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The Social Progress Index is a composite indicator that aims to gauge countries’ overall well-being. By capturing a wide variety of non-economic indicators, it attempts to complement the classical economic measurements like Gross Domestic Product. However, in its current form, it imposes the same equal weighting scheme to all its subjects. Such paternalistic weights imply that all subjects value the various indicators identically. This paper provides an alternative perspective since we advocated the Data Development Analysis-approach. We assigned the European Union’s NUTS2 regions with region- specific, endogenously determined benefit-of-the-doubt weights, that yield the highest possible relative performance scores for each region. Consequently, policymakers can benchmark their country/region using their own strengths. This makes it difficult to argue that poor rankings are a consequence of the weighting scheme applied. Moreover, to account for the environmental differences between the various regions, we added the robust conditional benefit-of-the-doubt extension. In our attempt to establish an indicator for social progress, that accounts for differences in policy priorities and operating environments, we came to the conclusion that the Scandinavian and Dutch regions are the strongest performers within the sample, while it are mostly Bulgarian and Romanian regions that lack behind. Furthermore, we learned that for some member states, including the operating environment appeared to have a divergent effect for their NUTS2 regions, implying the presence of intercountry heterogeneity.
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The following pages present an attempt to describe whether there has been a significant change in the efficiency of spending on education in 34 OECD countries between 2006 and 2015 by applying a popular technique in the field - Data Envelopment Analysis. Because countries tend to have a discrepant level of development as they function under divergent conditions, we see it as inadequate to compare all the OECD countries as if they were equals. As a solution, we apply an order-m calculation to avoid the potential disturbance created by outliers and the relatively small sample size. Furthermore, the Malmquest index is calculated for all the analysed years. The following research creates as well a relation to existing literature which is presented in the Annex.
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