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Economic Impacts of Intellectual Property-Conditioned Government Incentives

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Table of Contents

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    Book Overview
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    Chapter 1 Introduction
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    Chapter 2 IP-Conditioned Government Incentives in China and the EU: A Comparative Analysis of Strategies and Impacts on Patent Quality
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    Chapter 3 IP-Conditioned Subsidies in Germany and the EU: Operation, Dangers, and Recommendations
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    Chapter 4 The Impact of Public Support for SMEs’ Patenting Activity: Empirical Evidence from Italy
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    Chapter 5 IP-Conditioned Tax Incentives: The Right Approach to Stimulate Innovation and R&D in the European Union?
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    Chapter 6 Incentives for Chinese Inventors: A Proposal for a New Inventor Remuneration Scheme with German Elements
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    Chapter 7 The Upsurge of Domestic Patent Applications in China: Is R&D Expenditure or Patent Subsidy Policy Responsible?
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    Chapter 8 The Evolving Environment for Intellectual Property Tax Management in China
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    Chapter 9 Evaluating Patent Promotion Policies in China: Consequences for Patent Quantity and Quality
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    Chapter 10 University Patent Licensing and Its Contribution to China’s National Innovation System
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    Chapter 11 A Study on the Effects of Intellectual Property Policies in China: Evidence from China’s ‘IP Demonstration City’ Program
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    Chapter 12 Invention Patents Are Not for Everyone: Developing Less Industrialized Regions in China with ‘Light’ Intellectual Property
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Title
Economic Impacts of Intellectual Property-Conditioned Government Incentives
Published by
Springer Singapore, January 2016
DOI 10.1007/978-981-10-1119-1
ISBNs
978-9-81-101117-7, 978-9-81-101119-1
Editors

Dan Prud’homme, Hefa Song

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 25 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 20%
Student > Master 4 16%
Student > Bachelor 3 12%
Researcher 3 12%
Student > Doctoral Student 2 8%
Other 3 12%
Unknown 5 20%
Readers by discipline Count As %
Social Sciences 6 24%
Business, Management and Accounting 5 20%
Economics, Econometrics and Finance 5 20%
Arts and Humanities 1 4%
Computer Science 1 4%
Other 2 8%
Unknown 5 20%