The lost race against the machine: Automation, education, and inequality in an R&D-based growth model - Klaus Prettner; Holger Strulik (University of Göttingen, Center for European, Governance and Economic Development Research (cege), 2017)
We analyze the effects of automation and education on economic growth and inequality in an R&D-based growth model with two types of labor: high-skilled labor that is complementary to machines and low-skilled labor that is a substitute for machines. The model predicts that innovation-driven growth leads to increasing automation, an increasing skill premium, an increasing population share of college graduates, increasing income and wealth inequality, and a declining labor share. In contrast to conventional wisdom, our theory predicts that faster economic growth promotes inequality. Because education and technology are endogenous, redistribution to low-skilled individuals may actually not improve disposable low-skilled income, irrespective of whether it is financed by taxes on labor income or machine input in production. We extend the model by fair wage concerns and show how automation implies involuntary low-skilled unemployment.
Keywords: E23; E25; O31; O33; O40; 330; Automation; R&D-Based Growth; Inequality; Wealth Concentration; Unemployment; Redistribution