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Identifying spatial nonstationarity in German regional firm start-up data


  • Sven Müller



Background: We investigate the relationship between the rate at which new firms are established and regional characteristics and whether this relationship is constant over space or not. The characteristics reflect (i) agglomerations that in turn are related to increasing returns to production and (ii) measures which are influenceable immediately by regional decision makers. Method: In order to account for spatial nonstationarity, we use the geographically weighted regression method for German start-up data on the geographical scale NUTS3. Moreover, we discuss significance test for locally varying regression coefficients. Results: We are able to verify the global positive relationship between production convexities—measured by population density and growth amongst others—and the start-up rate on a regional level (Kreise). Furthermore, we find the share of industrial real estate to have positive influence on the regional start-up rate. Finally, we find strong empirically evidence that there is spatial nonstationarity in the data and hence the assumed relationship varies locally. Conclusion: The results give evidence that spatial nonstationarity could not be neglected in the analysis of start-up rates. However, we suggest to develop global models that account—at least partially—for the underlying spatial nonstationarity by exogenous variables. Copyright Springer-Verlag 2012

Suggested Citation

  • Sven Müller, 2012. "Identifying spatial nonstationarity in German regional firm start-up data," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 32(2), pages 113-132, September.
  • Handle: RePEc:spr:jahrfr:v:32:y:2012:i:2:p:113-132
    DOI: 10.1007/s10037-012-0064-3

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    References listed on IDEAS

    1. David Wheeler & Michael Tiefelsdorf, 2005. "Multicollinearity and correlation among local regression coefficients in geographically weighted regression," Journal of Geographical Systems, Springer, vol. 7(2), pages 161-187, June.
    2. Krugman, Paul, 1991. "Increasing Returns and Economic Geography," Journal of Political Economy, University of Chicago Press, vol. 99(3), pages 483-499, June.
    3. C Brunsdon & A S Fotheringham & M Charlton, 1998. "Spatial Nonstationarity and Autoregressive Models," Environment and Planning A, , vol. 30(6), pages 957-973, June.
    4. Christopher Bitter & Gordon Mulligan & Sandy Dall’erba, 2007. "Incorporating spatial variation in housing attribute prices: a comparison of geographically weighted regression and the spatial expansion method," Journal of Geographical Systems, Springer, vol. 9(1), pages 7-27, April.
    5. David Audretsch & Michael Fritsch, 1999. "The Industry Component of Regional New Firm Formation Processes," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 15(3), pages 239-252, November.
    6. Stuart A. Foster & Wilpen L. Gorr, 1986. "An Adaptive Filter for Estimating Spatially-Varying Parameters: Application to Modeling Police Hours Spent in Response to Calls for Service," Management Science, INFORMS, vol. 32(7), pages 878-889, July.
    7. Partridge, Mark D. & Rickman, Dan S., 2007. "Persistent Pockets of Extreme American Poverty and Job Growth: Is There a Place-Based Policy Role?," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 32(1), pages 1-24, April.
    8. Gino Cattani & Johannes M. Pennings & Filippo Carlo Wezel, 2003. "Spatial and Temporal Heterogeneity in Founding Patterns," Organization Science, INFORMS, vol. 14(6), pages 670-685, December.
    9. Dominique Meurs & Cyriaque Edon, 2007. "France: A Limited Effect Of Regions On Public Wage Differentials?," Manchester School, University of Manchester, vol. 75(4), pages 479-500, July.
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