Nonparametric Approach to Evaluation of Economic and Social Development in the EU28 Member States by DEA Efficiency

Abstract

Data envelopment analysis (DEA) methodology is used in this study for a comparison of the dynamic efficiency of European countries over the last decade. Moreover, efficiency analysis is used to determine where resources are distributed efficiently and/or were used efficiently/inefficiently under factors of competitiveness extracted from factor analysis. DEA measures numerical grades of the efficiency of economic processes within evaluated countries and, therefore, it becomes a suitable tool for setting an efficient/inefficient position of each country. Most importantly, the DEA technique is applied to all (28) European Union (EU) countries to evaluate their technical and technological efficiency within the selected factors of competitiveness based on country competitiveness index in the 2000–2017 reference period. The main aim of the paper is to measure efficiency changes over the reference period and to analyze the level of productivity in individual countries based on the Malmquist productivity index (MPI). Empirical results confirm significant disparities among European countries and selected periods 2000–2007, 2008–2011, and 2012–2017. Finally, the study offers a comprehensive comparison and discussion of results obtained by MPI that indicate the EU countries in which policy-making authorities should aim to stimulate national development and provide more quality of life to the EU citizens.

Efficiency of Public and Private Service Delivery: The Case of Secondary Education

“…Unlike the usual calculation of efficiency (productivity) rate, the DEA method applies mathematical programming, which enables the inclusion of a vast amount of inputs and outputs in the model. Unlike the ordinary calculation, the weights are variable and set to maximise the relative efficiency rate of the evaluated unit against the other units (Cooper, Seiford and Zhu, 2011;Dlouhý, Jablonský and Zýková, 2018;Melecký, Staníčková and Hančlová, 2019). Efficiency is expressed by the transformation process of inputs to relevant outputs.…”

The EU Countries’ Assessment with Respect to the Prevalence of Severe Material Deprivation and Determinants of Poverty: Application of Non-parametric DEA Approach

“…Similarly, DEA requires no prior designation of the weights of inputs and outputs because they are determined by the model itself (Rabar, 2017). DEA model can be formulated when several assumptions are accepted, the fundamental are: (I) homogeneity or at least mutual comparability of the DMUs (DMUs should consume the same inputs and produce the same outputs); (II) the rule of thumb (number of DMUs has to be at least twice as large as the total number of input and output variables); (III) the isotonicity criterion (outputs have to be at least the same and do not fall when the inputs are increased) (Melecký et al, 2019;Rabar, 2017;Saljoughian et al, 2013;Sarkis, 2007).…”

“…When the countries' relative efficiency is assessed, an output-oriented DEA model with constant returns to scale (CRS) is preferred (Melecký et al, 2019). Models with the CRS are appropriate if all DMUs operate at the optimal scale (Huguenin, 2012).…”

Nonparametric Econometric Methods and Applications

“…They derive both asymptotic properties and examine finite sample performance through Monte Carlo simulations. Finally, Melecký et al (2019) apply data envelopment analysis (DEA) methodology to compare the dynamic efficiency of European countries over the last decade.…”