nep-ipr New Economics Papers
on Intellectual Property Rights
Issue of 2023‒11‒20
five papers chosen by
Giovanni Ramello, Università degli Studi del Piemonte Orientale “Amedeo Avogadro”

  1. AI-Generated Inventions: Implications for the Patent System By Gaetan de Rassenfosse; Adam Jaffe; Melissa Wasserman
  2. Profit Shifting in the 21st Century: Multinationals’ Use of Intrafirm Patent Transfers By Mickenzie Bass; Jesse LaBelle; Ana Maria Santacreu
  3. Disentangling Business- and Tax-Motivated Bilateral Royalty Flows By Arjan Lejour; Maarten van 't Riet; Arjan M. Lejour
  4. The Economics of Copyright in the Digital Age By Christian Peukert; Margaritha Windisch
  5. Deriving Technology Indicators from Corporate Websites: A Comparative Assessment Using Patents By Sebastian Heinrich

  1. By: Gaetan de Rassenfosse (Ecole polytechnique federale de Lausanne); Adam Jaffe (Brandeis University); Melissa Wasserman (The University of Texas at Austin - School of Law)
    Abstract: This symposium Article discusses issues raised for patent processes and policy created by inventions generated by artificial intelligence (AI). The Article begins by examining the normative desirability of allowing patents on AI-generated inventions. While it is unclear whether patent protection is needed to incentivize the creation of AI-generated inventions, a stronger case can be made that AI-generated inventions should be patent eligible to encourage the commercialization and technology transfer of AI-generated inventions. Next, the Article examines how the emergence of AI inventions will alter patentability standards, and whether a differentiated patent system that treats AI-generated inventions differently from hu-man-generated inventions is normatively desirable. This Article concludes by considering the larger implications of allowing patents on AI-generated inventions, including changes to the patent examination process, a possible increase in the concentration of patent ownership and patent thickets, and potentially unlimited inventions.
    Keywords: generative AI; patent; intellectual property; invention
    JEL: K20 D23 O34
    Date: 2023–05
  2. By: Mickenzie Bass; Jesse LaBelle; Ana Maria Santacreu
    Abstract: An analysis indicates that a high percentage of U.S. patents that shifted to tax havens like Bermuda are intrafirm transfers. Such transfers may be a tax avoidance strategy by multinationals.
    Keywords: patents; tax havens; intrafirm patent transfers; tax avoidance; multinational corporations
    Date: 2023–09–12
  3. By: Arjan Lejour; Maarten van 't Riet; Arjan M. Lejour
    Abstract: Shifting intellectual property (IP) rights across jurisdictions is a well-known strategy of multinationals to reduce corporate income taxation. We investigate the extent to which the flows of remunerations for the use of IP rights are affected by differences in corporate income and withholding taxation. Using OECD data between 2014 and 2019, we determine the influence of bilateral tax rates on the IP-location. These rates result from a network analysis that distinguishes between the potential gains from direct shifting of IP rights and treaty shopping. The latter are gains for multinationals from exploiting lower withholding taxes by routing royalty flows through conduit countries. We use these bilateral tax gains to isolate the flows that could be only business-motivated. Next we apply a gravity framework with PPML estimators. We estimate that at least 18% of the royalty flows is motivated by tax planning in this period, which reduces tax revenues by 6.5 to 16 billion US dollar in 2018. We argue that both estimates are lower bounds due to missing observations. More reporting by OECD countries of flows to and from tax havens would improve the precision of the estimates. To the best of our knowledge these are the first estimates of worldwide tax avoidance with royalties.
    Keywords: bilateral royalty flows, international tax avoidance, treaty shopping, withholding tax, tax havens
    JEL: H25 H26 H32
    Date: 2023
  4. By: Christian Peukert; Margaritha Windisch
    Abstract: Intellectual property rights are fundamental to how economies organize innovation and steer the diffusion of knowledge. Copyright law, in particular, has developed constantly to keep up with emerging technologies and the interests of creators, consumers, and intermediaries of the different creative industries. We provide a synthesis of the literature on the law and economics of copyright in the digital age, with a particular focus on the available empirical evidence. First, we discuss the legal foundations of the copyright system and developments of length and scope throughout the era of digitization. Second, we review the literature on technological change with its opportunities and challenges for the stakeholders involved. We give special attention to empirical evidence on online copyright enforcement, changes in the supply of works due to digital technology, and the importance of creative re-use and new licensing and business models. We then set out avenues for further research identifying critical gaps in the literature regarding the scope of empirical copyright research, the effects of technology that enables algorithmic licensing, and copyright issues related to software, data and artificial intelligence.
    Keywords: copyright, digitization, technology, enforcement, licensing, business models, evidence
    JEL: K11 L82 L86
    Date: 2023
  5. By: Sebastian Heinrich (KOF Swiss Economic Institute, ETH Zurich, Switzerland)
    Abstract: This paper investigates the potential of indicators derived from corporate websites to measure technology related concepts. Using arti cial intelligence (AI) technology as a case in point, I construct a 24-year panel combining the texts of websites and patent portfolios for over 1, 000 large companies. By identifying AI exposure with a comprehensive keyword set, I show that website and patent data are strongly related, suggesting that corporate websites constitute a promising data source to trace AI technologies.
    Keywords: corporate website, patent portfolio, technology indicator, text data, artificial intelligence
    JEL: C81 O31 O33
    Date: 2023–07

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