Preparing for the (Non-Existent?) Future of Work

Author:

Korinek Anton1,Juelfs Megan2

Affiliation:

1. Economics, University of Virginia

2. Darden School of Business, University of Virginia

Abstract

Abstract This chapter analyzes how to set up institutions that future-proof our society for a scenario of ever-more-intelligent autonomous machines that substitute for human labor and drive down wages. It lays out three concerns arising from such a scenario and evaluates recent predictions and objections to these concerns. Then it analyzes how to allocate work and income if these concerns start to materialize. As the income produced by autonomous machines rises and the value of labor declines, it is optimal to phase out work, beginning with workers who have low labor productivity and job satisfaction, as they have a comparative advantage in enjoying leisure. This is in stark contrast to welfare systems that force individuals with low labor productivity to work. If there are significant wage declines, avoiding mass misery will require other ways of distributing income than labor markets, whether via sufficiently well-distributed capital ownership or via benefits. Recipients could still engage in work for its own sake if they enjoy work amenities, such as structure, purpose, and meaning. If work gives rise to positive externalities, such as social connections or political stability, or if individuals undervalue the benefits of work because of internalities, then there is a role for public policy to encourage work. However, in the long run, it may be more desirable for society to develop alternative ways of providing these benefits.

Publisher

Oxford University Press

Reference51 articles.

1. Tasks, automation, and the rise in U.S. wage inequality.;Econometrica,2022

2. Aghion, P., Jones, B., & Jones, C. (2019). Artificial intelligence and economic growth. In A. Agrawal, J. Gans, and A. Goldfarb, (Eds.) The economics of artificial intelligence: An agenda (pp. 237–290). NBER and University of Chicago Press.

3. Altman, S. (2021). Moore’s Law for everything. https://moores.samaltman.com/.

4. The economic implications of learning by doing.;Review of Economic Studies,1962

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. EXISTENTIAL RISK FROM TRANSFORMATIVE AI: AN ECONOMIC PERSPECTIVE;Technological and Economic Development of Economy;2024-07-10

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3