Empirical Studies on the Sources of Agglomeration Economies (original) (raw)
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Agglomeration economies in urban manufacturing industries: A case of Japanese cities
Journal of Urban Economics, 1985
Agglomeration economies are usually divided into two categories: urbanization economies and localization economies. In 80's 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 make clear those agglomeration concepts and 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 with regard to agglomeration economies vary significantly among the two-digit industries.
2007
In this paper, using census data from the assembly industry during the period 1960-2000, we attempt to expand the knowledge about how innovation and imitation lead to the exploitation of long-term subcontract networks and agglomeration economies; thus having an effect on an improvement in productivity. To this end, a data envelopment analysis is employed to decompose productivity into innovation and imitation. The main findings make it evident that as time passes innovation most noticeably tends to occur towards the outskirts of the core area, and that the level of efficiency readily improves in areas where the division of labor is advanced by relatively small establishments.
Review of Urban & Regional Development Studies, 2014
This empirical study finds that positive but weak agglomeration economies resulted from the agglomeration of Japan's assembly-type manufacturing industry during 1985-2000. Estimation results particularly indicate positive externalities from coagglomeration and very slightly increasing returns to scale. Traditional studies conceive of agglomeration economies as being related to localization and urbanization. We, however, estimate a flexible translog production function using four-digit Standard Industrial Classification industry panel data and Ellison and Glaeser's agglomeration index with the same industry and coagglomeration index with different industry groups. We theoretically obtain appropriate and significant results without the homotheticity restriction.
How important is geographical agglomeration to factory efficiency in Japan’s manufacturing sector?
The Annals of Regional Science, 2014
In this paper, geographical spillover potential is modeled and empirically examined using factory-level data from Japan's Census of Manufactures. First, the efficiency of each factory is estimated using a non-parametric data envelopment analysis (DEA) model for each industry. Second, the geographical distances to the most efficient factory in the prefecture and Japan overall are estimated. Third, the determinants of the factories' performance are identified and estimated. We find that clustering occurs in each industry, and efficient factories concentrate in certain regions. The percentage of efficient firms out of the total number of firms is particularly high in the Chubu and Tohoku regions. The estimation results also suggest that proximity to the most efficient factories plays a statistically significant role in determining the efficiency of factories in Japan in most industries. However, this is not the case in high-tech industries.
Agglomeration Economies in Japan: Technical Efficiency, Growth and Unemployment
Review of Urban & Regional Development Studies, 2008
This paper examines if the effects of agglomeration economies get manifested in technical efficiency and generate faster economic growth and higher (lower) levels of employment (unemployment). Using the prefecture level data for each of the two-digit groups of industries in Japan, the paper estimates region-specific technical efficiency index based on the stochastic frontier production function framework. The results of the factor analysis show that in most of the industry-groups (with a few exceptions) efficiency has a positive association with external scale variable(s). Though the relationship is not seen to be very strong, it would be equally erroneous to ignore the effect of agglomeration economies on efficiency. In the case of some of the light goods industries the agglomeration effect is relatively stronger. Further, economic growth varies positively with external scale variable(s) and unemployment rate tends to fall with respect to growth and concentration. All this tends to suggest that measures against industrial concentration may be counter-productive, particularly in the context of globalisation when countries are in dire need of raising productivity. The Institute of Developing Economies (IDE) is a semigovernmental, nonpartisan, nonprofit research institute, founded in 1958. The Institute merged with the Japan External Trade Organization (JETRO) on July 1, 1998. The Institute conducts basic and comprehensive studies on economic and related affairs in all developing countries and regions,
Industrial Agglomeration, R&D and Total Factor Productivity: The Case of Taiwan
The paper analyses the impact of geographic innovation on Total Factor Productivity (TFP) in Taiwan. Using 242 four-digit standard industrial classification (SIC) industries in Taiwan in 2001, we compute TFP by estimating Translog production functions with K, L, E and M inputs, and measure the geographic innovative activity using both Krugman's Gini coefficients and the location Herfindahl index. We also consider the geographic innovation variable as an endogenous variable and use 2SLS to obtain a consistent, albeit inefficient, estimator. The empirical results show a significantly positive effect of geographic innovation, as well as R&D expenditure, on TFP. These results are robust for the Gini coefficients and location Herfindahl index, when industry characteristics and heteroskedasticity are controlled. Moreover, according to the endogeneity of geographic innovation, the Hausman test shows that the geographic innovation variable should be treated as endogenous, which supports the modern theory of industrial clustering about innovation spillovers within clusters.
An empirical analytical framework for agglomeration economies
2008
This paper proposes an empirical analytical framework for agglomeration economies based on a translog production-inverse input demand system. Estimation of the system allows us to identify effects on total factor productivity (TFP), partial factor productivity, factor prices and factor demands. It also provides a decomposition of the aggregate agglomeration elasticity into returns that arise from the increased efficiency of factor inputs and a "direct" agglomeration effect which exists over and above any factor augmentation. This enables us to indirectly address the problem of unobserved heterogeneity in factor "quality". The paper provides an empirical application of the model using firm level data for UK manufacturing and service industries.
Technovation, 2008
In a regional innovation system, a dense inter-organizational network within the region is recognized as a key factor in enhancing knowledge diffusion, regional learning, and effective resource transfers. Therefore, understanding the network structure and physical proximity of organizations is essential. In this paper, we investigated the industrial structure of Yamagata prefecture in Japan as a case study. Because Yamagata is a representative industrial region, the analysis can also provide an insight into other industrial regions. Initially, we investigated the geographical dispersion of firms and found them to be agglomerated along Route 13 and the Tohoku Shinkansen railroad, indicating that infrastructures for transportation still have a decisive role in terms of site location. Subsequently, we analyzed the modular structure of the inter-firm network. The results showed that hub firms construct a different type of network and play different roles within the inter-firm network, reflecting their strategic choice. The results also showed that there is a tendency for firms to transact with those in close proximity, and that firm location is also affected by the location of the hub firm in the module in addition to the infrastructures. r
Agglomeration economies, inter-regional commuting and innovation in the peripheries
Regional Studies, 2020
Regional development and innovation are often studied in the context of agglomeration economies, leading to a perception bias regarding the virtues of cities. Recent work on interregional connectivity has explored alternative mechanisms for economic growth, such as borrowed size and regional embeddedness, but there are limited studies examining these in the context of peripheries. The paper addresses this by examining the spatial relations of industry, commuting and agglomeration to innovation in Japan peripheries, finding dynamics between and within communities vary in how these factors increase innovation. Such understandings are critical in policy redressing core–periphery imbalances and industry competitiveness.
Agglomeration Economies and Productivity Growth in Manufacturing Industry: Empirical Evidence from
This study examines the effect of agglomeration economies on productivity growth in Indonesian manufacturing industries during the first decade of this century. Productivity growth is measured at the firm level using the F€are-Primont Productivity Index. Each firm’s productivity growth is then regressed against a set of firm and industry characteristics, including three measures of agglomeration representing the effects of specialisation, diversity and competition. The results show evidence of a positive specialisation effect and a negative diversity effect for aggregate manufacturing and subsectors. Furthermore, there are mixed effects across industries, suggesting that Porter’s competition externalities stimulate firm productivity growth under some conditions but not others.