Scaling laws and a general theory for the growth of public companies
Jiang Zhang, Christopher P. Kempes, Marcus J. Hamilton, Ruyi Tao,, Geoffrey B. West

TL;DR
This paper develops a scaling theory for the growth of public companies, revealing universal patterns and differences in growth dynamics across US and Chinese markets based on extensive data analysis.
Contribution
It introduces a novel scaling framework that explains company growth patterns and predicts their long-term trajectories, supported by empirical data from US and Chinese firms.
Findings
Sales scale sublinearly with assets in both countries.
Assets grow as a power law over time, not exponentially.
Liabilities scale linearly in the US and superlinearly in China.
Abstract
Publicly traded companies are fundamental units of contemporary economies and markets and are important mechanisms through which humans interact with their environments. Understanding the general properties that underlie the processes of their growth has long been of interest, yet fundamental debates about the effects of firm size on growth have persisted. Here we develop a scaling framework that focuses on company size as the critical feature determining a variety of tradeoffs, and use this to reveal novel systematic behavior across the diversity of publicly-traded companies. Using a large database of 31,553 US companies over nearly 70 years, and 3,160 Chinese companies over 24 year, we show how the dynamics of companies expressed as scaling relationships leads to a quantitative, analytic theory for their growth. This theory produces several predictions that are in good agreement with…
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Taxonomy
TopicsComplex Systems and Time Series Analysis
