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Stricter rules for banks can relieve real estate markets Exuberant price levels in the German real estate market could...
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IWH FDI Micro Database
IWH FDI Micro Database The IWH FDI Micro Database (FDI = Foreign Direct...
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East Germany
East Germany Rearguard Only investments in education will lead to a further catch-up ...
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The Minimum Wage Effects on Skilled Crafts Sector in Saxony-Anhalt ...
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Centre for Evidence-based Policy Advice
Centre for Evidence-based Policy Advice (IWH-CEP) ...
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Monitoring of Business Cycles for the Land Saxony-Anhalt
Monitoring of Business Cycles for the Land Saxony-Anhalt This data was generated by...
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Info Graphs Sometimes pictures say more than a thousand words. Therefore, we selected...
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Identifying Cooperation for Innovation―a Comparison of Data Sources
Michael Fritsch, Mirko Titze, Matthias Piontek
Industry and Innovation,
No. 6,
2020
Abstract
The value of social network analysis is critically dependent on the comprehensive and reliable identification of actors and their relationships. We compare regional knowledge networks based on different types of data sources, namely, co-patents, co-publications, and publicly subsidized collaborative R&D projects. Moreover, by combining these three data sources, we construct a multilayer network that provides a comprehensive picture of intraregional interactions. By comparing the networks based on the data sources, we address the problems of coverage and selection bias. We observe that using only one data source leads to a severe underestimation of regional knowledge interactions, especially those of private sector firms and independent researchers.
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Identifying Cooperation for Innovation – A Comparison of Data Sources
Michael Fritsch, Matthias Piontek, Mirko Titze
Abstract
The value of social network analysis is critically dependent on the comprehensive and reliable identification of actors and their relationships. We compare regional knowledge networks based on different types of data sources, namely, co-patents, co-publications, and publicly subsidised collaborative Research and Development projects. Moreover, by combining these three data sources, we construct a multilayer network that provides a comprehensive picture of intraregional interactions. By comparing the networks based on the data sources, we address the problems of coverage and selection bias. We observe that using only one data source leads to a severe underestimation of regional knowledge interactions, especially those of private sector firms and independent researchers. The key role of universities that connect many regional actors is identified in all three types of data.
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