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One of the first steps to understand and forecast economic downturns isidentifying their frequency distribution, but it remains uncertain. Thisproblem is common in phenomena displaying power-law-like distributions. Powerlaws play a central role in complex systems theory; therefore, the currentlimitations in the identification of this distribution in empirical data are amajor obstacle to pursue the insights that the complexity approach offers inmany fields. This paper addresses this issue by introducing a reliablemethodology with a solid theoretical foundation, the Taylor Series-Based PowerLaw Range Identification Method. When applied to time series from 39 countries,this method reveals a well-defined power law in the relative per capita GDPcontractions that span from 5.53% to 50%, comprising 263 events. However, thisobservation does not suffice to attribute recessions to some specificmechanism, such as self-organized criticality. The paper highlights a set ofpoints requiring more study so as to discriminate among models compatible withthe power law, as needed to develop sound tools for the management ofrecessions.

Is it a power law distribution? The case of economic contractions