nep-ipr New Economics Papers
on Intellectual Property Rights
Issue of 2020‒11‒09
three papers chosen by
Giovanni Ramello
Università degli Studi del Piemonte Orientale “Amedeo Avogadro”

  1. Attracting Profit Shifting or Fostering Innovation? On Patent Boxes and R&D Subsidies By Andreas Haufler; Dirk Schindler
  2. Reliance on Science by Inventors: Hybrid Extraction of In-text Patent-to-Article Citations By Matt Marx; Aaron Fuegi
  3. Patent Policy and Economic Growth: A Survey By Chu, Angus C.

  1. By: Andreas Haufler; Dirk Schindler
    Abstract: Many countries have introduced patent box regimes in recent years, offering a reduced tax rate to businesses for their IP-related income. Patent boxes are supposed to increase innovative activity, but they are also suspected to aim at attracting inward profit shifting from multinational firms. In this paper, we analyze the effects of patent box regimes when countries can simultaneously use patent boxes and R&D subsidies to promote innovation. We show that when countries set their tax policies unilaterally, innovation is fostered, at the margin, only by the R&D subsidy. The patent box tax rate is instead targeted at attracting international profit shifting, and it is optimally set below the corporate tax rate. With cooperative tax setting, the optimal royalty tax rate is instead equal to, or even above, the statutory corporation tax. Hence, patent box regimes emerge in the decentralized policy equilibrium, but never under policy coordination. Enforcing a nexus principle, as proposed by the OECD, is helpful to mitigate harmful competition for paper profits, but it comes at the price of increased strategic competition in direct R&D subsidies to attract physical R&D units instead of intangible patents.
    Keywords: R&D investment, patent boxes, investment tax credits, profit shifting, tax competition
    JEL: H25 H87 F23
    Date: 2020
  2. By: Matt Marx; Aaron Fuegi
    Abstract: We curate and characterize a complete set of citations from patents to scientific articles, including nearly 16 million from the full text of USPTO and EPO patents. Combining heuristics and machine learning, we achieve 25% higher performance than machine learning alone. At 99.4% accuracy, coverage of 87.6% is achieved, and coverage above 90% with accuracy above 93%. Performance is evaluated with a set of 5,939 randomly-sampled, cross-verified “known good” citations, which the authors have never seen. We compare these “in-text” citations with the “official” citations on the front page of patents. In-text citations are more diverse temporally, geographically, and topically. They are less self-referential and less likely to be recycled from one patent to the next. That said, in-text citations have been overshadowed by front-page in the past few decades, dropping from 80% of all paper-to-patent citations to less than 40%. In replicating two published articles that use only citations on the front page of patents, we show that failing to capture those in the body text leads to understating the relationship between academic science and commercial invention. All patent-to-article citations, as well as the known-good test set, are available at
    JEL: O31 O32 O33 O34
    Date: 2020–10
  3. By: Chu, Angus C.
    Abstract: This survey provides a selective review of the literature on patent policy, innovation and economic growth. The patent system is a useful policy tool for stimulating innovation given its importance on technological progress and economic growth. However, the patent system is a multi-dimensional system, which features multiple patent policy instruments. In this survey, we review some of the commonly discussed patent policy instruments, such as patent length, patent breadth and blocking patents, and also use a canonical Schumpeterian growth model to demonstrate their different effects on innovation and the macroeconomy.
    Keywords: patent policy, innovation, economic growth
    JEL: O3 O4
    Date: 2020–10

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