Clusters: The Drivers of Competitiveness by Institute for Competitiveness

Clusters: The Drivers of Competitiveness

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ABOUT THE INSTITUTE FOR COMPETITIVENESS Institute for Competitiveness, India is the Indian knot in the global network of the Institute for Strategy and Competitiveness at Harvard Business School. Institute for Competitiveness, India is an international initiative centered in India, dedicated to enlarging and purposeful disseminating of the body of research and knowledge on competition and strategy, as pioneered over the last 25 years by Professor Michael Porter of the Institute for Strategy and Competitiveness at Harvard Business School. Institute for Competitiveness, India conducts & supports indigenous research; offers academic & executive courses; provides advisory services to the Corporate & the Governments and organizes events. The institute studies competition and its implications for company strategy; the competitiveness of nations, regions & cities and thus generate guidelines for businesses and those in governance; and suggests & provides solutions for socio-economic problems. Visit www.competitiveness.in for more information.

The Institute for Competitiveness U 24 / 8 DLF Phase 3 Gurgaon 122 002 Haryana, India Phone: +91 124 437 6676 Email: info@competitiveness.in

Clusters: The Drivers of Competitiveness

Clusters: The Drivers of Competitiveness WITH INPUTS FROM Amit Kapoor Honorary Chairman Institute for Competitiveness, India Christian Ketels Principal Associate Institute for Strategy and Competitiveness Manisha Kapoor Senior Researcher Institute for Competitiveness, India Rich Bryden Director of Information Products Institute for Strategy and Competitiveness

The Institute for Competitiveness

Clusters: The Drivers of Competitiveness

PREFACE In 2017 Institute for Competitiveness, India joined hands with Institute for Strategy and Competitiveness to lay the foundation for their initiative India Cluster Mapping. The objective was to provide the leaders, businesses, and changemakers in the country with open records on industry clusters and regional business environment to advance competitiveness. It was conceived on the understanding that there has been no systematic statistical cluster analysis at pan India level despite the realization that focus on clusters holds the key to competitive advantage. This indicated a pressing need for defining and analyzing clusters in India that can help businesses and regional policymakers to make informed decisions. A multi-stage process was followed to reach the final framework for assessing clusters. • •

•

The first stage involved interaction with the Institute for Strategy and Competitiveness team to gain an understanding of the cluster mapping project, its evolution, principles, and methodology. The second stage involved mapping the US NAAC industry codes with the Indian NIC codes to arrive at the Indian cluster definitions. After clusters were defined, a comprehensive evaluation was done to understand the role of clusters in the Indian economy. The third step involved engagement with key experts and stakeholders to solicit feedback and validation. Among those who provided valuable feedback was the team of experts at Economic Advisory Council to the Prime Minister (EAC-PM) and Department of Industrial Policy and Promotion (DIPP). The team conducted presentations of their work at EAC-PM and Department of Industrial Policy and Promotion. The fourth step involved building a highly optimised interactive platform that provides data infrastructure covering cluster presence, performance, regional business environment etc. It can be accessed at clustermapping.in

The Institute is thankful to everyone involved in the project. We could never hope to name all those who have helped us, but we would like to highlight the following individuals for their contributions. We would like to thank Prof. Michael E Porter for the intellectual framework. Thanks to Rich Bryden and Christian Ketels whose expertise has guided us in our journey. We would also like to express our gratitude to Dr. Bibek Debroy (Chairman, EAC-PM), Shri Ratan P. Watal (Member Secretary, EAC-PM), Shri K Rajeswara Rao (Advisor, EACPM) and Dr. Vandana Kumar (Joint Secretary, Department of Industrial Policy & Promotion) for their guidance and suggestions about national priorities. Many thanks to the team at the Institute for Strategy and Competitiveness, Harvard Business School for their strategic inputs and insights that helped us.

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Clusters: The Drivers of Competitiveness

INSPIRATION FOR THIS WORK •

The Economic Performance of Regions (Michael E Porter, 2003)1 We have conducted a similar study for the Indian states in Chapter 2.4.

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European Cluster Panorama (Christian Ketels and Sergiy Protsiv, 2014)2 We have used the cluster methodology developed by Ketels and Prostiv to assess the clusters in Chapter 2.1.

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EU Cluster Mapping and Strengthening of Clusters in Europe (Orjan Solvell, Christian Ketels and Goran Lindqvist3., 2009)

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The Determinants of National Competitiveness (Mercedes Delgado, Christian Ketels, Michael E Porter, Scott Stern, 2012)4

Economic Performance of Regions: http://abclusters.org/wp-content/uploads/2014/03/Porter2003-The_Economic_Performance_of_Regions1.pdf 2 European Cluster Panorama http://ec.europa.eu/DocsRoom/documents/7242/attachments/1/translations 3 EU Cluster Mapping and Strengthening of Clusters in Europe http://publications.europa.eu/resource/cellar/6f14c45f-7d6a-49c7-9bbf-785b313657d4.0001.02/DOC_1 4 The Determinants of National Competitiveness http://www.clustermapping.us/sites/default/files/files/resource/The_Determinants_of_National_Competitiveness.pdf 1

Clusters: The Drivers of Competitiveness

Contents ABOUT THE INSTITUTE FOR COMPETITIVENESS ........................................................................ II PREFACE ................................................................................................................................... III INSPIRATION FOR THIS WORK................................................................................................. IV INTRODUCTION ......................................................................................................................... 1 CHAPTER 1.1: EVOLUTION OF CLUSTERS............................................................... ……………7 CHAPTER 2.1: DEFINING CLUSTERS AND CLUSTER STRENGTH FOR THE INDIAN ECONOMY.17 CHAPTER 2.2: DEFINING THE INNOVATIVE CAPACITY OF REGIONS ..................................... 30 CHAPTER 2.3: DEFINING REGIONAL COMPETITIVENESS ........................................................ 32 CHAPTER 2.4: DEFINING THE ECONOMIC PERFORMANCE OF REGIONS ............................... 35 CHAPTER 2.5 FINDINGS AND DISCUSSION OF RESULTS......................................................... 38 BIBLIOGRAPHY ........................................................................................................................ 71 APPENDIX 1: DATA SOURCES ................................................................................................. 73 APPENDIX 2: CLUSTER PORTFOLIOS ....................................................................................... 76

Clusters: The Drivers of Competitiveness

INTRODUCTION In 1492 Christopher Columbus set sail for India, going west. He had the Nina, the Pinta, and the Santa Maria. He never did find India, but he called the people he met ''Indians'' and came home and reported to his king and queen: ''The world is round.'' I set off for India 512 years later. I knew just which direction I was going. I went east. I had Lufthansa business class, and I came home and reported only to my wife and only in a whisper: ''The world is flat.'' -

Thomas L. Friedman, 2005

The above quote reflects one of the many voices cheerleading the belief that in a globalized world, location holds no relevance in competition. This view that the role of physical proximity in shaping the regional distribution of economic activity is diminishing emerged in the 1990s when some scholars observed that due to the twin processes of globalization and digitalization manufacturing was being relocated beyond borders and information was instantaneously beamed across the world. The advent of modern technology not only made the movement of people and goods easier and cheaper than ever before, but more importantly, phone, telex, fax, the Internet, and, more generally, telematics, removed the need for much of that movement. This happened because they allowed all kinds of informationbased transactions to be carried out instantly over the telecommunications networks. This means that people can now stay at home and continue working as usual. Professional and social relations can be established and maintained almost equally easily over any distance across the globe (Environment and Planning B: Planning and Design, 1996)5. This was profitable for businesses as human resources, machinery, and capital can be easily and efficiently sourced in global markets. It became possible for them to move part of their operations overseas and function more efficiently than when everything was in the same building. The first response was to shift activities in low-cost locations to pursue these benefits as it was no longer necessary to locate near large markets to serve them. Globalization, escalated by advances in technology, was seen as a liberating force, ushering in a ‘borderless world.’ O’Brien (1992) referred to this as ‘the end of geography.’ His argument was mainly based on two phenomena - tradability and codification. Tradability is the phenomenon that separates the provision of services from its point of consumption. Due to tradability, many service sector jobs which were once considered to be place-specific became less dependent on the places where service is consumed. These jobs that were sheltered from the international competition can now be conducted from any place on the globe. Examples include electronic surveillance etc. Codification is the reduction of knowledge to a universally accessible digital form of information. ICT impacts the way information and knowledge is produced, stored and transformed. It accelerates the codification of knowledge. Codified knowledge, being explicit and standardized, can be transferred over long distances and across organizational boundaries at low cost and ICT enables such knowledge to be made available more quickly and more cheaply than ever before (Morgan, 2004). Tacit knowledge, which is difficult to communicate as it is context-driven is also transformed into codified knowledge and transferred easily. Due to codification, transfer of knowledge across long distances at low cost became possible; it helped in maintaining professional relations over any distance across the globe.

https://www.researchgate.net/profile/Helen_Couclelis/publication/23540977_The_Death_of_Distance/links/58919efdaca2 72f9a55800cc/The-Death-of-Distance.pdf

Clusters: The Drivers of Competitiveness

In a similar vein, Cairncross (1997) announced the ‘death of distance,’ and Friedman (2005) said that ‘the world is flattening.’ These views have a lot in common with the economistic theories which appear from time to time announcing the death of the nation-state, either because of the growth of multinationals or because of the growth of global markets more generally (Ohmae, 1990). But despite the twin processes of globalization and digitalization, the world, whether developed or developing, is home to several clusters, loosely defined as the geographic concentrations of interconnected companies (see Figure 1). And the presence of these clusters makes the “death of geography” argument look farfetched. The importance of location can be seen in the famed growth stories of IT hubs like Silicon Valley and Bangalore. Both of these places managed to utilize IT professionals highly productively, which was enhanced by their proximity to each other in a location where they could share knowledge, build relationships and instill a spirit of competition – traits which distant rivals could not match.

