E pluribus, quaedam. Gross domestic product out of a dashboard of indicators - Mattia Guerini; Fabio Vanni; Mauro Napoletano (Fondazione Eni Enrico Mattei (FEEM), 2022)
Is aggregate income enough to summarize the well-being of a society? We address this longstanding question by exploiting a novel approach to study the relationship between gross domestic product (GDP) and a set of economic, social and environmental indicators for nine developed economies. By employing dimensionality reduction techniques, we quantify the share of variability stemming from a large set of different indicators that can be compressed into a univariate index. We also evaluate how well this variability can be explained if the univariate index is GDP. Our results indicate that univariate measures, and GDP among them, are doomed to fail in accounting for the variability of well-being indicators. Even if GDP would be the best linear univariate index, its quality in synthesizing information from indicators belonging to different domains is poor. Our approach provides additional support for policy makers interested in measuring the trade offs between income and other relevant socio-economic and ecological dimensions. Furthermore, it adds new quantitative evidence to the already vast literature criticizing GDP as the most prominent measure of well-being.
Keywords: C43; I30; I31; 330; Gross domestic product; well-being indicators; data reduction techniques; principal component analysis; random matrix