A Complex MCDM Procedure for the Assessment of Economic Development of Units at Different Government Levels release_q3diuwv6cjbnxjntb2nyfquoy4

by Aleksandra Łuczak, Małgorzata Just

Published in Mathematics by MDPI AG.

2020   p1067

Abstract

Studies on the economic development of government units are among the key challenges for authorities at different levels and an issue often investigated by economists. In spite of a considerable interest in the issue, there is no standard procedure for the assessment of economic development level of units at different levels of government (national, regional, sub-regional). This assessment needs a complex system of methods and techniques applicable to the various types of data. So, adequate methods must be used at each level. This paper proposes a complex procedure for a synthetic indicator. The units are assessed at different government levels. Each level (national, regional, and sub-regional) may be described with a particular type of variables. Set of data may include variables with a normal or near-normal distribution, a strong asymmetry or extreme values. The objective of this paper is to present the potential behind the application of a complex Multi-Criteria Decision Making (MCDM) procedure based on the tail selection method used in the Extreme Value Theory (EVT), i.e., Mean Excess Function (MEF) together with one of the most popular MCDM methods, namely the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), to assess the economic development level of units at different government levels. MEF is helpful to identify extreme values of variables and limit their impact on the ranking of local administrative units (LAUs). TOPSIS is suitable in ranking units described with multidimensional data set. The study explored the use of two types of TOPSIS (classical and positional) depending on the type of variables. These approaches were used in the assessment of economic development level of LAUs at national, regional and sub-regional levels in Poland in 2017.
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