10 resultados para symptom clusters

em Academic Research Repository at Institute of Developing Economies


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This paper discusses the issue of upgrading industrial clusters from the perspective of external linkages. It is taken for granted that in most developing countries, due to the limited domestic market and poor traditional commercial networks, industrial clusters are able to upgrade only when they are involved in global value chains. However, the rise of China’s industrial clusters challenges this view. Historically, China has had a lot of industrial clusters with their own traditional commercial networks. This fact combined with its huge population resulted in the formation of a unique external linage to China’s industrial clusters after the socialist planning period ended. In concrete terms, since the 1980s, a traditional commercial institution . the transaction market . began to appear in most clusters. These markets within the clusters get connected to those in the cities due to interaction between traditional merchants and local governments. This has resulted in the formation of a powerful market network-based distribution system which has played a crucial role for China’s industrial clusters in responding to exploding domestic demand. This paper explains these features in detail, using Yiwu China Commodity City as a case study.

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The purpose of this report is to use information provided by a questionnaire survey to analyze the factors and processes underlying the formation of industrial clusters in Japan. The study, based on questionnaire surveys, forms part of an "Industrial Cluster Project". The Japanese government has implemented policies for industrial clusters so as to enable Japanese industries to maintain competitive power in global markets, and to aid the self-sufficient expansion of local industries. The government's project goes under the heading "Industry Agglomeration for the Recovery of Local Industries with respect to so-called "Industry Clusters." The authors aim to identify what expectations are held of government by the enterprises that make up industrial clusters. As part of our investigation, we used the results of a survey conducted by UNDP in 2004. Tsuji's study, published by the Osaka School of International Public Policy, surveyed 1198 small or medium sized manufacturing companies located in O ward, Tokyo and Higashi Osaka city, Osaka prefecture. The outcome of the present study, together with data from Tsuji's work on IT usage by SMEs in Japan, is meant to form the basis for policy design and implementation.

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This study examines the effects of intra-regional cooperation among firms and institutions on the growth of firms, using the unique data set of questionnaire survey collected in the three major industrial clusters in Japan. In contrast to the existing studies on regional innovations or agglomeration economies, this study explicitly focuses on the detailed contents of cooperative activities with two specific viewpoints: 1) the contents of regional cooperation in each of the three production stages of R&D, commercialization, and marketing, and 2) the detailed types of alliance partners. Our results demonstrate three points: 1) positive correlations are observed between the intensity of regional cooperation and the firm growth rate and R&D expenditure, 2) horizontal cooperation such as alliances with universities and cross-industry exchange organizations has positive significant effects on the growth rate of firms, which is in contrast with the previous studies that stressed only the role of vertically integrated inter-firm linkages in Japan, and 3) contents and partners of regional cooperation are different among the three clusters based on different dominant industries.

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One of the facilitating factors that enabled the rise of IT industry in India is the evolution of IT clusters. A study of these clusters can provide interesting insights. The rise of the Banglaore IT cluster was due, among other things, to some of the policies the Indian government took three decades or earlier. It would be difficult to talk of "benign neglect" of the government towards this sector. Different factors worked in the case of Hyderabad. A comparison between the IT clusters in India has much to tell the new emerging IT clusters in India as well as those outside of it.

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This paper proposes a flowchart approach to the automobile industry cluster policy and the hi-technology industry cluster policy to prioritize policy measures. First, in the automobile industry cluster, suppliers of parts and components to anchor firms such as Honda, Nissan and Toyota of Japanese assembly makers in Guangzhou, China, can innovate partly because the suppliers have become independent of their anchor firms in the Japanese Keiretsu system. Second, concerning the hi-technology industry clustering in Beijing, we show that the existence of universities is a precondition for the industrial cluster policy and that the leadership of the Zhongguancun Science Park Management Committee of Beijing Municipality is crucial to the success of the industrial cluster policy. The flowchart for the hi-technology industry is different from the one for the automobile industry cluster.