Figure 1: Some famous clusters This lands us in a paradoxical situation, and that makes it more important to understand that if the widespread view is that location matters less then why is the probability of finding a world-class mutual fund company in Boston higher than any other place? Why could the same be said about movie marketing and production companies in Mumbai, automotive companies in South Germany, pharmaceutical companies in Andhra Pradesh? It is critical to analyze what is happening to the concept of distance on which the idea of modern geography was built. Because all the classical models of geography are variations on the theme of distance: aggregate distance as disadvantage (potential model); distance as generator of spatial equilibrium between demand and supply (central place theory); distance as major economic cost (Weber model); distance as profitability adjuster (von Thiinen model); distance as socioeconomic sorting mechanism (Alonso model); distance as developmental handicap (core-periphery models); and so on.

Clusters: The Drivers of Competitiveness

The answer lies in the fact that traditional reasons for clustering have diminished in importance with globalization as they were based on cost minimization view of competition. In this view, competition is driven by cost which is based on factors such as land, labor, capital, etc. Due to globalization, factors per se have become less important as the opening of more countries to the global economy expands their supply, their market becomes more efficient, and the factor intensity of competition diminishes. This means that any company can locate their businesses in low-cost places now and the spatial advantage derived by the firms that were already there no longer exists. So, they no longer play a significant role in determining the standard of living. However, this does not imply that the role of location is diminishing. It has just shifted from factor endowments and size to productivity and productivity growth. The role of geography is beginning to be more widely appreciated in evolutionary theories of productivity, innovation and technological change. According to the new theories, the productivity and prosperity of a region is dependent on how the industries compete and not on what industries its firms compete in. Firms can be productive in any industry if they employ sophisticated methods, use advanced technology, and offer unique products and services (Porter, 1998). In this sense, every industry can become high-tech and knowledge-intensive with the right set of innovative practices. And the geographical concentration of firms positively impacts their innovative capacity. This happens because cluster participation eases the process of learning and innovation as firms try to create a shared understanding of the industry and its workings. Every firm within the district benefits from the idea/innovation and thus the productive benefits are realized at a larger scale. The participant firms not only have deeper insights about the evolving technology which helps in innovation, but they have better knowledge about the availability of new inputs and buyer needs. Clusters not only impact competition and competitiveness through innovation but also by directly impacting the productivity of firms. They affect productivity by providing highly specialized inputs at low costs, reducing transaction costs, and providing easy access to information. Location matters, then, albeit in different ways at the turn of the twenty-first century than in earlier decades (Porter, 1998). And clusters present a new way of thinking about economies, both national and local, and they define new roles for the government, businesses and other changemakers in enhancing competitiveness. Therefore, it is important for every economy to identify and analyze the clusters that are present; evaluate the linkages that exist between clusters, innovation, and economic development; empirically examine the role played by clusters in enhancing competitiveness. This report titled â&#x20AC;&#x153;Clusters: Drivers of Regional Competitivenessâ&#x20AC;? is a step in the same direction. It is aimed at providing policy recommendations to leaders and changemakers in India for enhancing the competitiveness of Indian regions based on the cluster theory. It is divided into two major sections. The first section traces the evolution of clusters and portrays how their role has changed over the years. This will help in understanding the ways by which clusters affect competition and regional competitiveness. The second section, building on the theories from section one, empirically tests the relationship between clusters, innovative capacity, competitiveness and economic performance. To tests these relationships, it is essential to develop a measure for all four concepts â&#x20AC;&#x201C; clusters, innovative capacity, economic performance, and competitiveness. For the same, section two is divided into five chapters. Four chapters are dedicated to creating empirical measures at the state level in India. The fifth chapter presents the linkages and provides recommendations based on them.

Clusters: The Drivers of Competitiveness

SECTION 1: EVOLUTION OF CLUSTERS This section traces the evolution of clusters and portrays how their role has changed over the years. This will help in understanding the ways by which clusters affect competition and competitiveness.

Clusters: The Drivers of Competitiveness

CHAPTER 1.1: EVOLUTION OF CLUSTERS All Schools arose against the background of distinct historical events that have significantly altered the competitive landscape and the way in which firms interact with their local environment (Saric, 2011). THE CONCEPT OF INDUSTRIAL DISTRICTS BY ALFRED MARSHALL The study of geographical proximity traces its roots back to Alfred Marshall. During the end of the 19th century, Marshall witnessed a paradigm shift in the production process that Piore and Sabel would later call the industrial divide (Saric, 2011). A manifestation of this divide was the emergence of large vertically integrated firms that threatened to replace the small and medium businesses due to the internal economies of scale. Surprisingly, Marshall observed that the small businesses were flourishing. This caught his interest, and he analyzed how the small firms in specific localities were able to compete with large corporations successfully. He began by identifying the factors that affect the concentration of industries in certain localities, which he termed as a localized industry. According to Marshall, three factors play a vital role in deciding the location of industries. First, physical conditions; such as the character of the climate and the soil, the existence of mines and quarries in the neighborhood, or within easy access by land or water. For instance, the high growth of the cotton textile industry in Western states of India is due to the humid climate that is suitable for the spinning of yarn. Similarly, the availability of limestone mines in Andhra Pradesh and Rajasthan explain the concentration of cement manufacturing industries in those regions. Second, development of manufacturing towns. The presence of employment opportunities in concentrated localities leads to their continuous growth which in turn causes the ground-rents to shoot up. The result is that factories now congregate in the outskirts of large towns and manufacturing districts in their neighborhood rather than in the towns themselves (Marshall, 1920). Third, patronage of court. The skilled artisans assembled at places where the wealthy and affluent demanded highquality goods. This explains the settlement of Flemish and other artisans in England which were made under the immediate direction of Plantagenet and Tudor kings. This ‘primitive’ localization, if it lasts long enough, becomes a ‘more compound’ localization, that is, it is transformed into an industrial district (Caldari & Belussi, March 2009). It happens because the passage of time allows these localized economies to attain certain advantages of production at a large scale. Skilling: Industrial districts offer a constant market for skill that leads to benefits for both employers and employees by minimizing the economic risk for both the parties as compared to isolated locations. Employers would set up new industries where they are likely to find employees with the skill set, they require and employees will move to places where there are many employers who need their skills. The movement of ideas and knowledge in industrial districts is easy. Every firm within the district benefits from the idea/innovation and thus the productive benefits are realized at a larger scale. Specialized Machinery: These areas are also beneficial as they enable small manufacturers to make use of expensive specialized machinery even though the individual capital employed is very small. The growth of industrial districts also favors the growth of supplementary industries. Specialised manufacturers of intermediate goods are also able to operate at higher capacity while supplying industrial districts with their specialised inputs (Hoover, 1971). This provides them the scale to upgrade their business.

Clusters: The Drivers of Competitiveness

Industrial districts not only help the firms but also inflict some external diseconomies. If the industrial district specializes in only one industry it will not only be liable to extreme depression in case of circumstances such as sudden fall in demand but the average earnings of the family in that district will also be low. In Marshall’s words, “if the work is done in it is chiefly of one kind, such for instance as can be done only by strong men. In those iron districts in which there are no textile or other factories to give employment to women and children, wages are high and the cost of labor dear to the employer, while the average money earnings of each family are low.” (Marshall, 1920) In a nutshell, Marshall concluded that industrial districts offer an alternative form of industrial organization that can compete with vertically integrated firms. NEW MARSHALLIAN DISTRICTS In 1970s small manufacturers1 in central and north-eastern part of Italy caught the attention of scholars when their contribution to Italy’s total manufacturing output increased to almost 27 percent (Amin, 1989). It was observed that these firms maintained a balance between co-operation and competition by following a shared set of values, norms and knowledge linkages. Becattini, Brusco, amongst others re-visited the concept of Industrial districts by Marshall to explain the economic success of these manufacturers. This new industrial district that had a socio-economic dimension was defined by Becattini as “a socio-economic entity which is characterized by the active presence of both a community of people and a population of firms in one naturally and historically bounded area.” The major characteristics of the New Marshallian Districts were somewhat similar to the original concept. The area supported a highly skilled labor force as a result of formal training, and a supply-demand information network was present due to the geographical proximity. This concept of New Marshallian Districts provided one major contribution to the literature by shifting the focus from individual firm to the community of firms as all the firms were dependent on each other. The success of the district was based on their interdependence. Although, a major drawback of this empirical investigation was its heavy reliance on the Italian example that made it difficult to generalize. This inability led to the emergence of the Californian School of Thought, that attempted to provide a general theory of clusters. THE CALIFORNIAN SCHOOL The Californian school studied the successful technology districts in southern and central California based on transaction cost-based analysis. They argue that due to the vertical disintegration of production the external transactions that a firm enter into increase rapidly. These transactions are less predictable and thus more complex which further adds to the transaction cost. In order to control them, firms tend to locate near each other. This agglomeration then not only helps them by a reduction in transaction cost but also by increasing flexibility and minimizing risk. As their study is based on the general theory of transaction cost, its applicability is much more than the New Marshallian Districts. This theory can be applied to any region, any sector and the firms of all sizes. THE NORDIC SCHOOL The idea of the Nordic school rests on innovation. They theorize innovation as a complex local learning process that is based on co-operation and mutual trust. They highlight that the efficient use of formal, codified knowledge may demand some tacit knowledge. This kind of knowledge cannot easily be isolated from its individual, social 8

