Rank-Optimal Weighting or “How to be Best in the OECD Better Life Index?”

Jan Lorenz (), Christoph Brauer () and Dirk Lorenz ()
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Christoph Brauer: Technische Universität Braunschweig
Dirk Lorenz: Technische Universität Braunschweig

Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, 2017, vol. 134, issue 1, No 4, 75-92

Abstract: Abstract We present a method of rank-optimal weighting which can be used to explore the best possible position of a subject in a ranking based on a composite indicator by means of a mathematical optimization problem. As an example, we explore the dataset of the OECD Better Life Index and compute for each country a weight vector which brings it as far up in the ranking as possible with the greatest advance of the immediate rivals. The method is able to answer the question “What is the best possible rank a country can achieve with a given set of weighted indicators?” Typically, weights in composite indicators are justified normatively and not empirically. Our approach helps to give bounds on what is achievable by such normative judgments from a purely output-oriented and strongly competitive perspective. The method can serve as a basis for exact bounds in sensitivity analysis focused on ranking positions. In the OECD Better Life Index data we find that 19 out the 36 countries in the OECD Better Life Index 2014 can be brought to the top of the ranking by specific weights. We give a table of weights for each country which brings it to its highest possible position. Many countries achieve their best rank by focusing on their strong dimensions and setting the weights of many others to zero. Although setting dimensions to zero is possible in the OECD’s online tool, this contradicts the idea of better life being multidimensional in essence. We discuss modifications of the optimization problem which could take this into account, e.g. by allowing only a minimal weight of one. Methods to find rank-optimal weights can be useful for various multidimensional datasets like the ones used to rank universities or employers.

Keywords: Composite indicators; Weighting; Ranking; OECD; Sensitivity analysis (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (17)

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DOI: 10.1007/s11205-016-1416-0

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