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on Intellectual Property Rights |
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Issue of 2021‒06‒28
three papers chosen by Giovanni Ramello Università degli Studi del Piemonte Orientale “Amedeo Avogadro” |
| By: | Bernadette Power; Gavin C Reid |
| Abstract: | Using a large, longitudinal panel (2004-2011) of USA start-ups this paper shows the extent to which IP types (e.g. trademarks, patents, copyrights, outward licensing) enhance multidimensional performance. An ordered probit analysis (with random effects), corrected for sample selection bias, estimates performance to derive the following conclusions. First, trademarks and out-licensing IP types increase a firm’s chances of being a high performer, confirming the importance of certain forms of IP protection for start-ups. Second, patenting significantly reduces the chances of being a high performer, suggesting patenting has limited performance benefits for start-ups. Third, few performance synergies exist in the joint use of IP types, suggesting that strong complementarities among IP types are limited. While out-licensing patents and out-licensing copyrights certainly increase performance, out-licensing patents and out-licensing trademarks actually diminish it. Further, registering more trademarks and outlicensing more trademarks also diminishes performance, suggesting start-up firms should keep trademarks in-house. |
| Keywords: | Performance, firm start-ups, intellectual property, out-licensing, complementarities |
| JEL: | C55 D22 L25 O34 |
| Date: | 2021–04 |
| URL: | http://d.repec.org/n?u=RePEc:cbr:cbrwps:wp523&r= |
| By: | Juranek, Steffen (Dept. of Business and Management Science, Norwegian School of Economics); Otneim, Håkon (Dept. of Business and Management Science, Norwegian School of Economics) |
| Abstract: | We use machine learning methods to predict which patents end up at court using the population of US patents granted between 2002 and 2005. We analyze the role of the different dimensions of an empirical analysis for the performance of the prediction - the number of observations, the number of patent characteristics and the model choice. We find that the extending the set of patent characteristics has the biggest impact on the prediction performance. Small samples have not only a low predictive performance, their predictions are also particularly unstable. However, only samples of intermediate size are required for reasonably stable performance. The model choice matters, too, more sophisticated machine learning methods can provide additional value to a simple logistic regression. Our results provide practical advice to everyone building patent litigation models, e.g., for litigation insurance or patent management in more general. |
| Keywords: | Patents; litigation; prediction; machine learning |
| JEL: | K00 K41 O34 |
| Date: | 2021–06–22 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:nhhfms:2021_006&r= |
| By: | Colombo, Stefano; Ma, Siyu; Sen, Debapriya; Tauman, Yair |
| Abstract: | For an outside innovator with a finite number of buyers of the innovation, this paper compares two licensing schemes: (i) fixed fee, in which a licensee pays a fee to the innovator and (ii) ad valorem profit royalty, in which a licensee leaves a fraction of its profit with the innovator. We show these two schemes are equivalent in that for any number of licenses the innovator puts for sale, these two schemes give the same licensing revenue. We obtain this equivalence result in a general model with minimal structure. It is then applied in a Cournot oligopoly for an outside innovator. Finally, in a Cournot duopoly it is shown that when the innovator is one of the incumbent firms rather than an outsider, the equivalence result does not hold. |
| Keywords: | fixed fee. advlorem profit royalty, licensing schemes, auction, posted price, outside innovator |
| JEL: | D43 D44 D45 L13 L24 |
| Date: | 2021–06–03 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:108275&r= |