Clusters: The Drivers of Competitiveness

and territorial context; it is a socially embedded knowledge, which is difficult to codify and transfer through formal channels of information (European Commission, 2002). MICHAEL E PORTER

… And never since Michael E. Porter elevated “the cluster” to stardom, has there been more attention brought to it by politicians, non-governmental organization, CEOs, consultants, and the scientific community alike. -

Prof. Dr.Dr.h.c. Hans-Christian Pfohl, 2012

Prof. Michael E Porter undertook a study of the world’s most successful businesses in which he explains how firms and nations can achieve and sustain competitive advantage. It is during this study he observed that firms from one or two nations achieve disproportionate success in particular industries and he developed his theory of clusters. Our study rests on the theory of clusters provided by Michael E Porter. Clusters are defined as geographically proximate group of interconnected companies and associated institutions in a particular field, linked by commonalities and complementarities. The geographic scope of clusters ranges from a region, a state, or even a single city to span nearby or neighbouring countries. The conceptual idea of clusters rests on presence of linked industries, geographical proximity and interplay of competition and cooperation. According to Porter, clusters affect competition in three broad ways that both reflect and amplify the parts of the diamond model: (a) increasing the current (static) productivity of constituent firms or industries, (b) increasing the capacity of cluster participants for innovation and productivity growth, and (c) stimulating new business formation that supports innovation and expands the cluster. 1. Clusters and Productivity The productivity within clusters enhanced as: − clusters provide highly specialized inputs at a low cost − clusters lead to a reduction in the transaction cost − clusters facilitate complementarities between activities of cluster members − clusters provide easy access to information, thereby reducing if not eliminating the information asymmetries Specialized inputs: The regions where clusters are present are more efficient due to the presence of local suppliers as they enable procurement of superior technology at a comparatively lower price. Even if the suppliers are not in attendance within the region, firms have strong alliances with outside entities. These vertical linkages help in importing specialized inputs such as machinery from distant locations. As the cluster grows, due to the benefits it offers, suppliers are attracted, and this leads to the development of the local supplier networks. Access to information: The ease of access to every information - market, technical and specialized and the fact that it can be accessed at a lower cost allows firms to move close to the productivity frontier. Every firm within the cluster benefits from the idea/innovation and thus the productive benefits are realized at a larger scale. Not only the firms have information about new and innovative practices, but they also have details 9

Clusters: The Drivers of Competitiveness

about the buyer needs. In more sophisticated clusters, buyers are a part of the clusters, and therefore the information about their specific needs is shared.

Access to institutions and public goods: The presence of clusters makes otherwise costly goods into public or quasi-public goods. Some of the public or quasi-public goods available in clusters are similar to conventional public goods in the sense that they are closely linked to government and public institutions (e.g., public investment in specialized infrastructure, educational programs, information and trade fairs). However, other quasi-public goods available to cluster participants are created as a natural byproduct of competition (Porter, 2000). These include information and pools of technology, the reputation of the cluster location, and some of the marketing and sourcing advantages described earlier. Apart from public investments, private investments in cluster-specific goods are common because the benefits of such investments will be perceived by all the firms within the clusters. They are enabled through industry associations or other institutes for collaborations. 2. Clusters and Innovation The presence of firms in clusters offers them advantages in perceiving new technological, operating, or delivery possibilities. As compared to an isolated firm, firms in the cluster have better access to insights into new technology, component and machinery availability, service and marketing concepts. Firms within the clusters can source new machinery and components more rapidly. Mostly, clusters also include upstream manufacturers and suppliers, so they can also be closely involved in the innovation process and makes it easy. The new specialized staff that is required for the new approaches can often be recruited locally, and complementarities involved in innovation are more easily achieved. In short, clusters contribute to innovation in the following ways: − by easier and faster access to new processes needed for innovation − by proceeding faster with innovations due to the proximity of potential suppliers − by making the availability of specialized professionals easy − by identifying new technological, operating and delivery opportunities − by direct observation of other firms The intense competition between firms’ rests on innovation and the search for strategic differences. But similar circumstances combined with the intense rivalry increases the pressure on firms. This leads to difficulty in continuing the business and most firms are unable to sustain. Under certain circumstances, cluster participation retards innovation. Firms are stuck with old behavior and practices and suppress new ideas. They are not open to innovative improvements. Such firms become unproductive in the medium run, and the mere presence of industries does not guarantee regional prosperity if the firms are unproductive. 3. Clusters and New Business Formation A lot of new business emerge in the regions with existing clusters. This happens due to varied reasons. First, the information about new opportunities within clusters is easily available. The mere existence of a cluster itself is a signal to the firms as the regions with the presence of clusters are highly innovative. Second, the barriers to entry are low compared to an isolated location. This happens as the basic 10

Clusters: The Drivers of Competitiveness

facilities such as a skilled labor pool, public infrastructure, raw material suppliers, etc. are already in place. A significant local market is also present. The presence of an established cluster not only lowers the barriers to entry to a location facing outside firms but also reduces the perceived risk. Therefore, already established companies that are based on other locations are also driven to these locations. In short, clusters lead to the new business formation as: − − −

they offer lower barriers to entry (and exit) as the cost of specialized inputs is lower compared to non-cluster areas they provide information about new business opportunities they provide an environment rich in social capital

Table 1: The ideas of different approaches to regional clustering School of Thought

Idea

Industrial Districts

Small firms gaining the advantage of external economies of scale

Californian School

Proximity leading to a reduction in transaction costs

Nordic School

Innovation is a process of learning and learning is a localized process

Michael E Porter

The role of clusters in impacting to competitiveness

HOW ARE CLUSTERS PLACED IN THE BROADER COMPETITIVENESS FRAMEWORK? Until now we have seen how clusters impact competition between firms by impacting their productivity and innovative capacity. We now move on to analyze how they impact the competitiveness of regions. To understand these linkages the first step is to clearly define competitiveness as there is a dichotomy about how the concept is perceived. The concept of competitiveness has gained significant momentum in the last two decades, theoretically as well as empirically. Policymakers across the world consider “enhancing competitiveness” as the main goal of economic policy. Despite this, there is a dichotomy about how the concept is perceived. On the one hand, it is associated with the qualities that enable higher living standard for the citizens and on the other hand, it is linked to the locational attributes that drive growth such as low wages, competitive exchange rate, etc. The naïve interpretation of competitiveness as low wages and market share works as follows. Low costs can help companies in gaining substantial market share globally. This implies lower unemployment, higher exports, and higher FDI. According to this theory, competitiveness is seen as a zero-sum game – one region’s gain would be another region’s loss. It was in response to these misconceptions that Porter (1990) tried shifting the competitiveness debate back to productivity. According to Porter, the central ingredient of competitiveness is productivity which determines a region’s prosperity. Productivity is measured by the value of goods and services produced per unit of the region’s human, capital, and natural resources. The standard of living of citizens – wages, returns on capital,

Clusters: The Drivers of Competitiveness

returns on natural resources depends on its level of productivity6. Building on Porter’s work, Delgado, Ketels, Porter & Stern (2012) developed a definition of competitiveness that links to economic growth and development and involves factors that shape national prosperity. Figure 2 depicts the framework that captures the range of factors that drive productivity and thus prosperity across locations.

Figure 2: Dimensions of Competitiveness Every region has endowments that form the foundation for prosperity, but a region’s ability to grow and achieve a higher standard of living depends on the productivity with which it utilizes these endowments. There are no industries that are inherently productive, and therefore productivity will not depend on what industries a region competes in but on how it competes. Macroeconomic competitiveness is affected by factors such as monetary and fiscal policies, the efficacy of public institutions and the level of social development. These factors impact the productivity potential of a region, but alone they are not sufficient to enhance the competitiveness. This is because the macroeconomic tools such as interest rates, fiscal stimulus through government spending will impact the economic growth in the short run. They don’t address the fundamental causes of productivity. Similarly, the absence of social infrastructure such as healthcare, education, safety, and security, etc will undermine the productivity of the workforce, but their presence does not automatically translate into higher productivity. Microeconomic competitiveness resides in the ability of firms to consistently and profitably produce products that meet the requirements of an open market in terms of price, quality, etc. The microeconomic factors directly influence the productivity level of firms. They include the quality of business environments such as

Productivity determines prosperity at all levels of geography – nation, states, districts and cities. This report focusses on regional (state level) competitiveness. 6

Clusters: The Drivers of Competitiveness

infrastructural facilities, human capacity, level of innovation; the presence and strength of clusters; and company strategy and sophistication. The quality of business environment such as physical infrastructure, access to capital, the ease of administrative practices has a significant impact on the productivity of firms as well as new business formation. Clusters, the geographic concentration of firms, are within the competitiveness framework an important part of a broader set of drivers that need to be seen in their interplay; they are not an isolated or only source of economic performance (Ketels, 2017). They are affected by the overall business environment and thrive in regions that have a favorable business landscape. And they drive the quality of the business environment through spillover effects and linkages.

