Relocation Model for Logistic Firms with Consideration of Spatial Effects
N Cao Y, Nagaoka University of Technology, JP
In this framework, spatial effects such as the correlation among firms in deterministic terms and the spatial correlation among zones in the error term are captured by mixed logit models.
Understanding the factors that play a key role in the decisions that businesses make on where to locate is very important in order to determine the most effective mechanisms to attract business, and thereby maintain a healthy economy. Numerous studies have examined the relative significance of various factors in the process of selecting a business location by developing theoretical models to explain the different facets of the process. However, less attention has been given to the use of a mixed logit model in particular, and discrete choice models in general, in the analysis of spatial effects for the relocation decision behavior of logistic companies.
In fact, the choice of location is generally influenced by factors such as the characteristics of the firm, the attributes of the zones being considered, and transportation accessibility. In addition, the interplay among logistic firms can be view as interactions in a space. A firm, while making a relocation decision, does not act in isolation; in contrast, firms are influenced by others who located nearby. Therefore, how the spatial effect on the firm relocation choice behavior should be considered carefully in the research process.
This paper presents an overview of a relocation choice model for logistic firms; the presented conceptual model distinguishes a separate relocation decision and a conditional decision for an alternative new location. This means that the joint decision of firm i to move and to relocate to location j is the product of the probability firm i will move and the conditional probability that firm i chooses location j from a subset of alternatives. This model takes into account spatial effects in the conditional decision for an alternative new location. A modeling framework is developed to analyze decisions regarding relocation choice for logistic firms based on mixed logit models. In this framework, spatial effects such as the correlation among firms in deterministic terms and the spatial correlation among zones in the error term are captured by mixed logit models. In addition, the dynamics of the consumption of commodities between consumers and suppliers is considered.
In a case study, the developed framework is applied to the Tokyo metropolitan area. The data set used in this case study was compiled from numerous sources such as the Establishment and Enterprise Census 2004 (EEC), the Road Traffic Census 2004 (RTC) survey, and the Tokyo Metropolitan Goods Movement Survey 2004 (TMGMS). These surveys are summarized as The 2004 EEC data provides information on all enterprises that operate in Japan, except for small firms with less than five employees and some particular types of industry. The 2004 RTC survey aims to characterize the usage of automobiles and quantify the volumes of traffic in Japan. The 2004 TMGMS surveys includes four surveys: Survey A on Firms? Characteristics, Survey B on Truck Behavior, Survey C on Goods Carried in and out, and Survey D on Firms? Locations.
All estimations in this research were implemented using the GAUSS programming language. Bhat?s Gauss code for a scrambled Halton sequence was modified and integrated into our maximum simulated likelihood estimation code. The results of the study indicate that for relocation decision, the number of employees is a more important determinant for manufacturers than it is for retailers and product wholesalers. Additionally, the results indicate that the spatial parameters are statistically significant in terms of the t-statistics with reasonable signs for all type of firms. This means that the significant role of spatial interaction and spatial autocorrelation in the relocation decision behavior of logistic firms in the Tokyo Metropolitan area. The values of the correlation coefficients indicate the effectiveness of the firm relocation model when it incorporates the correlation among zones in the error term, and when it incorporates the correlation among firms in deterministic terms, given the distribution of consumption between firms and suppliers. Furthermore, the land prices in a given zone strongly affect the decision-making process of all the firms in the metropolitan area.
In addition, the statistically significant results obtained from this research suggest that, given sufficient data, the research methodology developed in this study can be successfully applied to cities other than Tokyo in order to gain insight into the determinants of the location patterns of logistic firms in particular, and businesses in general. Our results can also be applied to predictions of the effects of public policies on these location patterns. The results, therefore, should also be of interest to freight transportation and urban planners.
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