AI, Automation and Taxation

Spencer Bastani and Daniel Waldenström

No 11084, CESifo Working Paper Series from CESifo

Abstract: This paper examines the implications of Artificial Intelligence (AI) and automation for the taxation of labor and capital in advanced economies. It synthesizes empirical evidence on worker displacement, productivity, and income inequality, as well as theoretical frameworks for optimal taxation. Implications for tax policy are discussed, focusing on the level of capital taxes and the progressivity of labor taxes. While there may be a need to adjust the level of capital taxes and the structure of labor income taxation, there are potential drawbacks of overly progressive taxation and universal basic income schemes that could undermine work incentives, economic growth, and long-term household welfare. Some of the challenges posed by AI and automation may also be better addressed through regulatory measures rather than tax policy.

Keywords: AI; automation; inequality; labor share; optimal taxation; tax progressivity (search for similar items in EconPapers)
JEL-codes: H21 H30 O33 (search for similar items in EconPapers)
Date: 2024
New Economics Papers: this item is included in nep-acc, nep-ain, nep-mac, nep-pbe, nep-pub and nep-tid
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.cesifo.org/DocDL/cesifo1_wp11084.pdf (application/pdf)

Related works:
Chapter: AI, automation and taxation (2024)
Working Paper: AI, Automation and Taxation (2024)
Working Paper: AI, Automation, and Taxation (2024)
Working Paper: AI, Automation and Taxation (2024)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ces:ceswps:_11084

Access Statistics for this paper

More papers in CESifo Working Paper Series from CESifo Contact information at EDIRC.
Bibliographic data for series maintained by Klaus Wohlrabe ().