nep-ipr New Economics Papers
on Intellectual Property Rights
Issue of 2015‒10‒17
four papers chosen by
Giovanni Ramello
Università degli Studi del Piemonte Orientale “Amedeo Avogadro”

  1. Economic Development and Stage-Dependent IPR By Bharat Diwakar; Gilad Sorek
  2. The paradox of openness revisited : collaborative innovation and patenting by UK innovators By Arora A.; Athreye S.; Huang C.
  3. A patent search strategy based on machine learning for the emerging field of service robotics By Kreuchauff, Florian; Korzinov, Vladimir
  4. Store Brands and the Role of Advertising By Griffith, Rachel; Krol, Michal; Smith, Kate

  1. By: Bharat Diwakar; Gilad Sorek
    Abstract: We study growth-maximizing Intellectual Property Rights (IPR) policy for developing economy in a close Overlapping-Generations model. We first show that R&D-based growth in such economy is subject to threshold externalities and transitional dynamics. Then we show that the IPR policy that maximizes output growth rates is stage-dependent: in early phases of development weak IPR protection may be necessary to sustain and to fasten economic growth. This is because weaker IPR protection shifts income from the old to the young generation and thereby enhancing saving and investment, which otherwise are insu¢cient to initiate growth. However as the economy develops and growth sustains optimal IPR protection tightens.
    Keywords: Stage-Dependent IPR, OLG, Poverty Trap, Growth
    JEL: O31 O34
    Date: 2015–10
  2. By: Arora A.; Athreye S.; Huang C. (UNU-MERIT)
    Abstract: We revisit the paradox of openness in the literature which consists of two conflicting views on the link between patenting and open innovation-the spillover prevention and the organisational openness views. We use the data from the Survey of Innovation and Patent Use and the Community Innovation Survey CIS6 in the UK to assess the empirical support for the distinct predictions of these theories. We argue that both patenting and external sourcing openness are jointly-determined decisions made by firms. Their relationship is contingent upon whether the firms are technically superior to their rivals and lead in the market or not. Leading firms are more vulnerable to unintended knowledge spillovers during collaboration as compared to followers, and consequently, the increase in patenting due to openness is higher for leaders than for followers. We develop a simple framework that allows us to formally derive the empirical implications of this hypothesis and test it by estimating whether the reduced form relationship between patenting and collaboration is stronger for leaders than for followers.
    Keywords: Management of Technological Innovation and R&D; Intellectual Property Rights;
    JEL: O32 O34
    Date: 2015
  3. By: Kreuchauff, Florian; Korzinov, Vladimir
    Abstract: Emerging technologies are in the core focus of supra-national innovation policies. These strongly rely on credible data bases for being effective and efficient. However, since emerging technologies are not yet part of any official industry, patent or trademark classification systems, delineating boundaries to measure their early development stage is a nontrivial task. This paper is aimed to present a methodology to automatically classify patents as concerning service robots. We introduce a synergy of a traditional technology identification process, namely keyword extraction and verification by an expert community, with a machine learning algorithm. The result is a novel possibility to allocate patents which (1) reduces expert bias regarding vested interests on lexical query methods, (2) avoids problems with citational approaches, and (3) facilitates evolutionary changes. Based upon a small core set of worldwide service robotics patent applications we derive apt n-gram frequency vectors and train a support vector machine (SVM), relying only on titles, abstracts and IPC categorization of each document. Altering the utilized Kernel functions and respective parameters we reach a recall level of 83% and precision level of 85%.
    Keywords: Service Robotics,Search Strategy,Patent Query,Data Mining,Machine Learning,Support Vector Machine
    JEL: C02 C18 C45
    Date: 2015
  4. By: Griffith, Rachel; Krol, Michal; Smith, Kate
    Abstract: Store brands are products over which a retailer (rather than a manufacturer) takes certain strategic decisions; we study the incentives of a retailer to advertise its store brands. We use detailed data on the British grocery market to document the considerable variation in store brand penetration across product categories and retailers. We develop a model that relates the pricing and advertising decisions of retailers and manufacturers to primitive characteristics of the category, and in particular the way that advertising affects consumer demand. We present empirical evidence that is consistent with several predictions from the model.
    Keywords: advertising; store brands
    JEL: D21 D22 M37
    Date: 2015–10

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