Czech researchers have boldly challenged the use of 'per capita' metrics.

An extensive analysis, covering datasets on Gross Domestic Product (GDP), COVID-19-related mortality, and CO2 production, highlights a systemic bias that overestimates values in countries with smaller populations while underestimating those in larger ones. 

This finding is particularly evident in their observation that only countries with smaller populations rank among the top ten in GDP per capita comparisons. 

They argue that such distortions can lead to misleading conclusions and, ultimately, ineffective policy measures.

The researchers call for a shift towards regression-based approaches, a statistical method they claim offers a more nuanced and scientifically robust framework for comparing countries. 

This method, they say, accounts for the varying relationships between the measure of interest (such as GDP or mortality rates) and population size, providing a more accurate reflection of a country's status or performance relative to its size.

The researchers note that their paper adds to a growing consensus on the need for more sophisticated statistical methodologies that not only enhance the accuracy of global comparisons, but also inform more effective policy decisions. 

The full study is accessible here.