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This study analyzes the patterns of agglomeration of some modern manufacturing sectors in India, and in particular the Indian automobile sector. It also examines and contrasts the factors that have led to different patterns of cluster development in two leading auto clusters in India-Chennai and the National Capital Region (NCR). Moreover, the study analyzes whether firms in clusters perform better than those that are excluded and whether the relative importance of variables that determine the behavior of firms differs among clusters. Our analyses, which employ a combination of quantitative and qualitative methods, show that Indian industrial clusters are largely concentrated in the three clustered regions: NCR, Mumbai-Pune, and Chennai-Bangalore, across different manufacturing sectors. Our study of the auto clusters in Chennai and the NCR find considerable differences in the patterns of cluster formation, due partly to the historical and policy conditions under which firms, particularly, the lead firms must operate. Moreover, our econometric analyses confirmed that being part of a cluster positively influences the performance of the auto component firms and those belonging to a cluster perform better.

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Since the introduction of the Doi Moi ('renovation') economic reform in 1986, Vietnam has experienced a transformation of its economic management, from a central planning economy to a market-oriented economy. High economic growth, created by the liberalization of activities in all sectors of the economy, has changed the economic structure of the country, and the once agriculture-based and poverty-stricken land now generates a midlevel income and possesses many industrial bases. Economic growth has also changed the landscape of the country. Business complexes have been built in metropolises like Ho Chi Minh City and Hanoi, and rice fields have been converted into industrial zones. As the number of enterprises increased, areas began to emerge where many enterprises agglomerated. Some of these 'clusters' were groups of cottage industry households, while many others were large-scale industrial clusters. As Porter [1998] argues, industrial clusters are the source of a nation's 'competitive advantage'. McCarty et al. [2005] indicate that in some key industries in Vietnam, some clusters of enterprises have been created, although the degree of agglomeration differs from one industry to another. Using industry census data from 2001, they include dot density maps for the 12 leading manufacturing industries in Vietnam. They show that most of the industries analyzed are clustered either in Hanoi or Ho Chi Minh City (or both). Among these 12 industries, the garments industry has the greatest tendency to cluster, followed by textile, rice, seafood, and paper industries. The fact that industrial clusters have begun to form in some areas could be a positive sign for Vietnam's future economic development. What is lacking in McCarty et al. [2005], however, is the identification of the participants in the industrial clusters. Some argue for the importance of small and medium enterprises (SMEs) in Vietnam's economic development (e.g. Nguyen Tri Thanh [2007], Tran Tien Cuong et al. [2008]), while others stress the impact of foreign direct investments (FDI) (for example, Tuan Bui [2009]). Adding information about the participants in the above cluster study (and in other studies of spatial patterns of location of enterprises) may broaden the scope for analysis of economic development in Vietnam. This study aims to reveal the characteristics of industrial clusters in terms of their participants and locations. The findings of the study may provide basic information for evaluating the effects of agglomeration and the robustness of the effects in the industrial clusters in Vietnam. Section 1 describes the characteristics of economic entities in Vietnam such as ownership, size of enterprise, and location. Section 2 examines qualitative aspects of industrial clusters identified in McCarty et al. [2005] and uses information on the size and ownership of clusters. Three key industries (garments, consumer electronics, and motor vehicle) are selected for the study. Section 3 identifies another type of cluster commonly seen in Vietnam, composed of local industries and called 'craft villages'. Many such villages have been developed since the early 1990s. The study points out that some of these villages have become industrialized (or are becoming industrialized) by introducing modern modes of production and by employing thousands of laborers.

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The formation of industrial clusters is critical for sustained economic growth. We identify the manufacturing clusters in Vietnam, using the Mori and Smith (2013) method, which indicates the spatial pattern of industrial agglomerations using the global extent (GE) and local density (LD) indices. Spatial pattern identification is extremely helpful because industrial clusters are often spread over a wide geographical area and the GE and LD indices—along with cluster mapping—display how the respective clusters fit into specific spatial patterns.

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Examining the spatial structure of clusters is essential for deriving regional development policy implications. In this study, we identify the manufacturing clusters in Cambodia, the Lao People's Democratic Republic, and Thailand, using two indices—global extent (GE) and local density (LD)—as proposed by Mori and Smith (2013). We also analyze four different combinations of these indices to highlight the spatial structures of industrial agglomerations. Since industrial clusters often spread over administrative boundaries, the GE and LD indices—along with cluster mapping—display how the detected clusters fit into specific spatial structures.