Agglomeration Economies and Linkage Externalities in Urban Manufacturing Industries - A Case of Japanese Cities

Agglomeration economies are usually divided into two categories: urbanization economies and localization economies. In 80fs a number of attempts have been devoted to estimate urbanization economies and/or localization economies. After the work by Glaeser et al. in 1992, however, historical effects on agglomeration called dynamic externalities in agglomeration are tried to estimate extensively. These externalities are named as MAR in a dynamic sense, and traditional agglomeration economies are evaluated in static sense. Besides urbanization and localization, more traditional sources of industrial concentration are found in industrial linkages, such as customer and supplier linkages or backward and forward linkages. These linkage effects come from the concentration of different kinds of industries while localization economies mean the benefit from the concentration of firms within the same industry. Also, linkage effects are often referred as pecuniary externalities. This paper tries to construct an estimable model of linkage effects among industries as well as agglomeration economies, and to estimate these effects separately within a framework of the Translog production function. In this model intermediate inputs play an important role as linkage effects. The empirical analysis is based on two-digit data for manufacturing industries in Japanese cities. Estimated results vary significantly among the two-digit industries. Furthermore, in order to capture dynamic effect in changes of agglomeration, a time variant production function model which is consistent to static production function model is constructed and estimated. From the time series evidence we find agglomeration economies are decreasing while linkage effects are still important.

Changes in Agglomeration Economies and Linkage Externalities for Japanese Urban Manufacturing Industries:1990 and 2000

Urban agglomeration economies are usually divided into two categories: urbanization economies and localization economies. In the 1970s and 1980s, a number of attempts were made to estimate urbanization economies and/or localization economies directly in the production function. Since the work by Glaeser et al. in 1992, however, historical effects on agglomeration called dynamic externalities in agglomeration are tried to estimate indirectly by use of the growth equation of urban labor force extensively. These externalities are called MAR in a dynamic sense, whereas traditional agglomeration economies are evaluated in static sense. Alongside urbanization and localization, more traditional sources of industrial concentration are found in various industrial linkages, such as customer and supplier linkages or backward and forward linkages. These linkage effects come from the concentration of different kinds of industries whereas localization economies mean the benefit from the concentration of firms within the same industry. Also, linkage effects are often referred as pecuniary externalities. This paper tries to clarify theose agglomeration concepts, and to construct an estimable model of linkage effects among industries as well as agglomeration economies, and to estimate these effects separately within the framework of the Translog production function. In this model intermediate inputs play an important role as linkage effects. Also, in order to investigate the change of agglomeration economies the estimations are implemented using data for 1990 and 2000. The empirical analysis is based on two-digit data for manufacturing industries in Japanese cities. The estimated results regarding agglomeration economies vary significantly among the two-digit industries, but most of the agglomeration effects have fallen since 1990.

An Econometric Study on the Urban Population Density Functions : A Survey

Since the seminal work by Clark (1951), a number of researches on urban population density have been appeared in several journals. In 1989 McDonald conducted a survey on the econometric study of urban population density functions. In the review he recommended the estimation technique pointed by Frankena (1978) and the use of Box−Cox transformation for choosing an optimal functional form. He also addressed to the model and estimation of beyond the distance−density functions such as varying parameter model. This paper begins in the second section with a choice of functional form. Sections 3 and 4 review the estimation model of suburbanization and the identification of the CBD including multi−center case. Recently more complicated population distributions are found in the real urban areas. Thus, more sophisticated models are needed to explain the real population distribution. In section 5 three estimation models ; cubic−spline, switching regression, and varying coefficient models are reviewed and in the varying coefficient model we propose theoretical foundations for the estimation of varying parameter model. In section 6 we describe data problems concerning population density estimation and estimation bias when we use census tract data. Finally, section 7 states a possible direction of the further research

Productive Efficiency Differences among Chinese Cities : A Stochastic Frontier Approach

This paper treats the empirical issue related to the urban productive efficiency. First, we construct a model incorporating urban inefficiency term and estimate a stochastic frontier production function at urban level. Next, we compare technical efficiency across Chinese cities during 1997-2007 by using estimated results. The urban technical inefficiency effect is found to be significant in many cities. Then, in the way of constructing the specification of the technical inefficiency in terms of various capital explanatory variables, we analyze the determinants of technical inefficiency of individual cities, and explore how the urban technical inefficiency is influenced by capital density, FDI, the domestic investment, while the impact of technical inefficiency on capital variables is investigated. From the empirical results, FDI is able to reduce effectively the technical inefficiency. Capital intensive industry would be the engine of economic growth in the future

Emission Credit Trading and Regional Inequalities

This paper examines how regional inequalities are affected by emission controls via credit trading and availability of absorption sources. We assume that homogeneous goods are costly traded without emission controls and that the rural areas have an advantage in terms of the availability of absorption sources. We especially focus on the long-term effects of firm relocation. Our two key findings are as follows. First, in the case where an emission control scheme is implemented without allowing for offsetting emissions with carbon absorption sources (carbon sinks), strengthening the emission controls drives firms to relocate from rural areas to urban areas, in the case that wage levels remain unchanged in both areas. As a result, regional inequalities in terms of both the number of firms and relative public welfare are enlarged by emission controls. Our second finding shows that in the case in which the emission control scheme allows for emissions-absorption offsetting, strengthening emission controls has mixed effects on the relative welfare of rural areas. Numerical simulations show that when the costs associated with transporting differentiated goods are relatively low, the introduction of emission controls with an offsetting system results in greater inequality across regions compared with introducing emission controls without such offsetting.

Market Potential and Regional Per Capita GRP Differentials : Application of the NEG Model to Indonesian Provinces

This study is conducted in order to examine the inter-provincial per capita GRP (Gross Regional Product) disparities in Indonesia under the NEG (New Economic Geography) framework. The coefficient of variation shows that the disparities are decreasing in the period of 1993-1997 and increasing in the period of 1998-2004. On the basis of the disparities trends, those two different periods are examined in our analysis. By utilizing wage equation which developed by Fujita, et al.（1999）we make use of panel data analysis in estimating the role of domestic market access, foreign market access, urbanization and human capital on interprovincial per capita GRP. The findings suggest that domestic market access, foreign market access and human capital play important roles on inter-provincial per capita GRP disparities, while urbanization shows reverse role because of excess of urban labor