Figure 3: Clusters and Competitiveness The challenge in enhancing regional competitiveness and prosperity, therefore, boils down to creating conditions that will lead to sustained productivity growth. In sustaining the level of productivity growth, it becomes important for regions (and businesses) to innovate continuously. This holds true especially for advanced economies as they wonâ&#x20AC;&#x2122;t be able to hold on to their market shares globally if they produce using the standard measures because they will be imitated by regions with lower costs. Therefore, competitiveness relies heavily on the capacity for innovation and as clusters impact innovation they become an important driver of regional competitiveness.

Clusters: The Drivers of Competitiveness

SECTION 2: THE IMPACT OF CLUSTERS This section building on the theories from section one, empirically tests the relationship between clusters, innovative capacity, economic performance, and competitiveness. To tests these relationships, it is important to develop a measure for all four concepts â&#x20AC;&#x201C; clusters, innovative capacity, economic performance, and competitiveness. For the same, section two is divided into five chapters. Four chapters are dedicated to creating empirical measures at the state level in India. The fifth chapter presents a discussion of results and provides recommendations based on them.

Clusters: The Drivers of Competitiveness

CHAPTER 2.1: DEFINING CLUSTERS AND CLUSTER STRENGTH FOR THE INDIAN ECONOMY A major impediment to the analysis of clusters till last quinquennia was the lack of a systematic methodology to define the clusters. Almost all the cluster literature was focused on detailed case studies based either on specific sectors or specific regions (see among others, Schmitz, 2010). Some of the studies generated cluster definitions and using those particular cluster definitions, they proved that the presence of related economic activity matters for regional and industry performance, including job creation, patenting, and new business formation (see among others, Feldman and Audretsch, 1999; Porter, 2003; Feser, Renski, and Goldstein, 2008; Glaeser and Kerr, 2009; Delgado, Porter, and Stern, 2010, 2014; Neffke, Henning, and Boschma, 2011). But a lack of comprehensive and comparable methodology made it difficult to compare the findings. Delgado, Porter, & Stern (2014) overcame this constraint by developing a clustering algorithm that generates a group of closely related industries using cluster analysis. Their algorithm is designed to define mutually exclusive clusters, where each industry is uniquely assigned to one cluster. They define the degree of industry relatedness by capturing multiple types of inter-industry linkages. They look at three similarity matrices to capture regional linkages, i.e. Location Correlation of Employment, Location Correlation of Establishments and Conglomeration Index. •

The employment co-location patterns of pairs of industries capture inter-industry linkages of various types such as technology, skills, supply, or demand links. Porter (2003) defines the locational correlation of employment (LC-Employment) of a pair of industries as the correlation coefficient between employment in industry i and employment in industry j in a region r: LC-Employmentij = Correlation (Employmentir, Employmentjr).

•

The presence of numerous establishments can facilitate inter-firm interactions that result in spillovers (Glaeser and Kerr, 2009). Thus, the co-location patterns of the count of establishments help to capture inter-industry linkages that are facilitated by the number of businesses. Delgado, Porter, & Stern (2014) define locational correlation of establishments as: LC-Establishmentsij = Correlation (Establishmentsir, Establishmentsjr).

•

The Coagglomeration Index captures whether two industries are more co-located than expected if their employment is distributed randomly. Delgado, Porter, & Stern (2014) define Coagglomeration Index as: COIij = ∑r (sri – xr )*(srj – xr )/(1 - ∑r xr2), where sri is the share of industry i’s employment in region r; and xr measures the aggregate size of region r.

They also capture national level industry linkages using input-output and labor occupational links. •

The IOij link takes a minimum value of zero if the two industries do not buy from or sell to each other, and a maximum value of 1 if any of the two industries buy or sell exclusively from or to the other. IOij = Max {inputi to j ,inputj to i ,outputi to j ,outputj to i }

•

Labor Occupation Links: They are used to measure the extent to which industries share similar skills. Occij = Correlation (Occupationi, Occupationj) 17

Clusters: The Drivers of Competitiveness

We use the cluster definitions generated by Delgado, Porter, & Stern (2014) to create the Indian clusters. The Indian cluster definitions will facilitate comparisons across regions and clusters over various aspects such as employment, wages, job creation, etc. By identifying the regional industries and the cross-sectoral links, this will also help to lay the groundwork for new industries. DATA AND METHODOLOGY FOR CLUSTER MAPPING Dataset The dataset used is Annual Survey of Industries (ASI), which is the principal source of organized industrial statistics in India. It provides information about the growth, employment, wages, composition and structure of organized manufacturing sector comprising activities related to manufacturing processes, repair services, gas and water supply and cold storage. Coverage The ASI extends to the entire country except for the States of Arunachal Pradesh and Mizoram and Union territory of Lakshadweep. It covers all factories registered under the sections 2(m) (i) and 2(m) (ii) of the Factories Act 1948, i.e. those factories employing 10 or more workers using power; and those employing 20 or more workers without using power. The survey also covers bidi and cigar manufacturing establishments registered under the Bidi and Cigar Workers (Conditions of Employment) Act 1966 and electricity undertakings. (Annual Survey of Industries, 2018) The primary unit of enumeration in the survey is a factory in the case of manufacturing industries, a workshop in the case of repair services, an undertaking or a licensee in the case of electricity, gas and water supply undertakings and an establishment in the case of bidi and cigar industries. Level The ASI data is collected at the factory level, and each unit is provided with a unique DSL code. Against each factory five and four-digit industry, codes are provided, and that makes it possible to club the factory level data at any five levels of industry classification. The grouping of activities in industry classification is as follows: activities are first grouped into ‘section’ alphabetically coded from A through U, every section is divided into ‘division’ with 2-digit numeric code, every division into ‘group’ with 3-digit numeric code, every group into ‘class’ with 4-digit numeric code and every 4-digit class into 5-digit ‘sub-class’. Period of study The period of this study is 1999-2014. Since the classification of industries changed in 2004 and then again in 2008 a concordance sheet between NIC 2008 & NIC 2004 and NIC 2008 & NIC 1998 is prepared at the 5-digit level. However, complete concordance at the five-digit level is not possible due to the structural differences in the grouping of activities in the two systems. For instance, some of the 5-digit sub-classes of NIC-2004 have been made separate 4-digit classes in NIC-2008. Therefore, some of the results presented in chapter 5 are from 2009-2014. Geographic unit The primary geographic unit used in the analysis is States and Union Territories. The Indian region comprises of 29 states and seven union territories. Our study covers 26 states and six union territories. The geographical areas that we leave out include Arunachal Pradesh, Mizoram, and Lakshadweep as they are beyond the 18

Clusters: The Drivers of Competitiveness

scope of ASI surveys and Telangana as the state came into existence only in 2013 while the study relates to the period between 1999-2014. Sampling Procedure ASI follows a circular systematic sampling procedure. According to the latest sampling design, all the industrial units in the universe are categorized into two sectors, i.e. census and sample. Census Sector: Census Sector consists of the following units: (i) (ii)

(iii)

All industrial units belonging to the six less industrially developed states/ UT's viz.Manipur, Meghalaya, Nagaland, Sikkim, Tripura and Andaman & Nicobar Islands. For the rest of the twenty-six states/ UT's., a. units having 100 or more employees, and b. all factories covered under Joint Returns. After excluding the Census scheme units, as defined above, all units belonging to the strata (State x District x Sector x 4-digit NIC 2008) having less than or equal to 4 units are also considered under Census Scheme. It may be noted that in the formation of the stratum, the sectors are considered as Bidi, Manufacturing, and Electricity.

