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, 11:36:53am WEST
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Daily Overview |
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G01: Capital Gains Taxation: Design, Avoidance and Distortions Location: Room 101 (Francesinhas 1) | |
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5:00pm - 5:22pm
Tackling the Timing Trap in Capital Gains Taxation Skatteforsk - Norwegian Centre for Tax Research, Norway This paper addresses issues in the design of current capital gains tax systems and how to tackle them. Important among such issues is an incentive to delay realisation. We explore alternative policies to mitigate issues in capital gains taxation, such as taxation on accrual, interest rate adjustments on delayed tax payments, indexing of capital gains and a rate of return allowance, and applying tax rules based on accrual and tax payments on realization. Whereas capital gains are measurable on realisation, measuring capital gains before realisation poses a challenge. We address this by exploiting Norwegian tax register data and machine learning methods. From this, we show revenue and incidence effects of the proposed alternatives. WE SUBMIT ONLY EXTENDED ABSTRACT - FULL PAPER WILL BE READY BY AUGUST
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