<abstract xmlns="http://www.w3.org/1999/xhtml"><p> This study investigates the determinants of patent protection regimes with the use of the self-organizing map (SOM) method. Unlike any previous attempts in the existing literature, this paper takes into consideration the lack of analytical techniques in the past and tries to demonstrate a potential relationship between the patent protection and its determinants through the employment of a newer, more consistent method. The study consists of two main parts. Firstly, the patent protection strengths of 111 countries have been classified via a SOM-based model and it turns out that three types of clusters can be found around the world; low-, mid- and high-protection. The results also show that the densities of these clusters have dramatically changed in the post-1980 period. In the second part of the study, the determinants of the patent rights are examined for 49 developed and developing countries. After revisiting the older econometric models with recent data, this study also analyses the determinants with the SOM method. The findings suggest that there is a significant relationship between GDP per capita, human capital, R&amp;D, market freedom, political rights and patent protection for about two-thirds of the sample; which implies that the patent policies of these countries are in accordance with the selected economic and social factors.</p></abstract>

This study investigates the determinants of patent protection regimes with the use of the self-organizing map (SOM) method. Unlike any previous attempts in the existing literature, this paper takes into consideration the lack of analytical techniques in the past and tries to demonstrate a potential relationship between the patent protection and its determinants through the employment of a newer, more consistent method. The study consists of two main parts. Firstly, the patent protection strengths of 111 countries have been classified via a SOM-based model and it turns out that three types of clusters can be found around the world; low-, mid- and high-protection. The results also show that the densities of these clusters have dramatically changed in the post-1980 period. In the second part of the study, the determinants of the patent rights are examined for 49 developed and developing countries. After revisiting the older econometric models with recent data, this study also analyses the determinants with the SOM method. The findings suggest that there is a significant relationship between GDP per capita, human capital, R&D, market freedom, political rights and patent protection for about two-thirds of the sample; which implies that the patent policies of these countries are in accordance with the selected economic and social factors.

Determinants of Patent Protection Regimes: A Self-Organizing Map Approach

Review of Economic Perspectives

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Determinants of Patent Protection Regimes: A Self-Organizing Map Approach"}

This study investigates the determinants of patent protection regimes with the use of the self-organizing map (SOM) method. Unlike any previous attempts in the...

Determinants of Patent Protection Regimes: A Self-Organizing Map Approach

Published Online: Sep 11, 2018

Page range: 261 - 283

Received: Dec 23, 2017

Accepted: May 16, 2018

DOI: https://doi.org/10.2478/revecp-2018-0013

Keywords
intellectual property rights, innovation, self-organizing map, artificial neural networks

© 2018 Caner Demir, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Determinants of Patent Protection Regimes: A Self-Organizing Map Approach

Published Online: Sep 11, 2018

Page range: 261 - 283

Received: Dec 23, 2017

Accepted: May 16, 2018

DOI: https://doi.org/10.2478/revecp-2018-0013

Keywordsintellectual property rights, innovation, self-organizing map, artificial neural networks

© 2018 Caner Demir, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Keywords
intellectual property rights, innovation, self-organizing map, artificial neural networks