nep-ipr New Economics Papers
on Intellectual Property Rights
Issue of 2018‒06‒18
three papers chosen by
Giovanni Ramello
Università degli Studi del Piemonte Orientale “Amedeo Avogadro”

  1. Is it just Luring Reported Profit? The Case of European Patent Boxes By Marko Köthenbürger; Federica Liberini; Michael Stimmelmayr
  2. That's classified! Inventing a new patent taxonomy By Billington, Stephen D.; Hanna, Alan J.
  3. Impact of counterfeiting on the performance of digital technology companies By Nikolaus Thumm; Vincenzo Butticè; Federico Caviggioli; Chiara Franzoni; Giuseppe, Scellato

  1. By: Marko Köthenbürger; Federica Liberini; Michael Stimmelmayr
    Abstract: Patent box regimes have become increasingly popular as an instrument to attract taxable income from intellectual property (IP). This paper assesses the quantitative impact of patent box regimes on profit shifting by multinational enterprises (MNEs). We proxy the ability to access the tax benefit of the patent box by historical IP ownership. On average, affiliates belonging to MNEs with historical IP ownership report, after the introduction of a patent box, 8.5 percent higher profit compared to their counterparts with no IP ownership. Patent boxes do not only lure reported profit. The pre-tax profit change is a net effect and thus also accounts for reversed internal debt shifting out of the country and productivity changes. The overall behavioral adjustments might lower corporate tax revenues. Further, the design of the patent box and the existence of a tax haven affiliate within an MNE turn out to be critical for the amount of profits shifted.
    Keywords: corporate tax avoidance, patent box, multinational enterprise, profit shifting
    JEL: H25 H26 F23 C21 C23
    Date: 2018
  2. By: Billington, Stephen D.; Hanna, Alan J.
    Abstract: Patent studies inform our understanding of innovation. Any study of patenting involves classifying patent data according to a chosen taxonomy. The literature has produced numerous taxonomies, which means patents are being classified differently across studies. This potential inconsistency is compounded by a lack of documentation provided on existing taxonomies, making them diffcult to replicate. Because of this, we develop a new patent taxonomy using machine learning techniques, and propose a new methodology to automate patent classification. We contrast existing taxonomies with our own upon a widely used patent dataset. In a regression analysis of patent classes upon patent characteristics, we show that classification bias exists: the size, statistical significance, and direction of association of coefficients depend upon how a patent dataset has been classified. We recommend investigators adopt our approach to ensure future studies are comparable and replicable.
    Keywords: Innovation,Invention,Machine Learning,Patents,Patent Classification,Taxonomy,Economic History
    JEL: K11 N24 N74 O31 O33
    Date: 2018
  3. By: Nikolaus Thumm (European Commission – JRC - IPTS); Vincenzo Butticè (School of Management, Politecnico di Milano); Federico Caviggioli (Department of Management and Production Engineering, Politecnico di Torino); Chiara Franzoni (School of Management, Politecnico di Milano); Giuseppe, Scellato (Department of Management and Production Engineering, Politecnico di Torino)
    Abstract: Counterfeiting activities target companies in various sectors, including digital technology companies, defined as companies that produce and/or commercialize at least one physical product that incorporates a digital technology, excluding the merchandising related to the company brands. Counterfeiting is a fraudulent activity that potentially damages the economic and innovation performance of companies and can pose major threats to global competition and economic growth. However, the actual impact of counterfeiting on the performance of companies has not been tested empirically, due to methodological problems, including the lack of data on counterfeiting at the firm-level. Furthermore, prior theoretical studies have speculated that counterfeiting could have in part a beneficial effect on the performance of companies, due to indirect advertising, calling for empirical investigations to shed light on the issue. The goal of the present study is to provide empirical evidence on the impact of counterfeiting on both the economic and innovative performance of digital technology companies at the firm-level and on the global scale. To this aim, a new database was created combining data on counterfeiting activities during 2011-2013 (OECD-EUIPO, 2016) with financial information and patent data from 2009 to 2015. The result is a firm-level database that enables unprecedented analyses on the impact of counterfeiting on performance of digital technology companies. About 9% of the seizures of counterfeits that were illegally traded across borders during 2011 2013 involved goods commercialized by digital technology companies, equivalent to about the 9.1% of the total value of seizures. Collectively, about 11% of companies affected by illegal international trade of counterfeits are digital technology companies. The majority of these (58%) are big corporations with Operating Revenues greater than USD 1 bn. These account for 77% of the number of total seizures, and 84% of the value of seizures related to the digital technology companies. SMEs, defined as those with Operating Revenues