Sample Sector All the remaining units in the frame are considered under Sample Scheme. For all the states, strata are formed for each State x District x Sector x 4-digit NIC2008 factories. The units are arranged in descending order of their number of employees. Samples are drawn as per Circular Systematic Sampling technique for this scheme. An even number of units with a minimum of 4 units are selected and distributed in four sub-samples. It may be noted that all the four sub-samples from a particular stratum may not have an equal number of units. Out of these four sub-samples, two pre-assigned sub-samples are given to NSSO (FOD), and the other twosubsamples are given to State/UT for data collection. Multiplier The weight / multiplying factor for the census sector is taken as 1, and for the sample sector it is estimated using the following technique: Mj = N’j/n’j In case, N’j and n’j are not known, Mj can be estimated by using the formula Mj = Nj/nj with the assumption that Nj / N’j≅nj / n’j Where, Nj = Number of units considered for selection from the jth stratum of sample sector S. N’j = Number of units reported to be existent in the frame for the jth stratum of S. nj= Number of sample units selected from jth stratum of S. n’j = Number of sample units reported in the jth stratum of S. 19

Clusters: The Drivers of Competitiveness

Mj = Multiplier for the jth stratum of S. Tc = Aggregate of a characteristic of the units reported under Census Sector C. Tj = Aggregate of a characteristic of the reported units of jth stratum in S. T = Aggregate of a characteristic for the factory sector as a whole in a state / U.T. For any characteristic, the estimate of T is given by T = Tc + ∑ MjTj Methodology 1. Preparing India Cluster Definitions The first step is to create the format in which the data – employment, wages, GVA is required, i.e., preparing India Cluster Definitions sheet based on US Cluster Definitions. The US Cluster Definitions prepared by Delgado, Porter, & Stern (2014) are based on the NAICS codes. So, a mapping is required between the US NAICS codes and Indian NIC codes. The Indian NIC Codes changed in 1998, 2004 and 2008. So, three mapping sheets are prepared. The following steps are required: ➢ Prepare a mapping sheet between 5-digit NIC 2008 and NAICS codes. ➢ Prepare India Cluster Definitions sheet based on the US Cluster Definitions by using the above mapping. ➢ The period of the study is 1999-2014, and for getting the data before the 2009 mapping of NIC 2008, NIC 2004 and NIC 1998 codes is required. ➢ Create a mapping between NIC 2008 and NIC 2004 codes. Update the India Cluster Definitions using 2004 codes. ➢ Create a mapping between NIC 2008 and NIC 1998 codes. Update the India Cluster Definitions using 1998 codes. ➢ Although the definitions are based on the industry patters in the US, there are a number of reasons why these definitions are useful in the Indian context as well. First, the US provides more granular data across all of its regions than what is available for India. An application of the same methodology in India would thus lack the same level of precision that can be achieved in the US. For instance, data for education and knowledge creation industries is missing in India thus making it impossible to define that cluster7. Also, for some clusters that are local by nature have data available for only some industries within them. Thus, the resulting local cluster definitions would suffer due to this lower quality of data. Second, patterns of economic geography in the U.S. are more visible than in India. Therefore, U.S definitions will provide a view of how cluster categories should look like.

The dataset used is ASI.

Clusters: The Drivers of Competitiveness

2. Extracting data from ASI The raw ASI data is available in different blocks in ASI. Each block provides the following information

Table 2: ASI Information Block

Information

Identification Particulars – Year, Block, State Code, District Code, Sector, Industry Code, Number of Working Days, Cost of Production, Multiplier, etc.

Owners Details – Type of organization, Type of ownership, Total number of units, the Original value of Investment in P & M (codes), ISO Certification, Year of initial production, Accounting year, Months of operation, Computerised A/C system and availability of data in Computer.

Fixed Assets Details – Gross Value, Net Value, Depreciation

Working Capital – Woking Capital opening and closing

Employment &Labour Cost – Manufacturing man-days worked, Non-Manufacturing man-days worked, average number of persons worked, number of man-days paid for, wages & salaries

Other Expenses – Purchase value of goods; interest paid, rent paid, total expenses, operating and non-operating expenses, insurance, repair, and maintenance

Other Incomes – Income from services, total receipts, rent received, interest received, subsidies, the balance of goods, the value of own construction

Input Items –

Clusters: The Drivers of Competitiveness

Quantity consumed, purchase value, the rate per unit, item and unit code I

Input Items Imported – Quantity consumed, purchase value, the rate per unit, item and unit code

Products – Quantity manufactured, quantity sold, gross and net sale value, excise, sales tax, ex-factory value of quantity manufactured

Each block contains the “Identifier” named DSL. This will be used to merge the data across blocks.

Each block is merged with Block A to get the required information. 3. Aggregating the ASI data The second step is to aggregate the required data in the cluster format created in the above step. We require the data for the following variables: Table 3: Definition Variable

Definition (ASI)

Number of Units

The number of units that are working, i.e., keep the status of the unit to 1.

Production Workers

Male and female workers employed directly, and workers employed through contractors

Skilled Workers

Supervisory & managerial staff and other employees

Total Employees

The sum of production workers, skilled workers, and unpaid family members.

Skilled Worker Wages

Wages of supervisory & managerial staff and other employees

Production Worker Wages

Wages of male and female workers employed directly, and workers employed through contractors

Total Employee Wages

Wages of production workers, skilled workers, and unpaid family members.

Gross Value Added

Total Output – Total Inputs 22

Clusters: The Drivers of Competitiveness

Where, Total Output = Ex-factory value of quantity manufactured + Total receipts Total Input = Materials consumed + Fuels consumed + Other input values

➢ Collect the number of units, production workers, skilled workers, total employees, production worker wages, skilled worker wages and total wages at the factory level for each state from different blocks after merging them with Block A. They represent the actual values. ➢ Use the multiplier value (already provided by ASI) to get the estimated figures for each of the variables. ➢ Aggregate this factory level in the cluster format (based on 5-digit NIC industry codes) using the following formula: Number of Unitscr = ∑ Number of Unitsir Where, c = cluster r = region i = 5-digit industries in the cluster c. ➢ Use the same formula for aggregating other variables. Limitations First, the ASI data relates only the organized manufacturing sector in India, and according to RBI8 statistics, the unorganized manufacturing sector accounts for 80 percent of the total employment in the Indian manufacturing industry. So, this data leaves out a major portion of the manufacturing industry. Second, the period of the study is 1999-2014. As the industry codes changed in 1998, 2004 and in 2008 a mapping of the codes is done. But a complete concordance at the five-digit level is not possible due to the structural differences in the grouping of activities in the two systems. For instance, some of the 5-digit subclasses of NIC-2004 have been made separate 4-digit classes in NIC-2008. Therefore, some of the results presented in chapter 5 are from 2009-2014. The definitions propose 51 Traded and 16 Local Clusters. Traded industries are those that are located in particular regions but sell products across regions and countries. (Delgado, Bryden, &Zyontz). In contrast, local industries are dispersed throughout the nation. Their presence in a particular region tends to be proportional to the region’s size since these industries primarily serve the local market. Examples of local industries would be real estate services, hospitals, etc. Traded Clusters are formed by grouping traded industries, and likewise, the groups of local industries form local clusters.

8https://www.financialexpress.com/industry/achieving-indias-manufacturing-growth-dream/91333/

Clusters: The Drivers of Competitiveness

Figure 4: Type of Industries

TABLE 4: TRADED CLUSTERS

Cluster Code

Cluster Name

Cluster Code

Cluster Name

Aerospace Vehicles and Defense

Lighting and Electrical Equipment

Agricultural Products, Inputs and Services

Livestock Processing

Apparel

Marketing, Design, and Publishing

Automotive

Medical Devices

Biopharmaceuticals

Metal Mining

Business Services

Metalworking Technology

Coal Mining

Music and Sound Recording

Communications Equipment and Services

Nonmetal Mining

Construction Products and Services

Oil and Gas Production and Transportation

Distribution and Electronic Commerce

Paper and Packaging

Downstream Chemical Products

Performing Arts

Downstream Metal Products

Plastics

Education and Knowledge Creation

Printing Services

Electric Power Generation and Transmission

Production Technology and Heavy Machinery 24

Clusters: The Drivers of Competitiveness

Environmental Services

Recreational and Small Electric Goods

Financial Services

Textile Manufacturing

Fishing and Fishing Products

Tobacco

Food Processing and Manufacturing

Trailers, Motor Homes, and Appliances

Footwear

Transportation and Logistics

Forestry

Upstream Chemical Products

Furniture

Upstream Metal Manufacturing

Hospitality and Tourism

Video Production and Distribution

Information Technology and Analytical Instruments

Vulcanized and Fired Materials

Insurance Services

Water Transportation

Jewelry and Precious Metals

Wood Products

Leather and Related Products

Clusters: The Drivers of Competitiveness

TABLE 5: LOCAL CLUSTERS

Cluster Code

Cluster Name

101

Local Food and Beverage Processing and Distribution

102

Local Personal Services (Non-Medical)

103

Local Health Services

104

Local Utilities

105

Local Logistical Services

106

Local Household Goods and Services

107

Local Financial Services

108

Local Motor Vehicle Products and Services

109

Local Retailing of Clothing and General Merchandise

110

Local Entertainment and Media

111

Local Hospitality Establishments

112

Local Commercial Services

113

Local Education and Training

114

Local Community and Civic Organizations

115

Local Real Estate, Construction, and Development

116

Local Industrial Products and Services

Clusters: The Drivers of Competitiveness

CLUSTER ASSESSMENT The following framework is used to measure the overall performance of the cluster:

Figure 5: Assessing the performance of clusters

Specialization reflects how strong a region is in a cluster category compared to all other regions. This is captured by identifying top 20 percent of the locations by Location Quotient. Location Quotient (LQ) is a measure of a regionâ&#x20AC;&#x2122;s specialization. It captures the degree to which a particular industry or cluster is concentrated in a region compared to the nation. (Delgado, Bryden, &Zyontz) It is calculated as follows:

