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11 Am. U. Bus. L. Rev. 1 (2022-2023)
Empirical Evidence on Robot Taxation: Literature Review and Technical Analysis

handle is hein.journals/aubulrw11 and id is 10 raw text is: EMPIRICAL EVIDENCE ON ROBOT TAXATION: LITERATURE REVIEW AND TECHNICAL ANALYSIS BRET N. BOGENSCHNEIDER* The literature on robot taxation has continued to expand since 2018 with numerous articles now referring to empirical evidence. The evidence presented in prior studies comprises abstract modeling and statistical pattern reviews with no statistically significant findings reported to date. Notably, one article is an advocacy piece by a tech lobbyist who at one point purchased priority Google results for the search robot taxation. In some cases, technical errors are sufficient to reverse the stated results. Examples of error in empirical analyses include (i) motivated reasoning, such as the failure to model simpler or best explanations; (ii) lack of causal analysis; (iii) tax technical errors; (iv) omission of citations to conflicting theory or results; (v) errors in accounting methods; (vi) enhanced degrees offreedom in modeling parameters; and (vii) reliance on economic theories not reflecting robots as a fourth factor of production. The empirical evidence indicates that capital investment, such as in robots, occurs largely in higher tax nations, and that robot density is positively associated with high corporate tax rates, such as in Germany, Japan, South Korea, and the Nordic states, with little or no automation occurring in tax havens where the value of tax deductions for capital investment is zero. I. Introdu ction ................................................................................ . . 2 II. Technical Errors in the Empirical Evidence on R obot T axation ..................................................................... 7 III. Literature Review: Empirical Evidence on Robot Taxation ........................................................................18 A. Technequality (Statistical Trends in EU Public Finances Related to Robots).............................................18 * PhD; J.D.; LL.M. Associate Professor of Accounting, State University of New York, Plattsburgh. Thanks to participants in the Southeastern Institute for Operations Research and the Management Sciences 2021 conference. 1

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