EconPapers    
Economics at your fingertips  
 

Modelling the location decisions of manufacturing firms with a spatial point process approach

Chiara Bocci and Emilia Rocco

Journal of Applied Statistics, 2016, vol. 43, issue 7, 1226-1239

Abstract: The paper is devoted to explore how the increasing availability of spatial micro-data, jointly with the diffusion of GIS software, allows to exploit micro-econometric methods based on stochastic spatial point processes in order to understand the factors that may influence the location decisions of new firms. By using the knowledge of the geographical coordinates of the newborn firms, their spatial distribution is treated as a realization of an inhomogeneous marked point process in the continuous space and the effect of spatial-varying factors on the location decisions is evaluated by parametrically modelling the intensity of the process. The study is motivated by the real issue of analysing the birth process of small and medium manufacturing firms in Tuscany, an Italian region, and it shows that the location choices of the new Tuscan firms is influenced on the one hand by the availability of infrastructures and the level of accessibility, and on the other by the presence and the characteristics of the existing firms. Moreover, the effect of these factors varies with the size and the level of technology of the new firms. Besides the specific Tuscan result, the study shows the potentiality of the described micro-econometric approach for the analysis of the spatial dynamics of firms.

Date: 2016
References: Add references at CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://hdl.handle.net/10.1080/02664763.2015.1093612 (text/html)
Access to full text is restricted to subscribers.

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:taf:japsta:v:43:y:2016:i:7:p:1226-1239

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20

DOI: 10.1080/02664763.2015.1093612

Access Statistics for this article

Journal of Applied Statistics is currently edited by Robert Aykroyd

More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().

 
Page updated 2025-03-20
Handle: RePEc:taf:japsta:v:43:y:2016:i:7:p:1226-1239