Industrial clusters in India have been developed for decades and are currently taking the form of competitive and innovative clusters where related government organizations, research centers, and universities are located together. These clusters are designed to facilitate interactions between firms and institutions as well as promoting efficient distribution of information, skills, and labor across industries. As a result, a large share of multinational firms are located in existing Indian clusters or create their own clusters with local government support. Korean firms planning to start new businesses in India are also likely to consider these clusters for their potential business location. However, despite the importance of these clusters, there has been a lack of studies that quantitatively or qualitatively examine the characteristics of such clusters.
   This study compares the competitiveness of industrial clusters in Maharashtra, India, in four major dimensions: firm density, industry specialization, sustainability and innovation. While previous studies largely focused on efficient or precise definitions of clusters, we compare the characteristics of existing industrial clusters because these provide important reference points to be considered by Korean firms planning to set up a new business (or expand an existing one) in the Indian market, as well as the Korean government when promoting Korean investments in India.
   In Chapter 1, we briefly provide the background and motivation for the study and introduce methodologies used to measure the competitiveness of clusters. Also, we discuss how we address a few of the methodological challenges. The current study employs both qualitative and quantitative approaches to overcome the limitations of a quantitative approach alone when it comes to assessing overall competitiveness of clusters. Although we measure the competitiveness of clusters using information on firms’ finances and location, there are still other important factors to consider, like land price or quality of infrastructure. Therefore, we add qualitative analysis to complement our quantitative approach. In addition, we occasionally extend the boundary of industrial clusters to take account of the distributions of firms that are naturally created near the clusters.
   The second chapter introduces theoretical and political views related to clusters. Since the pioneering work of Michael E. Porter, there have been numerous studies seeking for an efficient and precise definition of clusters. We explore the different approaches to defining clusters and introduce other terminologies used interchangeably. We also examine how international organizations or local government entities in India define clusters differently. The industrial clusters in our study follow definitions used by the Maharashtra Industry Development Corporation (MIDC).
   The third chapter examines the development stages of Indian clusters as well as the cluster policies implemented in the state of Maharashtra. Since the first official development policy was initiated in 1997 by the United Nations Industrial Development Organization (UNIDO), the Ministry of Small & Medium Enterprises (MSE), state governments, and local government agencies have implemented numerous programs to enhance firms’ productivity in clusters. As a result, India appears to have thousands of industrial clusters in recent periods. The state of Maharashtra also has several cluster projects led by the state government and Maharashtra Industrial Development Corporation (MIDC). In addition, there are multiple special economic zones, IT parks, and cooperative industrial estate zones in Maharashtra to develop its economy.   
   In Chapter 4, we measure the competitiveness of the industrial clusters in Maharashtra. We use kernel density estimation and nearest neighborhood analysis to measure the density of firms. Industry specialization is measured by Location Quotients (LQ). Given that LQ coefficients do not consider the externalities between firms’ interactions, we identify the top 10% closely related industries using a similarity matrix, and then generate LQ coefficients within these industries. For sustainability, we use weighted kernel density estimation, which assigns weights based on the number of employees and the profit levels to take account of the firms of different sizes. Lastly, to measure innovation we count the number of universities, research centers, and other government organizations located in the cluster and estimate the relative importance.
   Our results indicate that the density of firms is the highest in Pimpri-Chinchiwad (east & west) in Pune region and relatively higher in several districts in Tane region. This is due to the high density of firms within individual clusters or across clusters. The level of sustainability is higher in Gansoli-Mahape and Airloi clusters in TTC region, and Pimpri-Chinchiwad, Chakan III, and Taloje clusters given that the firms with a large number of employment and profit level are present in the districts. Innovation level appears to be higher in most of the districts in Tane and Pune regions. Lastly, capital-intensive industries such as automobiles, electronics, or chemicals are more developed in TTC, Pimpri-Chinchiwad, and Chakan regions while other clusters tend to be more specialized in labor intensive industries such as food, textiles or plastics.
   Chapter 5 suggests the implications of our study for Korean firms and government and the directions for future research. India is a key partner in Korea’s New Southern Policy, and its sustainable economic growth and large domestic market have attracted the investment of many Korean firms. The variations of characteristics across industrial clusters in Maharashtra, which we quantitatively demonstrate, can be important information to be considered based on firms’ needs or purposes of investing in Indian market. Also, the Korean government could potentially build a better policy for Korean firms with the information provided by this study. Future research can benefit from extending the analysis to all Indian regions as well as creating a single indicator by incorporating other important information.