đ?&#x2018;&#x2020;â&#x201E;&#x17D;đ?&#x2018;&#x17D;đ?&#x2018;&#x;đ?&#x2018;&#x2019; đ?&#x2018;&#x153;đ?&#x2018;&#x201C; đ?&#x2018;&#x;đ?&#x2018;&#x2019;đ?&#x2018;&#x201D;đ?&#x2018;&#x2013;đ?&#x2018;&#x153;đ?&#x2018;&#x203A;đ?&#x2018;&#x17D;đ?&#x2018;&#x2122; đ?&#x2018;&#x2019;đ?&#x2018;&#x161;đ?&#x2018;?đ?&#x2018;&#x2122;đ?&#x2018;&#x153;đ?&#x2018;Śđ?&#x2018;&#x161;đ?&#x2018;&#x2019;đ?&#x2018;&#x203A;đ?&#x2018;Ą đ?&#x2018;&#x2013;đ?&#x2018;&#x203A; đ?&#x2018;&#x;đ?&#x2018;&#x2019;đ?&#x2018;&#x201D;đ?&#x2018;&#x2013;đ?&#x2018;&#x153;đ?&#x2018;&#x203A;đ?&#x2018;&#x17D;đ?&#x2018;&#x2122; đ?&#x2018;&#x2013;đ?&#x2018;&#x203A;đ?&#x2018;&#x2018;đ?&#x2018;˘đ?&#x2018; đ?&#x2018;Ąđ?&#x2018;&#x;đ?&#x2018;Ś đ?&#x2018;&#x153;đ?&#x2018;&#x; đ?&#x2018;?đ?&#x2018;&#x2122;đ?&#x2018;˘đ?&#x2018; đ?&#x2018;Ąđ?&#x2018;&#x2019;đ?&#x2018;&#x; đ?&#x2018;&#x2020;â&#x201E;&#x17D;đ?&#x2018;&#x17D;đ?&#x2018;&#x;đ?&#x2018;&#x2019; đ?&#x2018;&#x153;đ?&#x2018;&#x201C; đ?&#x2018;&#x2013;đ?&#x2018;&#x203A;đ?&#x2018;&#x2018;đ?&#x2018;˘đ?&#x2018; đ?&#x2018;Ąđ?&#x2018;&#x;đ?&#x2018;Ś â&#x20AC;˛ đ?&#x2018; đ?&#x2018;&#x153;đ?&#x2018;&#x; đ?&#x2018;?đ?&#x2018;&#x2122;đ?&#x2018;˘đ?&#x2018; đ?&#x2018;Ąđ?&#x2018;&#x2019;đ?&#x2018;&#x; â&#x20AC;˛ đ?&#x2018; đ?&#x2018;&#x2019;đ?&#x2018;&#x161;đ?&#x2018;?đ?&#x2018;&#x2122;đ?&#x2018;&#x153;đ?&#x2018;Śđ?&#x2018;&#x161;đ?&#x2018;&#x2019;đ?&#x2018;&#x203A;đ?&#x2018;Ą đ?&#x2018;&#x2013;đ?&#x2018;&#x203A; đ?&#x2018;&#x203A;đ?&#x2018;&#x17D;đ?&#x2018;Ąđ?&#x2018;&#x2013;đ?&#x2018;&#x153;đ?&#x2018;&#x203A;đ?&#x2018;&#x17D;đ?&#x2018;&#x2122; đ?&#x2018;&#x2019;đ?&#x2018;&#x161;đ?&#x2018;?đ?&#x2018;&#x2122;đ?&#x2018;&#x153;đ?&#x2018;Śđ?&#x2018;&#x161;đ?&#x2018;&#x2019;đ?&#x2018;&#x203A;đ?&#x2018;Ą where,

The share of regional employment in the regional industry =

đ?&#x2018;&#x2021;đ?&#x2018;&#x153;đ?&#x2018;Ąđ?&#x2018;&#x17D;đ?&#x2018;&#x2122; đ??¸đ?&#x2018;&#x161;đ?&#x2018;?đ?&#x2018;&#x2122;đ?&#x2018;&#x153;đ?&#x2018;Śđ?&#x2018;&#x161;đ?&#x2018;&#x2019;đ?&#x2018;&#x203A;đ?&#x2018;Ąđ?&#x2018;&#x2013;,đ?&#x2018;&#x; đ?&#x2018;&#x2021;đ?&#x2018;&#x153;đ?&#x2018;Ąđ?&#x2018;&#x17D;đ?&#x2018;&#x2122; đ??¸đ?&#x2018;&#x161;đ?&#x2018;?đ?&#x2018;&#x2122;đ?&#x2018;&#x153;đ?&#x2018;Śđ?&#x2018;&#x161;đ?&#x2018;&#x2019;đ?&#x2018;&#x203A;đ?&#x2018;Ąđ?&#x2018;&#x;

Clusters: The Drivers of Competitiveness

The share of industryâ&#x20AC;&#x2122;s employment in national employment =

đ?&#x2018;&#x2021;đ?&#x2018;&#x153;đ?&#x2018;Ąđ?&#x2018;&#x17D;đ?&#x2018;&#x2122; đ??¸đ?&#x2018;&#x161;đ?&#x2018;?đ?&#x2018;&#x2122;đ?&#x2018;&#x153;đ?&#x2018;Śđ?&#x2018;&#x161;đ?&#x2018;&#x2019;đ?&#x2018;&#x203A;đ?&#x2018;Ąđ?&#x2018;&#x2013; đ?&#x2018;&#x2021;đ?&#x2018;&#x153;đ?&#x2018;Ąđ?&#x2018;&#x17D;đ?&#x2018;&#x2122; đ??¸đ?&#x2018;&#x161;đ?&#x2018;?đ?&#x2018;&#x2122;đ?&#x2018;&#x153;đ?&#x2018;Śđ?&#x2018;&#x161;đ?&#x2018;&#x2019;đ?&#x2018;&#x203A;đ?&#x2018;Ą

Size is measured by identifying top 20 percent of the location by employment. This is a significant measure of performance as the number of linkages within a cluster increases with an increase in the number of participants. Productivity reflects how well is cluster operating. This is measured by identifying the top 20 percent of the locations by gross value added and average wages. Dynamism, captured by the top 20 percent of the locations by employment growth, reflects whether a cluster is benefitting from strong cluster effects from its development. To reach a single measure of cluster performance, we follow a four-star methodology. A star is assigned for each of the four dimensions to the regions that are in top 20 percent. As there are around 31 regions that are considered for this study a star is assigned to 6 regions in each dimension for every cluster category. (This is a modified version of the methodology followed by Ketels and Protsiv, 2014) For instance, if Maharashtra is in top 20 percent of the regions by employment for Automotive Cluster, it will be assigned one star, and Automotive will be a one-star cluster in Maharashtra. But if for the same cluster it lies in the top 20 percent regions by location quotient then it will get two stars. Being in the top 20 percent regions for the same cluster by productivity and dynamism will earn Maharashtra four stars. The strength of a regionâ&#x20AC;&#x2122;s cluster portfolio is measured by summing up the performance across its individual clusters. For instance, if a state has four one-star clusters, seven two-star clusters, four three-star clusters and five four-star clusters, then the total stars that the state gets are 4*1 + 7*2 + 4*3 + 5*4, i.e., 50. While these indicators provide powerful insights about the performance of clusters across regions some caveats should be kept in mind: -

First, large regions benefit because they will have high absolute employment numbers. Second, large regions are less likely to have high location quotient (as observed in the previous section). This is because they tend to have employees spread across many clusters. Third, high wages are not only a measure of high productivity but also of the general cost and wage levels in a region.

The cluster strength of Indian states is presented in the table below. Table 6: Cluster Strength State Name

Total Stars

State Name

Total Stars

State Name

Total Stars

Andhra Pradesh

Haryana

Orissa

Assam

Himachal Pradesh

Pondicherry

Bihar

Jammu & Kashmir

Punjab

Chandigarh

Jharkhand

Rajasthan

Chhattisgarh

Karnataka

Sikkim

Clusters: The Drivers of Competitiveness

Dadra & Nagar Haveli

Kerala

Tamil Nadu

100

Daman & Diu

Madhya Pradesh

Tripura

Delhi

Maharashtra

109

Uttar Pradesh

123

Goa

Manipur

Uttarakhand

Gujarat

Meghalaya

West Bengal

Haryana

Nagaland

Clusters: The Drivers of Competitiveness

CHAPTER 2.2: DEFINING THE INNOVATIVE CAPACITY OF REGIONS Innovation is largely used to describe a ‘new idea device or method.’9 The Oslo Manual (2006)10 defines innovation as ‘the implementation of a new or significantly improved product (good or service), or process, a new marketing method, or a new organizational method in business practices, workplace organization or external relations.’ The following broad pillars and parameters were considered for the construction of the state innovation index in India. 1. 2. 3. 4.

Factors of Production Demand Conditions Industries, Innovation and Entrepreneurship Social and Political Institutions

These four major pillars are further categorized into sub-indices. There are four sub-indices for factors of production, two for demand conditions, two for Industries, Innovation and Entrepreneurship and two for Social and Political Institutions. These sub-indices further have indicators. Thus, there is a 3-level hierarchy for measurement for the overall competitiveness score of states of India (Figure 6).

