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)
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)
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: Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:zbw:glodps:320
Access Statistics for this paper
More papers in GLO Discussion Paper Series from Global Labor Organization (GLO) Contact information at EDIRC.
Bibliographic data for series maintained by ZBW - Leibniz Information Centre for Economics ().