The Smarter State? Artificial Intelligence and Modern State and Local Public Finance

David R. Agrawal and William F. Fox

No 12716, CESifo Working Paper Series from CESifo

Abstract: This paper examines how artificial intelligence (AI) reshapes subnational public finance, largely through familiar channels observed from prior technological change. Although some effects are novel, many issues surrounding the taxation of AI-related income and consumption parallel earlier challenges from e-commerce, digitalization, and remote work. AI shifts income from labor toward capital and reallocates tax bases toward consumption and market-based activity, raising questions such as the sales tax treatment of digital services. For governments, AI relaxes long-standing informational and administrative constraints in taxation, enforcement, budgeting, and service delivery, while strengthening scale economies. Cost reductions depend critically on labor-intensive sectors like K-12 education. However, government AI use may advantage larger jurisdictions with greater data access, raising equity and transparency concerns and increasing the value of interstate cooperation to harness scale economies from more data. Overall, AI reinforces—rather than overturns—the classic trade-offs emphasized in the fiscal federalism literature.

Keywords: artificial intelligence; state and local public finance; digital services; economies of scale; federalism (search for similar items in EconPapers)
JEL-codes: C55 H71 H72 H77 J45 (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:ces:ceswps:_12716

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