Figure 6: State Innovation Index

Source: Institute for Competitiveness, India

http://www.learnersdictionary.com/definition/innovation Accessed from http://www.oecdilibrary.org/docserver/download/9205111e.pdf?expires=1468997818&id=id&accname=guest&checksum=269A09B74A5 7EB12A2ADC568E18CC552 P.46. 9

Clusters: The Drivers of Competitiveness

Analysis The State Innovation Index is an aggregate of all the individual indicators. Thus, one sees an overall contribution to the value of the Index by all the insignificant individual indicators considered for the study. Almost 50 indicators were used for assessing the Innovation environment of Indian states. The sub-pillar measure and the pillar weights were assigned on the basis of extensive research and a principal component factor analysis. Table 7: State Innovation Scores State

Innovation Score

Andhra Pradesh

26.73

Assam

21.72

Bihar

14.12

Chhattisgarh

19.10

Delhi

42.88

Goa

36.45

Gujarat

30.47

Haryana

26.86

Himachal Pradesh

30.10

Jammu & Kashmir

25.03

Jharkhand

12.97

Karnataka

35.07

Kerala

36.36

Madhya Pradesh

22.28

Maharashtra

37.84

Manipur

23.41

Meghalaya

19.44

Nagaland

21.90

Odisha

19.79

Punjab

27.18

Rajasthan

23.71

Sikkim

28.15

Tamil Nadu

42.56

Tripura

22.86

Uttar Pradesh

38.50

Uttarakhand

22.35

West Bengal

28.75

Clusters: The Drivers of Competitiveness

CHAPTER 2.3: DEFINING REGIONAL COMPETITIVENESS The quality of the region’s business environment is embodied in four broad areas (Figure 7). Factor Conditions: They measure the health of the factors that directly affect the productivity of any region. These include factors of production; not just the conventional ones like land, labor, and capital but also specialized factors like better infrastructure, skilled labor, etc. The sub-pillars to measure factor conditions are as follows•

•

Physical factor conditions: Physical factor conditions include the endowments that a region inherits the endowments as well a region creates. Physical factor conditions include natural endowments, like arable land or tree cover, as well as created endowments like the physical infrastructure of transport and energy. Financial factor conditions: This sub-pillar covers the macroeconomic, financial health of the region. It also includes the ability of the population of a region to spend/save, and the ability of financial institutions to absorb those saving and loan out for productive economic activities. Communication Factor conditions: In this era of ICT, communication infrastructure plays a vital role in disseminating information. Hence, maintaining a healthy communication infrastructure has become indispensable for a region trying to be competitive. This sub-pillar is evaluated by measuring the health of information and communication technology in a particular region. Administrative factor conditions: The administrative factor condition is measured by evaluating the condition of law and order, the ability of a region to maintain a healthy workforce and the ease of doing business in a particular region. Human Capacity: Developing human capital in a region is an essential part of enhancing competitiveness and prosperity. Since labor is one of the most basic inputs for production, enhancing labor skills can directly increase productivity. The human capacity sub-pillar is evaluated by indicators such as the proportion of working-age population as well as education and skills of the population in a region.

Figure 7: Diamond Model. Source: Michael E Porter 32

Clusters: The Drivers of Competitiveness

•

Innovation: A critical in ingredient for staying at par with others is being innovative. Innovation shouldn’t be seen as a one-time thing rather it’s a continuous process. We measure this sub-pillar by evaluating the institutions of higher education and research as well as reforms taken by the government to enhance innovative capabilities of the population.

Demand Conditions: These represent the forces that are important in shaping consumer expenditure. The changes in the type of type of demand shape the relationships between firms/enterprises/ business and consumers. The sub-pillars used to evaluate demand conditions are•

•

Demographics: The structure and composition of the population ultimately decide the nature of the business and economic activities that can successfully run in a region. To evaluate this sub-pillar, we use the proportion of population falling in different age groups and the density of population in a region. Income distribution: An equal and prosperous society generates demand for better infrastructure, administration and sets the ground for a thriving business environment. To measure this sub-pillar, we evaluate the assets possessed by the population of a region and measures of inequality.

The context for strategy and rivalry: Firms work to increase productivity and innovation primarily by direct competition. This market becomes the battlefield for domestic and foreign companies to compete for profits and sustainability. The local rules for taxation, FDI, Foreign trade, remittances and the incentives structure can, therefore, make or mar the conditions for business success. The sub-pillars used to evaluate this are• •

Competitive Intensity and Diversity of firms: Diversification of industries increase both competition and competitiveness of a region. Business incentives: Business incentives evaluate the ease of doing business in a region. This includes indicators such as the ease of getting funds for starting a business and labor market stability in the region.

Related and supporting industries: Presence of clusters rather than isolated firms offers proximity of upstream and downstream industries and allows for the interchange of knowledge and increases firm productivity. This also helps in meeting depth of demand and innovation. •

•

Suppliers Sophistication: This sub-pillar measures the availability of supplies needed for production. The presence of industries producing capital goods and export units are used to evaluate this subpillar. Industrial Support: Industrial support evaluates the strength of those institutes and policies that enhance the productivity of manufacturing units. Creation of special economic zones and presence of commercial banks and other financial institutions are used to evaluate this sub-pillar.

Clusters: The Drivers of Competitiveness

Results: Table 8: Competitiveness Scores State Name

SCR Score

Andhra Pradesh

33.49758558

Assam

22.28600633

Bihar

21.89813681

Chhattisgarh

27.25544021

Delhi

49.84407713

Goa

48.56751592

Gujarat

47.16145458

Haryana

38.06577417

Himachal Pradesh

37.42204896

Jammu & Kashmir

23.63727104

Jharkhand

24.71309129

Karnataka

50.29020375

Kerala

42.1841105

Madhya Pradesh

25.30601792

Maharashtra

48.75058042

Manipur

22.30806725

Meghalaya

20.06819375

Nagaland

25.8638988

Odisha

24.21254704

Punjab

39.98942171

Rajasthan

26.71277088

Sikkim

32.61570463

Tamil Nadu

48.63450833

Tripura

29.37003183

Uttar Pradesh

35.48146175

Uttarakhand

27.44717708

West Bengal

31.24848588

Clusters: The Drivers of Competitiveness

CHAPTER 2.4: DEFINING THE ECONOMIC PERFORMANCE OF REGIONS The performance should be measured by multiple parameters that can bring out important insights not only about the current level of prosperity but also about advancements in living standards, and how it affects competitiveness and productivity. It is important to have a holistic view that encompasses all these parameters due to the interlinkages that exist between them. The standard of living that depicts the purchasing power of the citizens is affected by the level of employment and the average wages of the region. The employment levels and average wages are in turn a result of the productivity levels of the firms. And the foundation of productive capacity is innovation. We use average wages, wage growth, employment, and employment growth to gain knowledge about the regional economic performance.

Economic Performance Average Wages Wage Growth

Unemployment Growth Rate of employment Figure 8: Measures of Economic Performance

Clusters: The Drivers of Competitiveness

Results: Table 9: Economic Performance State Name

Total Employees

Growth Rate of employment

Average Wage

(2009 to 2014) Andaman Islands

Nicobar 520

1.867875844

137174.1712

Andhra Pradesh

5,19,758.52

-15.17045452

170196.8856

Assam

1,86,266.25

7.198842198

108074.1101

Bihar

1,13,180.74

7.310600267

95807.44371

Chandigarh

13,151.17

3.597803177

260771.6882

Chhattisgarh

1,65,825.31

-6.683145142

285104.2737

Dadra & Nagar Haveli

1,48,506.10

7.904599619

129785.1184

Daman & Diu

1,09,336.01

6.332015967

162049.9454

Delhi

1,11,684.50

-7.749896263

215873.9974

Goa

56,688

4.132761639

286298.0778

Gujarat

13,71,770.97

11.02130661

193668.1839

Haryana

6,10,646.10

4.300436346

232140.3899

Himachal Pradesh

1,94,661.45

8.680516279

228626.0519

Jammu & Kashmir

66,595.83

5.540434034

131532.2247

Jharkhand

1,87,822.81

6.616293397

301133.9645

Karnataka

9,27,048.96

9.981282935

213896.2023

Kerala

3,51,127.42

-8.78753321

144208.5051

Madhya Pradesh

3,22,884.91

7.714019469

197151.2407

Maharashtra

18,77,581.53

12.17614201

269027.4636

Manipur

5,868

4.028160992

58340.66121

Meghalaya

13,425

5.012601279

162427.8209

Nagaland

3,741

2.868152418

53420.32638

Clusters: The Drivers of Competitiveness

Orissa

2,51,550.82

7.254782601

229932.2633

Pondicherry

50,042.33

2.760590008

150642.5762

Punjab

6,00,992.53

7.968784967

131541.6036

Rajasthan

4,70,121.67

9.398179927

181493.1964

Sikkim

12,749

5.623617185

226080.5345

Tamil Nadu

20,29,272.18

11.07335853

173380.0633

Tripura

28,940

3.92140784

35139.99568

Uttar Pradesh

9,10,832.02

10.18371149

184816.0704

Uttarakhand

3,86,075.71

9.938821207

164513.2357

West Bengal

6,42,123.94

8.856379798

160650.9808

Clusters: The Drivers of Competitiveness

CHAPTER 2.5 FINDINGS AND DISCUSSION OF RESULTS A. Analyzing the differences in economic performance, competitiveness, local & traded industries, cluster strength and innovative capacity of regions. 1. REGIONS VARY SIGNIFICANTLY IN TERMS OF AVERAGE WAGES

Figure 9 depicts the average wages of workers across Indian states and shows that the national average in 2014 was Rs. 176,677. However, the details reveal some surprising statistics. 350000.00

