64th ISI World Statistics Congress - Ottawa, Canada

64th ISI World Statistics Congress - Ottawa, Canada

Multi-dimensional reduction techniques applied to measuring global interlinkages between SDGs


Jean-Pierre Cling


  • C
    Clément Delecourt


64th ISI World Statistics Congress - Ottawa, Canada

Format: CPS Abstract

Keywords: sdgs

Session: CPS 67 - Sustainable development goals II

Tuesday 18 July 5:30 p.m. - 6:30 p.m. (Canada/Eastern)


We measure interlinkages between SDGs, applying linear dimensionality reduction techniques on a dataset derived from the UN Global SDG Database. This is the first study of this kind at the world level.
It was widely accepted from the adoption of Sustainable Development Goals in 2015 that taking into account interlinkages between SDGs conditions reaching the 2030 Agenda. The objective of this paper is to measure these global interlinkages between countries through an analysis of correlations between the SDGs and their associated indicators, complemented by country clustering to check the consistency of our analysis.
The Multiple Factor Analysis (MFA) used to synthesize the correlations between indicators shows that SDGs related to human development alone contribute to 30 % of the observed variance of all the indicators at the world level, and that country performances in this field are strongly correlated to their income level (GNI per capita). SDGs related to the environment and governance also contribute to a lesser but significant extent to the variance of the dataset. We observe synergies (positive correlations) only and no than trade-offs (negative correlations) between SDGs and their monitoring indicators.
The Hierarchical Cluster Analysis (HCA) distinguishes three country clusters according to their performance in terms of SDG indicators. The differentiation between countries, and hence the composition of these clusters, mainly reflects their economic development as measured by their GNI per capita. The first cluster, which is mostly composed of African countries, lags behind in terms of sustainable (especially economic and social) development. The second and third clusters are mostly composed of respectively middle-income and high-income countries (according to the World Bank classification). Multiple Factor Analysis finds broadly the same types of interlinkages both within each group of countries and globally.
The aim of the SDGs was to take a holistic approach to development beyond the measurement of GDP, which is often misused as a summary indicator of development and well-being. These results suggest, however, that the differences between countries on the SDG indicators stem largely from their level of development, as measured by their income per capita
Data is still lacking on many dimensions of sustainable development, especially on SDGs covering new domains such as the environment and governance. Some progress will have to be made to address these shortcomings before the deadline of the Agenda 2030, so that increased data availability will help to keep improving knowledge for monitoring SDGs and their interlinkages.