Artificial Intelligence, Income Distribution and Economic Growth

A new GLO Discussion Paper studies the implications of Artificial Intelligence (AI) in an endogenous growth model. It can explain why advanced countries tend to experience, despite much AI hype, the simultaneous existence of rather high employment with stagnating wages, productivity, and GDP.

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GLO Discussion Paper No. 632, 2020

Artificial Intelligence, Income Distribution and Economic GrowthDownload PDF
by
Gries, Thomas & Naudé, Wim

GLO Fellow Wim Naudé

Wim Naudé

Author Abstract: The economic impact of Artificial Intelligence (AI) is studied using a (semi) endogenous growth model with two novel features. First, the task approach from labor economics is reformulated and integrated into a growth model. Second, the standard representative household assumption is rejected, so that aggregate demand restrictions can be introduced. With these novel features it is shown that (i) AI automation can decrease the share of labor income no matter the size of the elasticity of substitution between AI and labor, and (ii) when this elasticity is high, AI will unambiguously reduce aggregate demand and slow down GDP growth, even in the face of the positive technology shock that AI entails. If the elasticity of substitution is low, then GDP, productivity and wage growth may however still slow down, because the economy will then fail to benefit from the supply-side driven capacity expansion potential that AI can deliver. The model can thus explain why advanced countries tend to experience, despite much AI hype, the simultaneous existence of rather high employment with stagnating wages, productivity, and GDP.

GLO Discussion Papers are research and policy papers of the GLO Network which are widely circulated to encourage discussion. Provided in cooperation with EconStor, a service of the ZBW – Leibniz Information Centre for Economics, GLO Discussion Papers are among others listed in RePEc (see IDEAS,  EconPapers)Complete list of all GLO DPs – downloadable for free.

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