Long Tails, Automation and Labor

TL;DR

This paper presents a theoretical model analyzing how long tail distributions in consumer demand influence the competition between automation and human labor, validated by historical US economic data.

Contribution

It introduces a novel model framing automation versus human labor as a competition driven by long tail demand, with analytical expressions and empirical validation.

Findings

Model accurately predicts automation and labor shares

Long tail demand significantly impacts automation trends

Analytic expressions relate innovation rates to labor and automation shares

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.