Recipes and Economic Growth: A Combinatorial March Down an Exponential Tail

New ideas are often combinations of existing goods or ideas, a point emphasized by Romer (1993) and Weitzman (1998). A separate literature highlights the links between exponential growth and Pareto distributions: Gabaix (1999) shows how exponential growth generates Pareto distributions, while Kortum (1997) shows how Pareto distributions generate exponential growth. But this raises a "chicken and egg" problem: which came first, the exponential growth or the Pareto distribution? And regardless, what happened to the Romer and Weitzman insight that combinatorics should be important? This paper answers these questions by demonstrating that combinatorial growth in the number of draws from standard thin-tailed distributions leads to exponential economic growth; no Pareto assumption is required. More generally, it provides a theorem linking the behavior of the max extreme value to the number of draws and the shape of the tail for any continuous probability distribution. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

[1]  Samuel Kortum,et al.  Research, Patenting, and Technological Change , 1997 .

[2]  Jesse Perla,et al.  Equilibrium Imitation and Growth , 2014, Journal of Political Economy.

[3]  Charles I. Jones,et al.  R & D-Based Models of Economic Growth , 1995, Journal of Political Economy.

[4]  L. Haan,et al.  Extreme value theory : an introduction , 2006 .

[5]  Cláudia Neves,et al.  Extreme Value Distributions , 2011, International Encyclopedia of Statistical Science.

[6]  C. I. Jones,et al.  A Schumpeterian Model of Top Income Inequality , 2014, Journal of Political Economy.

[7]  J. Corcoran Modelling Extremal Events for Insurance and Finance , 2002 .

[8]  Daron Acemoglu,et al.  Endogenous Production Networks , 2017, Econometrica.

[9]  P. Romer Endogenous Technological Change , 1989, Journal of Political Economy.

[10]  Four Models of Knowledge Diffusion and Growth , 2015 .

[11]  M. Weitzman,et al.  Recombinant Growth , 2009 .

[12]  P. M. Shearer,et al.  Zipf Distribution of U . S . Firm Sizes , 2022 .

[13]  Robert E. Lucas,et al.  Knowledge Growth and the Allocation of Time , 2011, Journal of Political Economy.

[14]  J. D. T. Oliveira,et al.  The Asymptotic Theory of Extreme Order Statistics , 1979 .

[15]  X. Gabaix Zipf's Law for Cities: An Explanation , 1999 .

[16]  Erzo G. J. Luttmer Selection, Growth, and the Size Distribution of Firms , 2007 .

[17]  S. Resnick Extreme Values, Regular Variation, and Point Processes , 1987 .

[18]  P. Romer Two Strategies for Economic Development: Using Ideas and Producing Ideas , 1992 .

[19]  L. Kogan,et al.  Technological Innovation, Resource Allocation, and Growth , 2012 .

[20]  J. Benassy,et al.  Taste for variety and optimum production patterns in monopolistic competition , 1996 .