Long Tails, Automation and Labor

Nat Kausik ()

MPRA Paper from University Library of Munich, Germany

Abstract: A central question in economics is whether automation will displace human labor and diminish standards of living. Whilst prior works typically frame this question as a competition between human labor and machines, we frame it as a competition between human consumers and human suppliers. Specifically, we observe that human needs favor long tail distributions, i.e., a long list of niche items that are substantial in aggregate demand. In turn, the long tails are reflected in the goods and services that fulfill those needs. With this background, we propose a theoretical model of economic activity on a long tail distribution, where innovation in demand for new niche outputs competes with innovation in supply automation for mature outputs. Our model yields analytic expressions and asymptotes for the shares of automation and labor in terms of just four parameters: the rates of innovation in supply and demand, the exponent of the long tail distribution and an initial value. We validate the model via non-linear stochastic regression on historical US economic data with surprising accuracy.

Keywords: Labor share; Automation; AI (search for similar items in EconPapers)
JEL-codes: D63 E2 J2 O33 O4 (search for similar items in EconPapers)
Date: 2023-07-16
New Economics Papers: this item is included in nep-tid
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Working Paper: Long Tails, Automation and Labor (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:117996

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