The Footprint of Evolutionary Processes of Learning and Selection Upon the Statistical Properties of Industrial Dynamics

30 Pages Posted: 15 Apr 2016

See all articles by Giovanni Dosi

Giovanni Dosi

Scuola Superiore Sant'Anna di Pisa - Laboratory of Economics and Management (LEM)

Marcelo C. Pereira

University of Campinas

Maria Enrica Virgillito

Scuola Superiore Sant'Anna di Pisa

Date Written: 2015

Abstract

Evolutionary theories of economic change identify the processes of idiosyncratic learning by individual firms and of market selection as the two main drivers of the dynamics of industries. Are such processes able to account for the statistical regularities which industrial structures and dynamics display? In this work we address this question by means of a simple multi-firm model, formalizing the mechanisms of learning and selection. As such, despite its simplicity, the model is able to robustly reproduce an ensemble of empirical stylised facts, including ample heterogeneity in productivity distributions, persistent market turbulence and fat-tailed distribution of growth rates.

Keywords: Firm Growth Rates, Heterogeneous Productivities, Fat Tail Distributions, Learning Processes, Market Selection

JEL Classification: C63, L11, L6

Suggested Citation

Dosi, Giovanni and Pereira, Marcelo and Virgillito, Maria Enrica, The Footprint of Evolutionary Processes of Learning and Selection Upon the Statistical Properties of Industrial Dynamics (2015). Available at SSRN: https://ssrn.com/abstract=2764279 or http://dx.doi.org/10.2139/ssrn.2764279

Giovanni Dosi (Contact Author)

Scuola Superiore Sant'Anna di Pisa - Laboratory of Economics and Management (LEM) ( email )

Piazza Martiri della Liberta, 33
Pisa, I-56127
Italy

HOME PAGE: www.lem.sssup.it

Marcelo Pereira

University of Campinas ( email )

Rua Pitagoras 353
Campinas, Campinas 13088-857
Brazil

Maria Enrica Virgillito

Scuola Superiore Sant'Anna di Pisa ( email )

Biblioteca Scuola Superiore Sant'Anna
Piazza Martiri della Liberta, n. 33
Pisa, 56127
Italy

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