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Analysing and predicting micro-location patterns of software firms
Kinne, Jan
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Resch, Bernd
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URL:
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https://ub-madoc.bib.uni-mannheim.de/43818
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URN:
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urn:nbn:de:bsz:180-madoc-438180
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Dokumenttyp:
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Arbeitspapier
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Erscheinungsjahr:
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2017
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Titel einer Zeitschrift oder einer Reihe:
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ZEW Discussion Papers
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Band/Volume:
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17-063
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Ort der Veröffentlichung:
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Mannheim
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Sprache der Veröffentlichung:
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Englisch
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Einrichtung:
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Sonstige Einrichtungen > ZEW - Leibniz-Zentrum für Europäische Wirtschaftsforschung
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MADOC-Schriftenreihe:
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Veröffentlichungen des ZEW (Leibniz-Zentrum für Europäische Wirtschaftsforschung) > ZEW Discussion Papers
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Fachgebiet:
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330 Wirtschaft
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Fachklassifikation:
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JEL:
R12 , L86 , R30,
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Freie Schlagwörter (Englisch):
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Firm location , location factors , software industry , microgeography , OpenStreetMap (OSM) , prediction , Volunteered Geographic Information (VGI)
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Abstract:
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While the effects of non-geographic aggregation on inference are well studied in economics, research
on geographic aggregation is rather scarce. This knowledge gap together with the use of aggregated
spatial units in previous firm location studies result in a lack of understanding of firm location determinants at the microgeographic level. Suitable data for microgeographic location analysis has become available only recently through the emergence of Volunteered Geographic Information (VGI), especially the OpenStreetMap (OSM) project, and the increasing availability of official (open) geodata. In this paper, we use a comprehensive dataset of three million street-level geocoded firm observations to explore the location pattern of software firms in an Exploratory Spatial Data Analysis (ESDA). Based on the ESDA results, we develop a software firm location prediction model using Poisson regression and OSM data. Our findings demonstrate that the model yields plausible predictions and OSM data is suitable for microgeographic location analysis. Our results
also show that non-aggregated data can be used to detect information on location determinants, which are superimposed when aggregated spatial units are analysed, and that some findings of previous firm location studies are not robust at the microgeographic level. However, we also conclude that the lack of high-resolution geodata on socio-economic population characteristics causes systematic prediction errors, especially in cities with diverse and segregated populations.
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