Document Type
Article
Publication Date
Spring 2022
ISSN
0019-6665
Publisher
Indiana University School of Law - Bloomington
Language
en-US
Abstract
The Federal Income Tax Code has become increasingly complex over time with the implication that many taxpayers no longer understand the connection between their life decisions and their taxes. Some commentators have suggested that increasing computational complexity may be attributable in part to the proliferation of tax preparation software that renders such complexity manageable at filing time, but otherwise does nothing to mitigate the "black box" nature of the tax system. While such complexity and opacity undercut explicit incentives embedded in the Code, make planning more difficult, and undermine political accountability for taxes, they may also reduce the inefficient distortion or deadweight loss of the income tax, particularly with respect to higher-income taxpayers.
This Article argues that technology represents a potential response to tax complexity and opacity as well as a contributing factor. It argues that tax planning software can and likely will be used to restore 'functional transparency" to the Code, for good or bad, alerting taxpayers to explicit incentives, allowing taxpayers to easily determine the tax consequences of their life decisions, and providing a means for improving fiscal citizenship, but also highlighting tax burdens in such a way as to increase deadweight loss. This Article also makes the case for government provision or subsidization of planning software targeted at lower-income taxpayers. Such a targeted approach will help level the tax planning playing field and improve the take-up of tax incentives by this population, while avoiding facilitating social welfare reducing tax planning by higher-income taxpayers.
Recommended Citation
David I. Walker,
Tax Complexity and Technology
,
in
97
Indiana Law Journal
1095
(2022).
Available at:
https://scholarship.law.bu.edu/faculty_scholarship/979
Working paper available on SSRN
Comments
Please do not cite without author’s permission.
Updated with published version of article on 10/6/22
Working paper available on SSRN