Competitiveness of Nations and Inequality-Adjusted Human Development: Evaluating the Efficiency of Nations Using DEA and Random Forest Classification

Christopher Colin Campbell ()
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A chapter in New Perspectives in Operations Research and Management Science, 2022, pp 113-141 from Springer

Abstract: Abstract Although popular indices like the Human Development Index (HDI) and Global Competitiveness Index (GCI) measure human development and competitiveness separately, no index directly considers their linkage, namely, the relative ability of countries to leverage their economic competitiveness to improve the human development of their citizens. This paper aims to combine data envelopment analysis and random forest classification to explore the relative performance of countries in terms of competitiveness and human development. In the first stage of the methodology, we evaluate 124 countries using data envelopment analysis (DEA), taking indicators from the GCI and IHDI (inequality-adjusted human development index) as input and output variables, respectively. In the methodology’s second stage, we use random forest classification to identify the relative importance of input and output variables on the DEA results—specifically, whether countries were classified as efficient or inefficient. Our findings indicate that only 20 of 124 countries are efficient at using their competitiveness to generate human development, and that variables related to a country’s innovation ecosystem are most important. The results suggest most countries fail to take full advantage of their economic resources amidst a period of rapid technological and social change; it also highlights huge disparities between different groups of countries (e.g. regions).

Keywords: Competitiveness of nations; Human development; Random forests; Data envelopment analysis (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-3-030-91851-4_5

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DOI: 10.1007/978-3-030-91851-4_5

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