Innovation, Automation, and Inequality: Policy Challenges in the Race against the Machine

Klaus Prettner and Holger Strulik

No 320, GLO Discussion Paper Series from Global Labor Organization (GLO)

Abstract: We analyze the effects of R&D-driven automation on economic growth, education, and inequality when high-skilled workers are complements to machines and low-skilled workers are substitutes for machines. The model predicts that innovation-driven growth leads to an increasing population share of college graduates, increasing income and wealth inequality, and a declining labor share. We use the model to analyze the effects of redistribution. We show that it is difficult to improve income of low-skilled individuals as long as both technology and education are endogenous. This is true irrespective of whether redistribution is financed by progressive wage taxation or by a robot tax. Only when higher education is stationary, redistribution unambiguously benefits the poor. We show that education subsidies affect the economy differently depending on their mode of funding and that they may actually reduce education. Finally, we extend the model by fair wage concerns and show how automation could induce involuntary low-skilled unemployment.

Keywords: Automation; Innovation-Driven Growth; Inequality; Wealth Concentration; Unemployment; Policy Responses (search for similar items in EconPapers)
JEL-codes: E23 E25 O31 O33 O40 (search for similar items in EconPapers)
Date: 2019
New Economics Papers: this item is included in nep-gro, nep-ino and nep-mac
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (25)

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Related works:
Journal Article: Innovation, automation, and inequality: Policy challenges in the race against the machine (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:glodps:320

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