Geoadditive models for regional count data: an application to industrial location

Roberto Basile (), Luigi Benfratello and Davide Castellani

ERSA conference papers from European Regional Science Association

Abstract: We propose a geoadditive negative binomial model (Geo-NB-GAM) for regional count data which allows us to simultaneously address some important methodological issues, such as spatial clustering, nonlinearities and overdispersion. We apply this model to study location determinants of inward greenfield investments occurred over the 2003-2007 period in 249 European regions. The inclusion of a geoadditive component (a smooth spatial trend surface) permits us to control for spatial unobserved heterogeneity which induces spatial clustering. Allowing for nonlinearities reveals, in line with theoretical predictions, that the positive effect of agglomeration economies fades as the density of economic activities reaches some limit value. However, no matter how dense the economic activity becomes, our results suggest that congestion costs would never overcome positive agglomeration externalities. Keywords: Geoadditive models, Negative binomial models, Industrial location, European Union JEL classification: C14, C21, F14, F23

Date: 2012-10
New Economics Papers: this item is included in nep-geo
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

Downloads: (external link)
https://www-sre.wu.ac.at/ersa/ersaconfs/ersa12/e120821aFinal00085.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wiw:wiwrsa:ersa12p83

Access Statistics for this paper

More papers in ERSA conference papers from European Regional Science Association Welthandelsplatz 1, 1020 Vienna, Austria.
Bibliographic data for series maintained by Gunther Maier ().