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An Application of Spatial Poisson Models to Manufacturing Investment Location Analysis

Published online by Cambridge University Press:  28 April 2015

Dayton M. Lambert ,
Kevin T. McNamara  and
Megan I. Garrett
Dayton M. Lambert
Affiliation:
Economic Research Service at the U.S. Department of Agriculture
Kevin T. McNamara
Affiliation:
Department of Agricultural Economics, Purdue University, West Lafayette, IN
Megan I. Garrett
Affiliation:
Agricel, Effingham, IL
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Abstract

The influence product markets, agglomeration, labor, infrastructure, and government fiscal attributes had on manufacturing investment flows in Indiana between 2000 and 2004 were estimated using Poisson regression, geographically weighted regression, and a spatial general linear model. Counties with access to urbanization economies, product markets, available labor, a high-quality workforce, and transport infrastructure were more likely to attract manufacturing investment. These effects were magnified to some extent when inter-county spatial effects were modeled. The distributional assumptions of the spatial models are different, but both methods are useful for understanding the spatial context of the factors influencing manufacturing investment flows.

Keywords

geographically weighted regressionlocation determinantslocation theorymanufacturing site selectionPoisson spatial generalized linear modelR1R3
Type
Articles
Information
Journal of Agricultural and Applied Economics , Volume 38 , Issue 1 , April 2006 , pp. 105 - 121
Copyright
Copyright © Southern Agricultural Economics Association 2006

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