Shoulders and shadows of giants: intra-regional distribution of the digital industry in Germany

ABSTRACT

This paper investigates digital firm birth activity in municipalities in the urban hinterland of core cities in Germany. It conducts panel fixed-effect regressions for monocentric and polycentric urban labour market regions covering the years 1995–2017. The digital industry’s regional distribution is shaped significantly by the closest core cities: municipalities in monocentric urban regions (MURs) profit from urban population growth and universities’ general knowledge. Municipalities in polycentric urban regions (PURs), however, are affected by industry-specific externalities, that is, an above-average growth in the share of firm birth of their closest urban cores. Overall, agglomeration externalities experience spatial decay relative to the core size with all regions benefiting from their own industry-enhancing urbanization externalities as captured by population growth and universities.

KEYWORDS:

• firm birth
• entrepreneurship capital
• regional competition
• polycentric urban regions

JEL Clasification codes::

• O18
• R12
• R13
• R58

ACKNOWLEDGEMENT

The author would like to thank Christiane Hellmanzik, Maren Kaliske and Felix Dorfseifer for excellent comments and suggestions within the research process.

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the author.

Notes

1 Core cities are cities with more than 100,000 inhabitants and a surplus of inbound commuters. Additionally, the main commuter flow does not come from the neighbouring centre, as defined by BBSR (2022b). The terms ‘core cites’ and ‘centres’ are used interchangeably.

2 NACE codes 62.01.0, 62.01.1, 62.01.9, 62.02.0, 62.03.0, 62.09.0, 63.11.0 and 63.12.0.

3 First, the description of the identified ICT firms has been analysed and the most frequently used words related to information technology (IT) and software has been identified. These keywords are then used to obtain those firms operating on digital business models with the help of several word combinations. Further, firms that only distribute their products via a webpage have been excluded. For those firms, keywords related to ‘software development’ needed to be included. As an example, a firm registered in ‘Placement of workers’ has been included in the sample because the objective of the company is ‘the operation of a social networking platform for skills enhancement and marketing as well as the provision, brokerage and distribution of products and Internet-based services’. A full list of keywords and in/exclusion of firms can be obtained from the author upon request. Note that the keywords are in German.

4 Darmstadt/Frankfurt/Wiesbaden/Mainz; Düsseldorf/Duisburg/Krefeld/Mönchengladbach; Essen/Bochum/Dortmund/Hagen; Köln/Bonn; and Ludwigshafen/Mannheim; Nürnberg/Erlangen; Berlin/Potsdam.

5 Augsburg, Bielefeld, Braunschweig, Bremen, Bremerhaven, Chemnitz, Dresden, Erfurt, Freiburg, Göttingen, Halle, Hamburg, Hannover, Heidelberg, Heilbronn, Hildesheim, Ingolstadt, Jena, Karlsruhe, Kassel, Kiel, Koblenz, Leipzig, Lübeck, Magdeburg, München, Münster, Oldenburg, Osnabrück, Paderborn, Pforzheim, Regensburg, Reutlingen, Rostock, Saarbrücken, Salzgitter, Siegen, Stuttgart, Trier, Ulm, Wolfsburg and Würzburg.

6 The final dataset does not cover all municipalities in Germany, but excludes municipalities not belonging to core labour market regions. Core cities are excluded from the analysis.

7 Addresses have been geocoded with the geocode command in R.