up to USD 50 million, represent 21% of digital technology companies targeted and account for 5% of total seizures and 6% of the total value of seizures. The industries mostly targeted are electronics (both consumers’ electronics and electronics for industrial use), automotive and digital media. The digital technology products commercialized in frauds of IPRs include computer hardware and electronic components, batteries, sensors, autoparts, optical instruments, videogames, and recording of movies and motion picture. About 34% of digital technology companies affected by international trade of counterfeits are located in the EU28 or EFTA, 41% are located in North America, 23% are located in Asia. Within the EU28, UK, Germany, France and Italy are the countries hosting the largest number of targeted digital technology companies. Within the EU28, Germany and UK, followed by Belgium and Ireland, are the most-common country of destination of seized counterfeits. The overwhelming majority of seized goods related to digital technology companies is imported from Asia. 51% of these are imported from China, 41% comes from Hong Kong, China, 3% from Singapore. Other economies of provenance account each for less than 1% of the seizures. The vast majority (93%) of seizures affecting digital technology companies are due to violations of trademarks, and only a minority are due to violations of design models (4%), and copyrights (2%). Less than 1% of the seizures are due to violations of patents. However, seizures enacted in defence of patents are those that have the highest mean value. The analysis of infringed companies with respect to a control samples of non-infringed companies indicates that counterfeiting targets specifically highly profitable companies, with high propensity to innovate. Indeed, digital technology companies are more likely to become target of counterfeiting when they have larger Operating Revenues, and when they perform at a higher level in terms of profitability (return on total assets), prior to the window of observation. Target companies also have on average larger patent portfolios, prior to the observation of counterfeiting activities. Digital technology companies located in EU28 are on average less likely than companies located outside of EU28 to be the target of counterfeiting activities. Results from impact analyses indicate lower growth rates of operating profits for digital technology companies targeted by counterfeiting with respect to control samples of firms not affected by counterfeiting. In particular the econometric models provide evidence of a negative impact of counterfeiting on both EBITDA (Earnings before interest taxes depreciation and amortisation) and EBIT (Earnings before interest taxes). This result is robust across different estimation methods, model specifications and time windows. The data reveals only a weak negative impact on operating revenues, with limited statistical confidence. Conversely, there is no significant evidence that counterfeiting affected the investment in Fixed Assets of targeted firms with respect to the control sample. The results about the negative impact of counterfeiting activities on operating profits are in line with reports of greater costs incurred by these companies to enact anti-counterfeiting strategies, reported in prior descriptive literature. These practices include the broadening of product ranges, with fewer scale-economies and the enactment of anti-infringement procedures, such as ‘conspicuous packaging’, more screening and origin certifications, development of licensing downstream retailers and direct self-enforcement aimed at limiting the circulation of counterfeits. Results do not provide support for the existence of indirect positive spillover effects, as hypothesised by the theoretical literature, according to which infringed companies might benefit from an advertising effect due to the greater diffusion of brands from the counterfeiting activities. Indeed, at least for what concerns digital technology companies, there is no evidence of any positive effect of infringement on sales of original products. The digital technology companies that were affected by counterfeiting on average increased their patent portfolios during the observation period, but less than the digital technology companies that were not affected by counterfeiting. However, the result is not robust to the inclusion of control variables and to the adoption of alternative measures of innovation performance (Intangible Assets). It certainly merits further research, once more data on counterfeiting become available. Overall, the results indicate that counterfeiting activities harm the economic performance of targeted digital technology companies, by eroding their operating profits. The effect on innovative performance is negative, but still inconclusive due to insufficient dataset, and cannot exclude that counterfeiting may harm the propensity to innovate of digital technology companies. The analysis rules-out the existence of any positive spillover from counterfeiting.
    Keywords: Counterfeiting, trade, trade seizures, digital technologies, economic performance, innovative performance, patents, trademarks
    JEL: F1 K42 L63 O25 O31 O32 O34 O39
    Date: 2018–05

This nep-ipr issue is ©2018 by Giovanni Ramello. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
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