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Modeling Knowledge Networks in Economic Geography: A Discussion of Four Empirical Strategies

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  • Tom Broekel

    ()

  • Pierre-Alexandre Balland
  • Martijn Burger
  • Frank van Oort

Abstract

The importance of network structures for the transmission of knowledge and the diffusion of technological change has been emphasized in economic geography. Since network structures drive the innovative and economic performance of actors in regional contexts, it is crucial to explain how networks form and evolve over time and how they facilitate inter-organizational learning and knowledge transfer. The analysis of relational dependent variables, however, requires specific statistical procedures. In this paper, we discuss four different models that have been used in economic geography to explain the spatial context of network structures and their dynamics. First, we review gravity models and their recent extensions and modifications to deal with the specific characteristics of networked relations. Second, we discuss the quadratic assignment procedure that has been developed in mathematical sociology for diminishing the bias induced by network dependencies. Third, we present exponential random graph models that not only allow dependence between observations, but also model such network dependencies explicitly. Finally, we deal with dynamic networks, by introducing stochastic actor oriented models. Strengths and weaknesses of the different approaches are discussed together with domains of applicability for the analysis of (knowledge) network structures and their dynamics.

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File URL: http://econ.geo.uu.nl/peeg/peeg1325.pdf
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Bibliographic Info

Paper provided by Utrecht University, Section of Economic Geography in its series Papers in Evolutionary Economic Geography (PEEG) with number 1325.

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Length: 31 pages
Date of creation: Dec 2013
Date of revision: Dec 2013
Handle: RePEc:egu:wpaper:1325

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Keywords: Economic geography; knowledge networks; network models; quadratic assignment procedure; gravity model; exponential random graph model; stochastic actor-oriented model;

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References

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  1. Roberto Basile & Roberta Capello & Andrea Caragliu, 2012. "Technological interdependence and regional growth in Europe: Proximity and synergy in knowledge spillovers," Papers in Regional Science, Wiley Blackwell, vol. 91(4), pages 697-722, November.
  2. Stefano Breschi & Francesco Lissoni, 2009. "Mobility of skilled workers and co-invention networks: an anatomy of localized knowledge flows," Journal of Economic Geography, Oxford University Press, vol. 9(4), pages 439-468, July.
  3. Kristian Behrens & Cem Ertur & Wilfried Koch, 2012. "‘Dual’ Gravity: Using Spatial Econometrics To Control For Multilateral Resistance," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(5), pages 773-794, 08.
  4. Elhanan Helpman & Marc Melitz & Yona Rubinstein, 2008. "Estimating Trade Flows: Trading Partners and Trading Volumes," The Quarterly Journal of Economics, MIT Press, vol. 123(2), pages 441-487, 05.
  5. Jarno Hoekman & Koen Frenken & Frank Oort, 2009. "The geography of collaborative knowledge production in Europe," The Annals of Regional Science, Springer, vol. 43(3), pages 721-738, September.
  6. Tom Broekel & Matté Hartog, 2013. "Explaining the Structure of Inter-Organizational Networks using Exponential Random Graph Models," Industry and Innovation, Taylor & Francis Journals, vol. 20(3), pages 277-295, April.
  7. Stanley Wasserman & Philippa Pattison, 1996. "Logit models and logistic regressions for social networks: I. An introduction to Markov graphs andp," Psychometrika, Springer, vol. 61(3), pages 401-425, September.
  8. Melo, Patricia C. & Graham, Daniel J. & Noland, Robert B., 2009. "A meta-analysis of estimates of urban agglomeration economies," Regional Science and Urban Economics, Elsevier, vol. 39(3), pages 332-342, May.
  9. Ron Boschma & Pierre-Alexandre Balland & Dieter Kogler, 2011. "A relational approach to knowledge spillovers in biotech. Network structures as drivers of inter-organizational citation patterns," Papers in Evolutionary Economic Geography (PEEG) 1120, Utrecht University, Section of Economic Geography, revised Dec 2011.
  10. David Dekker & David Krackhardt & Tom Snijders, 2007. "Sensitivity of MRQAP Tests to Collinearity and Autocorrelation Conditions," Psychometrika, Springer, vol. 72(4), pages 563-581, December.
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Cited by:
  1. Andrea Morescalchi & Fabio Pammolli & Orion Penner & Alexander M. Petersen & Massimo Riccaboni, 2014. "The evolution of networks of innovators within and across borders: Evidence from patent data," Working Papers 1/2014, IMT Institute for Advanced Studies Lucca, revised Jan 2014.

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