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

TL;DR

This paper introduces a new methodology to identify power law distributions in economic contractions, revealing a well-defined power law in GDP downturns across 39 countries, but emphasizes the need for further research to understand underlying mechanisms.

Contribution

It presents the Taylor Series-Based Power Law Range Identification Method, a theoretically grounded approach for detecting power laws in economic data.

Findings

Power law distribution found in GDP contractions from 5.53% to 50%.

Method applied to 39 countries with 263 events analyzed.

Further research needed to understand mechanisms behind the power law.

Abstract

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.