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on Intellectual Property Rights |
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Issue of 2023‒08‒21
four papers chosen by Giovanni Ramello Università degli Studi del Piemonte Orientale “Amedeo Avogadro” |
| By: | Serguey Braguinsky; Joonkyu Choi; Yuheng Ding; Karam Jo; Seula Kim |
| Abstract: | We use the U.S. patent data merged with firm-level datasets to establish new facts about the role of mega firms in generating “novel patents”—innovations that introduce new combinations of technology components for the first time. While the importance of mega firms in novel patents had been declining until about 2000, it has strongly rebounded since then. The timing of this turnaround coincided with the ascendance of firms that newly became mega firms in the 2000s, and a shift in the technological contents, characterized by increasing integration of Information and Communication Technology (ICT) and non-ICT components. Mega firms also generate a disproportionately large number of “hits”—novel patents that lead to the largest numbers of follow-on patents (subsequent patents that use the same combinations of technology components as the first novel patent)—and their hits tend to generate more follow-on patents assigned to other firms when compared to hits generated by non-mega firms. Overall, our findings suggest that mega firms play an increasingly important role in generating new technological trajectories in recent years, especially in combining ICT with non-ICT components. |
| JEL: | L10 O30 O32 O33 |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:31460&r=ipr |
| By: | Rishi Sharma; Joel Slemrod; Michael Stimmelmayr |
| Abstract: | We develop a positive model of multinational firm behavior and analyze a firm’s incentive to transfer an intellectual property (IP) right of uncertain value offshore ex ante, i.e. before its success or failure is realized. Our analysis highlights two major aspects of this decision. First, an asymmetric treatment of project gains and losses in the home country creates an incentive to transfer IP to a foreign lowtax country to avoid potentially negative profits at home. These incentives exist even when IP is priced at a fair arms-length price and are further strengthened in the presence of R&D tax incentives. Second, when multinationals have private information about the probability of project success, they have an incentive to transfer their most promising IP ex ante. |
| JEL: | H25 |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:31452&r=ipr |
| By: | Leogrande, Angelo; Leogrande, Domenico; Costantiello, Alberto |
| Abstract: | In the following article, we estimate the value of Patent Applications-PA in the context of the Environmental, Social and Governance-ESG model at world level. We use data from World Bank for 193 countries in the period 2011-2021. We found that PA is positively associated, among others, to “ CO 2 Emissions ” and “ Mammal Species Threated ”, and negatively associated among others to “ Hospital Beds ” and “ Research and Development Expenditures ”. Furthermore, we found that at aggregate level PA is negatively associated to each macro component of the ESG model i.e.: Environment, Social and Governance. Furthermore, we have applied eight different machine-learning algorithms for the prediction of the future value of PA. We found that the best predictive algorithm is the Simple Regression Tree in terms of minimization of MAE, RMSE and MSE and maximization of R-squared. The value of PA is predicted to growth by an average of 9.82% for the analysed countries. |
| Keywords: | Political Processes, Legislatures, Corruption |
| JEL: | D7 D70 D72 D73 D78 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:esprep:273495&r=ipr |
| By: | Mu-Jeung Yang |
| Abstract: | Several ongoing survey programs by the US Census Bureau are based on sampling of establishments based on forecasted size. Current practice by the Census is to mainly rely on past size as predictor of future size. This project uses responsiveness to patent news shocks as additional forecast variables for establishment size and evaluates using such variables by showing out-of-sample prediction performance using machine learning. |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:cen:tnotes:23-13&r=ipr |