Social Progress beyond GDP: A Principal Component Analysis (PCA) of GDP and Twelve Alternative Indicators

Abstract

What social progress is and how to measure it are seemingly plain but essentially intri-cate questions that have not been clarified to date, which has led to various social problems and development failures. Designed after the Great Depression in the 1930s, Gross Domestic Product (GDP) has been, on the one hand, regarded as the greatest invention of the 20th century and is widely accepted as the primary indicator for social progress, but on the other hand, it has been criticized as knowing the price of everything but the value of nothing. The Beyond GDP Movement that has been active since the 2010s has inspired global interest in designing indicators to replace or supplement GDP, but none of them stands out as GDP’s successor. We take 12 influential indicators that consider beyond GDP and carry out a Principal Component Analysis (PCA) to investigate their correlations. The results indicate that GDP per capita (GDPP) can explain 65.61% of the information in the first principal component (PC) and account for 51.10% of the information related to the total 13 indicators, indicating its major role in social progress. Most indicators have strong correlations with GDPP, not beyond, and only the Ecological Footprint per capita (EFP) and Happy Planet Index (HPI) that have negative and weak correlations with GDPP, respectively, can provide new perspectives and values beyond GDP. Social progress is based upon various public values, and the indicators are the measurements of these values. Although GDP and economic values play major roles during social development, other indicators and their potential public values cannot be ignored. Prioritizing these public values and monitoring their indicators are essential to achieving sustainable and comprehensive social progress.

European Union and its progress towards affordable and clean energy in context of the Agenda 2030

“…To solve this issue, principal component analysis (PCA) was selected. PCA is a technique that creates a linear combination of analyzed indicators and belongs to dimensionality reduction methods [44][45][46].…”