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Determinants of Cross-Regional R&D Collaboration Networks: An Application of Exponential Random Graph Models

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The Geography of Networks and R&D Collaborations

Part of the book series: Advances in Spatial Science ((ADVSPATIAL))

Abstract

This study investigates the usefulness of exponential random graph models (ERGM) to analyze the determinants of cross-regional R&D collaboration networks. Using spatial interaction models, most research on R&D collaboration between regions is constrained to focus on determinants at the node level (e.g. R&D activity of a region) and dyad level (e.g. geographical distance between regions). ERGMs represent a new set of network analysis techniques that has been developed in recent years in mathematical sociology. In contrast to spatial interaction models, ERGMs additionally allow considering determinants at the structural network level while still only requiring cross-sectional network data.

The usefulness of ERGMs is illustrated by an empirical study on the structure of the cross-regional R&D collaboration network of the German chemical industry. The empirical results confirm the importance of determinants at all three levels. It is shown that in addition to determinants at the node and dyad level, the structural network level determinant “triadic closure” helps in explaining the structure of the network. That is, regions that are indirectly linked to each other are more likely to be directly linked as well.

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Notes

  1. 1.

    The relational data derived from the 5th, 6th, and 7th EU-Framework Programmes are (currently) a good example in this respect. While they represent longitudinal data, it covers only a limited time-period (1998–2013). Of course, this may change when data on future programs will become available.

  2. 2.

    This figure is based on the number of executing organizations (“Ausführende Stelle”) as given in the data. Many of these organizations are part of larger organizations. This has however little relevance for the results as all data are aggregated to the regional level.

  3. 3.

    The latest version of the “Patentatlas” was published in 2006 and includes the patent data up to 2005. We use the aggregated numbers for 2001–2005 to minimize annual fluctuation.

  4. 4.

    The employment numbers are relatively stable over time. Using data for a single year is therefore considered appropriate.

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Correspondence to Tom Broekel .

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Appendix

Appendix

Fig. 4.2
figure 2

Goodness of fit of exponential random graph model with dyad level, node level + structural network level variables

Fig. 4.3
figure 3

MCMC-Statistics of exponential random graph model with dyad level, node level and structural network level variables

Fig. 4.4
figure 4

MCMC-Statistics of exponential random graph model with dyad level, node level and structural network level variables

Fig. 4.5
figure 5

MCMC-Statistics of exponential random graph model with dyad level, node level and structural network level variables

Fig. 4.6
figure 6

MCMC-Statistics of exponential random graph model with dyad level, node level and structural network level variables

Table 4.2 Descriptives of empirical variables

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Broekel, T., Hartog, M. (2013). Determinants of Cross-Regional R&D Collaboration Networks: An Application of Exponential Random Graph Models. In: Scherngell, T. (eds) The Geography of Networks and R&D Collaborations. Advances in Spatial Science. Springer, Cham. https://doi.org/10.1007/978-3-319-02699-2_4

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