Innovation, automation, and inequality: Policy challenges in the race against the machine

被引：87

作者：

Prettner, Klaus ^{[1
,2
]}

Strulik, Holger ^{[3
]}

机构：

[1] Univ Hohenheim, Inst Econ, D-70593 Stuttgart, Germany

[2] Global Labor Org, Essen, Germany

[3] Univ Gottingen, Dept Econ, Pl Gottinger Sieben 3, D-37073 Gottingen, Germany

来源：

JOURNAL OF MONETARY ECONOMICS | 2020年 / 116卷

关键词：

Automation; Education; Innovation-Driven growth; Inequality; Policy responses; BIASED TECHNOLOGICAL-CHANGE; JOB POLARIZATION; ECONOMIC-GROWTH; POPULATION; KNOWLEDGE; FUTURE; ROBOTS;

D O I：

10.1016/j.jmoneco.2019.10.012

中图分类号：

F8 [财政、金融];

学科分类号：

0202 ;

摘要：

The effects of automation on economic growth, education, and inequality are analyzed using an R&D-driven growth model with endogenous education in which high-skilled workers are complements to machines and low-skilled workers are substitutes for machines. The model predicts that automation leads to an increasing share of college graduates, increasing income and wealth inequality, and a declining labor share. We show that standard policy suggestions for the age of automation can trigger unintended side effects on inequality, growth, and welfare, irrespective of whether they are financed by progressive wage taxation or by a robot tax. (C) 2019 The Author(s). Published by Elsevier B.V.

引用

页码：249 / 265

页数：17

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