Abstract
The distribution of firms' growth and firms' sizes is a topic under intense scrutiny. In this paper, we show that a thermodynamic model based on the maximum entropy principle, with dynamical prior information, can be constructed that adequately describes the dynamics and distribution of firms' growth. Our theoretical framework is tested against a comprehensive database of Spanish firms, which covers, to a very large extent, Spain's economic activity, with a total of 1 155 142 firms evolving along a full decade. We show that the empirical exponent of Pareto's law, a rule often observed in the rank distribution of large-size firms, is explained by the capacity of economic system for creating/destroying firms, and that can be used to measure the health of a capitalist-based economy. Indeed, our model predicts that when the exponent is larger than 1, creation of firms is favoured; when it is smaller than 1, destruction of firms is favoured instead; and when it equals 1 (matching Zipf's law), the system is in a full macroeconomic equilibrium, entailing ‘free’ creation and/or destruction of firms. For medium and smaller firm sizes, the dynamical regime changes, the whole distribution can no longer be fitted to a single simple analytical form and numerical prediction is required. Our model constitutes the basis for a full predictive framework regarding the economic evolution of an ensemble of firms. Such a structure can be potentially used to develop simulations and test hypothetical scenarios, such as economic crisis or the response to specific policy measures.
Highlights
Many natural, social and economic phenomena follow power laws
We show that a thermodynamic model based on the maximum entropy principle, with dynamical prior information, can be constructed that adequately describes the dynamics and distribution of firms’ growth
We show the size distribution equation (2.9) for different simulation times measured in Monte Carlo (MC) steps
Summary
Social and economic phenomena follow power laws. Their ubiquity has been previously ascertained in the distribution of financial or econometric values such as wealth and income, [1,2,3,4,5,6,7,8], or the size of cities [9,10,11,12,13], and even in human language and frequency of words [14,15,16], Internet networks [17] or scientific publications and citations [18,19,20,21], among many other human-related measurable observables. Finding a complete theory for describing this kind of systems seems an impractical task, given the huge amount of degrees of freedom involved in discussing these social systems. This notwithstanding, remarkable regularities were reported and studied, such as Zipf’s law [22,23,24,25], or the celebrated Gibrat’s law of proportional growth [26], which constitute important milestones on the quest for a unified framework that could mathematically describe predictable tendencies [7,10,27,28,29].
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