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Entrepreneurship, intellectual property and innovation ecosystems
Author(s) -
Diego Araújo Reis,
Fábio Rodrigues de Moura,
Iracema Machado de Aragão Gomes
Publication year - 2021
Publication title -
deleted journal
Language(s) - English
Resource type - Journals
ISSN - 2411-2933
DOI - 10.31686/ijier.vol9.iss2.2879
Subject(s) - entrepreneurship , intellectual property , ecosystem , index (typography) , originality , proxy (statistics) , business , economics , ecology , creativity , political science , biology , computer science , finance , world wide web , law , machine learning
This research aims to determine the relationship between entrepreneurship, intellectual property and innovation ecosystems at a global level. To assess the structural relationships between ecosystems, the unconditional quantile regressions using annual country data are estimated from two perspectives, namely: pooled data and data with fixed effects and time control. The Global Entrepreneurship Index (GEI), the US Chamber International IP Index (IPI) and the Global Innovation Index (GII) are used as a proxy for the entrepreneurship, intellectual property and innovation ecosystem, respectively. The results indicate that the entrepreneurship and intellectual property ecosystems has a causal relationship with the global innovation ecosystem. However, when control of individual and fixed time effects is included, the relationship between ecosystems is confirmed in just a few quantiles. The sterile results require efforts from public, private and other agents to improve the performance of ecosystems, especially to increase the generation of innovative assets. This study looks at ecosystems from a different perspective, and the results are relevant to policymakers looking to improve the ecosystems of entrepreneurship, intellectual property and innovation. The originality of this article lies in bringing together issues that are generally dealt with in theoretical and empirical literature in separate domains. The study of the relationship between ecosystems from global indexes remains a little explored field, despite the various alternative approaches already investigated.

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