  One of the first steps to understand and forecast economic downturns is
identifying their frequency distribution, but it remains uncertain. This
problem is common in phenomena displaying power-law-like distributions. Power
laws play a central role in complex systems theory; therefore, the current
limitations in the identification of this distribution in empirical data are a
major obstacle to pursue the insights that the complexity approach offers in
many fields. This paper addresses this issue by introducing a reliable
methodology with a solid theoretical foundation, the Taylor Series-Based Power
Law Range Identification Method. When applied to time series from 39 countries,
this method reveals a well-defined power law in the relative per capita GDP
contractions that span from 5.53% to 50%, comprising 263 events. However, this
observation does not suffice to attribute recessions to some specific
mechanism, such as self-organized criticality. The paper highlights a set of
points requiring more study so as to discriminate among models compatible with
the power law, as needed to develop sound tools for the management of
recessions.
