only minor differences in the CI-scores.
As to the specification of the base performance values y B,i in formula (3), it was noted by Van Puyenbroeck and Rogge (2017) that the choice of a specific set of base performance values is largely arbitrary. Depending on the evaluation context, base performance values other than the sample average of each sub-indicator can be specified (e.g., median, maximum, etc.). In the present context of evaluating EU Member State performances on the Europe 2020 social inclusion indicators one could equally well define the base performance values as the EU target values declared by the European Commission (or alternatively, the country-specific target values).
This combination corresponds with the third method of analysing employment and social developments and levels in the Joint Employment Report as outlined by the European Commission and Council in March 2014. Specifically, these combinations point out the synthesized “dynamics of socio-economic convergence/divergence” by summarizing the change in the social inclusion policy performance of each Member State between consecutive periods relative to the change at the EU-level (Council of the European Union 2014, p. 51).
The reason for not including Sweden and Luxembourg in Fig. 2 are the rather extreme PC c -scores for these two countries relative to the other countries for the period 2010–2014 (Sweden with PC c = 5.7074 and Luxembourg with PC c = 0.1822). Including these two countries in Fig. 2 would mean that differences in PC c -scores between the majority of the EU Member States would no longer result in a colour difference in the visualization.
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Acknowledgements
We thank three anonymous referees for insightful comments and suggestions. This paper is an offshoot of the Impulsproject IMP/14/011 of the KU Leuven (Belgium).
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Rogge, N., Konttinen, E. Social Inclusion in the EU Since the Enlargement: Progress or Regress?. Soc Indic Res 135, 563–584 (2018). https://doi.org/10.1007/s11205-016-1504-1
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DOI: https://doi.org/10.1007/s11205-016-1504-1