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Causal dynamic effects in regional systems of technological activities: a SVAR approach

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Abstract

This paper analyzes the causal relationships in regional technological systems within a structural vector autoregression framework. Applying a data-driven independent component analysis, it shows how the regional dynamics of economic, research, innovation and educational activities affect each other instantaneously and over time. By matching differently classified data on employees, patents and graduates, the analysis is based on a unique database which embraces multi-dimensional aspects for five clearly separated industries. The findings on how industry specific growth processes unfold are explained by referring to the type of industry and its knowledge base. For instance, it is found that more engineering-oriented industries like machine tools show a success-driven pattern, whereas science-based industries show a stronger dependence on universities. Such knowledge on the causal relations is of utmost relevance to the design and implementation of policy instruments.

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Notes

  1. In case of non-normal distributed random variables, this requirement is stronger than linear uncorrelatedness (Moneta et al. 2013).

  2. The named authors also provide an implementation of the algorithm for R, which was adapted for the use in this paper (http://www.cs.helsinki.fi/u/entner/VARLiNGAM).

  3. For statistical reasons, the number of R&D employees is subtracted from Empl, because they build their own variable (RD).

  4. Using Moran’s I test statistics, spatial autocorrelation in the growth rates variables was not found to be present.

  5. The current version is an update of the concordance originally published by Schmoch et al. (2003) and was obtained directly from the author. For a full list see Brenner and Schlump (2013).

  6. The bivariate correlation coefficients between the growth variables \(\tilde{\hbox {g}}_{r,i,t}\) are all below the threshold of 0.70 and, except for the pair Empl and RD, tend to be rather small. Hence, multicollinearity is not an issue and the variables seem to reflect different facets of regional economic development.

  7. To ensure statistical reliability, growth events which are based on less than five units of their corresponding level variable are excluded from the analysis. An unbalanced panel is possible, unless in one single region-year the values for all variables are available (Coad et al. 2012).

  8. We thank Alex Coad for pointing this out.

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Authors and Affiliations

  1. Department of Geography, Philipps University of Marburg, Deutschhausstr. 10, 35032, Marburg, Germany

    Thomas Brenner & Matthias Duschl

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  1. Thomas Brenner
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  2. Matthias Duschl
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Correspondence to Thomas Brenner.

Appendix

Appendix

See Fig. 4.

Fig. 4
figure 4

Frequency distribution of alternative causal orderings resulting from the bootstrapping analysis \((n = 500)\). The number indicate the ordering of the variables, with 1 = Empl, 2 = RD, 3 = Pat and 4 = Grad

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Brenner, T., Duschl, M. Causal dynamic effects in regional systems of technological activities: a SVAR approach. Ann Reg Sci 55, 103–130 (2015). https://doi.org/10.1007/s00168-015-0678-9

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