2003年-世界发展银行全球_Metropolitan_Industrial_Clusters___Patterns_and_Processes_44页_1mb

Metropolitan Industrial Clusters: Patterns and Processes

Core Content

This working paper investigates the spatial patterns and processes of industrial clustering in three Indian metropolises: Mumbai, Kolkata, and Chennai. The authors analyze eight industrial sectors—food/beverages, textiles, leather, printing/publishing, chemicals, metals, machinery, and electrical/electronics—to understand how industries locate and cluster within metropolitan areas. The study emphasizes the role of land use policy in shaping the intra-metropolitan spatial distribution of industry.

Main Questions and Objectives

The paper addresses the following key questions:

• Where do industries locate within a metropolitan area?
• Do different industrial sectors exhibit distinct clustering patterns?
• Can these patterns be explained by industry characteristics?
• What is the geographical relationship between different types of industrial clusters?
• How do localization economies influence the clustering process?

The objective is to test for global and local clustering, and to distinguish between co-clustering and co-location of industries.

Key Findings

Global Clustering

• Kolkata shows the most consistent clustering across all sectors, except for textiles and metals/electrical/electronic workers.
• Chennai and Mumbai show inconsistent clustering, with more clustering among factories than workers.
• In the food/beverages sector, factories are clustered in all three cities, but workers are only clustered in Kolkata and Mumbai.
• In the textiles sector, factories are clustered only in Chennai, while workers are clustered in Kolkata and Mumbai.
• In the leather sector, both factories and workers are clustered in Kolkata, but not in Chennai or Mumbai.
• In the printing/publishing sector, both factories and workers are strongly clustered in Kolkata, and in Chennai, but not in Mumbai.
• In the chemicals sector, factories are clustered in all three cities, while workers are clustered only in Kolkata and Chennai.
• In the machinery sector, factories are clustered in all three cities, but workers are only clustered in Kolkata.
• In the electrical/electronics sector, factories are clustered in all three cities, but workers are only clustered in Mumbai.

Local Clustering

• The analysis reveals that factories and workers in the same industry do not necessarily cluster in the same pin codes.
• Some industries show a significant overlap between factory and worker clusters (e.g., printing/publishing in Mumbai, machinery in Chennai), while others show little to no overlap (e.g., food/beverages in Mumbai).
• Kolkata has the most clustered industries in terms of both factories and workers.
• The textiles and electrical/electronic sectors in Mumbai, leather in Kolkata, and leather in Chennai are the most clustered.
• The machinery sector in Mumbai is an exception, as it shows no local clustering among workers, despite having a high location quotient (LQ).

Co-clustering and Co-location

• Polluting industries (chemicals and leather) tend to locate in fringe areas of the metropolitan regions.
• Co-location is observed in some cities, particularly in Mumbai and Kolkata, where chemicals and leather industries are found in close proximity.
• Chennai is an exception, with chemicals factories clustered in the southern and western extremities, while workers are located in the northern fringes.

Influencing Factors

• Land use policy is identified as the key variable influencing the spatial distribution of industry within metropolitan areas.
• Infrastructure and regulatory frameworks are also significant factors in the location decision of firms.
• Localization economies (e.g., access to labor, buyer-supplier networks) have limited influence on the clustering process.
• Economies of urbanization (e.g., access to specialized services, labor pool diversity, infrastructure) are more influential in shaping industrial clusters.

Methodology

• The authors use spatial statistics, particularly Moran's I, to measure clustering at both global and local levels.
• Global Moran's I measures overall spatial association across all geographical units.
• Local Moran's I identifies heterogeneous spatial associations within the study area.
• The data are collected from the Annual Survey of Industries (ASI) by the Central Statistical Organization (CSO), and are geocoded using pin code information.
• The hit rates for geocoding are high: 99.9% in Mumbai, 97.1% in Kolkata, and 97.5% in Chennai.

Conclusion

The study suggests an evolutionary model of industry location in mixed rather than specialized industrial districts. It highlights the influence of land use policy and urbanization economies over localization economies in shaping industrial clusters. The findings contribute to the growing literature on industrial clustering in developing countries, offering insights into the spatial dynamics of industry in urban areas.

Key Sectors and Clustering Patterns

Study Areas Overview

• Mumbai is the second-largest urban agglomeration in the world, with a population of over 18 million.
• Kolkata is the second-largest metropolitan region in India, with a population of around 12.5 million.
• Chennai is the fourth-largest metropolitan area in India, with a population of approximately 9.5 million.
• All three cities are colonial cities that have evolved into major urban centers with significant industrial activity.

Data and Methodology

• The data used are geographically disaggregated, based on pin code and street address information.
• The analysis uses spatial statistics to measure the extent and pattern of clustering.
• The location quotient (LQ) is used to assess the relative concentration of industries within the metropolitan area.

This paper provides a detailed understanding of industrial clustering patterns in Indian metropolises, emphasizing the role of land use policy and urbanization economies in shaping these patterns.