The orientation and progress of spatial agglomeration for Krugman's core-periphery model are investigated in this paper. Possible agglomeration patterns for a system of cities spread uniformly on a circle are set forth theoretically. For example, a possible and most likely course predicted for eight cities is a gradual and successive oneconcentration into four cities and then into two cities en route to a single city. The existence of this course is ensured by numerical simulation for the model. Such gradual and successive agglomeration, which is called spatial-period doubling, presents a sharp contrast with the agglomeration of two cities, for which spontaneous concentration to a single city is observed in models of various kinds. It exercises caution about the adequacy of the two cities as a platform of the spatial agglomerations and demonstrates the need of the study on a system of cities.
Because tourists derive utility from the enjoyment of destination characteristics, Lancaster's approach is putatively appropriate to address the particular structure of the tourism industry. Most research efforts regarding tourism destination, including those applying Lancaster's model, specifically address the choice of a single destination. This article is intended to explain multiple destination choice using Lancaster's characteristics model and a discussion of model implications of some marketing strategies for destinations as well as for tour operators. The model developed herein explains that packages of multiple destinations can create preferable combinations of characteristics for certain travelers. Furthermore, the model provides useful strategies for tour operators in combining destinations into a travel menu or package.
Previous studies have looked at how the components of fiscal spending affect economic growth. However, we explicitly enquire into how to adjust the components in order to achieve the highest rate of economic growth starting from the present shares of components, by introducing a gradient method. The resulting optimal adjustment shares are proportional to the deviations from the average over elements of a gradient vector. The optimal adjustment share is completely estimated by using linear regression with any choice of omitted variable. The paper also provides an illustrative example taken from the annual panel data for the Japanese prefectural governments.j ere_493 320..340 JEL Classification Numbers: C61, C63, C50, E23, H50.
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