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An Alternative Aggregation Process for Composite Indexes: An Application to the Heritage Foundation Economic Freedom Index José Manuel Cabello1   · Francisco Ruiz1   · Blanca Pérez‑Gladish2  Accepted: 3 October 2020 © Springer Nature B.V. 2020

Abstract Composite indices of economic freedom may constitute a useful and practical guide and a significant source of information for experts in government policy, investors and market researchers across the world. Their high applicability and the important consequences of the relative position of the countries in a ranking of economic freedom, has led many prac‑ titioners and academic researchers to open a debate about some methodological aspects in the construction of these indices. In this paper, we will focus on the aggregation and nor‑ malization processes used to build the composite indicator, and we will make user of the data of the index of economic freedom published by the Heritage Foundation. In particular, we will propose a different more general and rich aggregation and normalization approach based on the Multiple Reference Point method. Rather than just providing a ranking, the resulting composite indicators will be able to show overall and particular strengths and weaknesses of the countries in terms of their economic freedom enriching therefore, the information provided by other indexes of economic freedom. Both, numerical scores and their correspondent graphical representation give the decision maker a full and detailed picture of the situation of the countries, informing in addition about the relative perfor‑ mance of the country with respect to other countries. Keywords  Economic freedom · Composite indicator · Multiple reference point method · Aggregation methods

* Blanca Pérez‑Gladish [email protected] José Manuel Cabello [email protected] Francisco Ruiz [email protected] 1

Department of Applied Economics (Mathematics), University of Málaga, Málaga, Spain

2

Department of Quantitative Economics, University of Oviedo, Oviedo, Spain



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1 Introduction Different definitions of composite indicators can be found in the literature. Chevalier et al. (1992) define them as the observable part of a phenomenon, which allows us to evaluate some non-observable part of this phenomenon. Holling (1978) defines them as a measure of the behaviour of a system in terms of significant and perceptible attributes. Ott (1978) establishes that they are the simplest form of reduction of a great amount of data, in such a way that the essential information for the purposes of the data is kept. Bermejo (2001) defines them as a way of simplifying a complex reality, centring the attention in certain relevant aspects, so that the resulting number of parameters is manageable. Among the main purposes of a system of indicators, we can highlight: the modelling, simulation, monitoring and control and prediction of a complex phenomenon. The model‑ ling is conducted thorough the analysis of the elements that form the system under study. Simulation imp