Conference Agenda
Overview and details of the sessions of this conference.
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Some information on the session logistics:
If not stated otherwise, the discussant is the following speaker, with the first speaker being the discussant of the last paper. The last speaker of each session is the session chair. (Exception: invited sessions)
Presenters should speak for no more than 20 minutes, and discussants should limit their remarks to no more than 5 minutes. The remaining time should be reserved for audience questions and the presenter’s responses. We suggest following these guidelines also in the (less common) 3-paper sessions in a 2-hour slot, to allow participants to move between sessions. Discussants are encouraged to avoid summarizing the paper. By focusing on a few questions and comments, the discussants can help start a broader discussion with the audience.
Only registered participants can attend this conference. Further information available on the congress website https://www.iseg.ulisboa.pt/en/event/iipf/ .
Venue address: ISEG - Lisbon School of Economics & Management, R. Francesinhas 21, 1200-675 Lisboa, Portugal
Please note that all times are shown in the time zone of the conference. The current conference time is: 17th Sept 2026, 11:39:15am WEST
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Daily Overview |
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A12: Artificial Intelligence and Local Government Performance Location: Room 113 (Francesinhas 1) | |
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The Smarter State? Artificial Intelligence and Modern State and Local Public Finance 1: University of California, Irvine, United States of America; 2: University of Tennessee, Knoxville, United States of America This paper examines how artificial intelligence reshapes subnational public finance through familiar channels posed by prior technological change. AI shifts income from labor toward capital and tax bases toward consumption and market-based allocation, raising issues like the recent discussion of the sales tax treatment of digital services. With respect to the use of AI, we argue that AI relaxes long-standing informational and administrative constraints in state and local taxation, enforcement, budgeting, and service delivery, while simultaneously strengthening scale economies. The ability to reduce costs at the depends critically on labor-intensive services such as K-12 education. At the same time, the use of AI may advantage larger jurisdictions because of the larger amounts of data that they have, but also potentially raising equity and transparency concerns and increasing the value of interstate cooperation to harness the scale advantages of having more data. AI amplifies— the classic trade-offs emphasized in fiscal federalism.
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