Average Wage, 2014

300000.00 250000.00 200000.00 150000.00 100000.00 50000.00

Jharkhand Goa Chhattisgarh Maharashtra Chandigarh Haryana Orissa Himachal Pradesh Sikkim Delhi Karnataka Madhya Pradesh Gujarat Uttar Pradesh Rajasthan Tamil Nadu Uttarakhand Meghalaya Daman & Diu West Bengal Pondicherry Andhra Pradesh Kerala Andaman & Nicobar Islands Jammu & Kashmir Punjab Dadra & Nagar Haveli Assam Bihar Manipur Nagaland Tripura

0.00

Figure 9: Average Wages by State, 2014 First, it is shocking that the average wage of the highest earning state is nine times that of the poorest one. This depicts the regional differences in standard of living. Second, Jharkhand turns out to be the state with the highest average wages; not a state usually associated with prosperity. Annual reports by the Labour Bureau also concur with this finding and point to the fact that the highest wages per man-day paid to all workers are the highest in Jharkhand (Labour Bureau, 2012-13). The third surprising finding from Figure 9 is that industrial states like Tamil Nadu and Andhra Pradesh have average wages that fall below the national average. A closer look at the employment and wage scenario in these states reveals that even though they are known for some high-paying sectors (automobiles in Tamil Nadu and pharmaceuticals in Andhra Pradesh), most of the workers are employed in low-paying ones. Figure 10 illustrates this fact for Andhra Pradesh. It plots total employees and wages in Andhra Pradesh for some major sectors. A clear negative relationship between the two is observed. 38

0.00

Food Processing and… Textile Manufacturing Construction Products and… Upstream Metal Manufacturing Agricultural Products, Inputs… Biopharmaceuticals Fishing and Fishing Products Apparel Paper and Packaging Vulcanized and Fired Materials Tobacco Plastics Upstream Chemical Products Production Technology and… Printing Services Downstream Metal Products Electric Power Generation… Communications Equipment… Footwear Oil and Gas Production and… Lighting and Electrical… Automotive Downstream Chemical Products Water Transportation Distribution and Electronic… Recreational and Small… Wood Products Information Technology and… Metalworking Technology Jewelry and Precious Metals Leather and Related Products Environmental Services Furniture Aerospace Vehicles and… Livestock Processing Nonmetal Mining Medical Devices Marketing, Design, and… Trailers, Motor Homes, and…

Clusters: The Drivers of Competitiveness

Food Processing and Manufacturing, the highest employer in Andhra Pradesh is one of the lowest paying sectors.

Andhra Pradesh

1,40,000.00

1,20,000.00

1,00,000.00

80,000.00

60,000.00

40,000.00

20,000.00

Total Employees Average Wages (In 10)

Figure 10: Cluster Wise Employment in Andhra Pradesh

2. ECONOMIC CONVERGENCE BETWEEN THE STATES IS ON THE RISE

The average state also experienced an annual wage growth of 17.17 percent between 2009 and 2014 as seen in Figure 11. It is interesting to note that Jharkhand is at the wrong end of the chart with a compound annual growth rate of 11.3 percent, which is well below the national average. Therefore, its status as the highest wage payer on an average among Indian states is not a recent phenomenon.

CAGR of Wages (%), 2009-14

Clusters: The Drivers of Competitiveness

40 35 30 25 20

National Average = 17.17%

15 10 5 0

Figure 11: CAGR of Wages Another noteworthy trend is that most states which had average wages below the national average are the ones that have displayed higher growth in wages. The implication that can be drawn is that convergence between the states is on the rise, especially for the north-eastern states. The reduction in regional disparity is clearly shown in Figure 12, which plots the average wage for the states in 2009 against its CAGR over the period 2009-14. There exists a medium level of negative correlation between the two implying that states which had a higher average wage, to begin with, grew slower than the ones with a lower average wage. It can be said that economic success or failure is not dependent on their starting levels but are affected by factors that will be explained later in chapter 6.

Clusters: The Drivers of Competitiveness

CAGR of Average Wage, 2009-2014

Nagaland Kerala

Chandigarh

Haryana

Delhi Goa Tamil Nadu Karnataka Rajasthan Himachal Pradesh Andhra Pradesh Punjab Uttar Pradesh Jammu & Kashmir Assam Maharashtra Bihar West Bengal Chhattisgarh Tripura Odisha Jharkhandy = -6E-05x + 17.91 Pondicherry RÂ˛ = 0.4097 Madhya Pradesh Dadra & Nagar Haveli Manipur

Uttarakhand

0 0

50000

100000

150000

200000

250000

300000

350000

Sikkim

-5

-10

Average Wage, 2009

Figure 12: Average Wages vs CAGR of Average Wages A mobility matrix is another insightful way of visualizing the shift in average wages across regions over time. The states can be grouped into wage deciles for 2009 and 2014 and can be examined for mobility during the period. It shows that 43.7 percent of the states showed no income mobility and remained in the same decile during the examined period. On the other hand, 34.3 percent of the states showed upward income mobility, and the rest moved to a lower decile. It is noteworthy that four states, Tamil Nadu, Rajasthan, Delhi, and Haryana moved up two or more wage deciles. Thus, one can conclude that shocking regional disparities in average wages exist in India, but they are depicting a downward trend during 2009-2014.

Clusters: The Drivers of Competitiveness

Average Wages: Mobility Matrix 2014 1

Assam, Punjab

Jammu & Kashmir

Andhra Pradesh

Andaman & Nicobar

Rajasthan

West Bengal

Haryana

Dadra & Nagar Haveli

Uttar Pradesh

Delhi Madhya Pradesh, Gujarat

Pondicherry

Karnataka

Himachal Pradesh

Tamil Nadu Daman & Diu, Meghalaya

Kerala

Nagaland, Tripura, Manipur, Bihar

2009

Chandigarh

Orissa

Uttarakhand

Sikkim

Goa, Maharashtra Chhattisgarh, Jharkhand

3. CLUSTER PROFILE OF REGIONS DIFFERS SIGNIFICANTLY, WITH SOUTHERN REGION OUTPERFORMING THE REST OF THE COUNTRY

The map below (Figure 13) presents the results of the cluster assessment methodology described above. The regional cluster portfolio strength is reached by counting all stars achieved across cluster categories. A clear geographical distinction is observed, with the Southern region having a stronger cluster profile than the rest of the country. 32 percent of the regions have less than 20 stars, implying the lack of strong clusters that can enhance competitivenesss and increase prosperity in the region.

Clusters: The Drivers of Competitiveness

Figure 13: Cluster Strength Table 10 below presents the complete cluster portfolio of a state. Each state has a distinct profile of clusters creating prosperity.

Table 10: Cluster Profile State Name

Andhra Pradesh

Total Stars

One Star Clusters 5

Two Star Clusters

Three Star Clusters

Four Star Clusters

Top 3 clusters by LQ

Fishing and Fishing Products Agricultural Products, Inputs and Services Construction Products and Services

Assam

Oil and Gas Production and Transportation Vulcanized and Fired Materials Food Processing and Manufacturing

Bihar

Vulcanized and Fired Materials Distribution and Electronic Commerce Local Entertainment and Media

Clusters: The Drivers of Competitiveness

Chandigarh

Local Entertainment and Media Local Utilities Local Motor Vehicle Products and Services

Chhattisgarh

Upstream Metal Manufacturing Electric Power Generation and Transmission Tobacco

Dadra & Nagar Haveli

Printing Services Plastics Textile Manufacturing

Daman & Diu

Plastics Lighting and Electrical Equipment Recreational and Small Electric Goods

Delhi

Video Production and Distribution Local Industrial Products and Services Local Household Goods and Services

Goa

Medical Devices Water Transportation Communications Equipment and Services

Gujarat

Nonmetal Mining Marketing, Design, and Publishing Jewelry and Precious Metals

Haryana

Medical Devices Recreational and Small Electric Goods Automotive

Himachal Pradesh

Business Services Trailers, Motor Homes, and Appliances Biopharmaceuticals

Jammu & Kashmir

Upstream Chemical Products Biopharmaceuticals Furniture

Jharkhand

Upstream Metal Manufacturing Local Utilities Electric Power Generation and Transmission

Karnataka

Local Hospitality Establishments Aerospace Vehicles and Defense Business Services

Kerala

Fishing and Fishing Products Water Transportation

Clusters: The Drivers of Competitiveness

Local Entertainment and Media Madhya Pradesh

Business Services Environmental Services Local Utilities

Maharashtra

109

Video Production and Distribution Music and Sound Recording Local Commercial Services

Manipur

Local Household Goods and Services Vulcanized and Fired Materials Local Personal Services (Non-Medical)

Meghalaya

Construction Products and Services Wood Products Local Commercial Services

Nagaland

Wood Products Vulcanized and Fired Materials Local Motor Vehicle Products and Services

Orissa

Coal Mining Metal Mining Upstream Metal Manufacturing

Pondicherry

Information Technology and Analytical Instruments Upstream Chemical Products Furniture

Punjab

Recreational and Small Electric Goods Vulcanized and Fired Materials Metalworking Technology

Rajasthan

Construction Products and Services Furniture Electric Power Generation and Transmission

Sikkim

Biopharmaceuticals Downstream Chemical Products Local Personal Services (Non-Medical)

Tamil Nadu

100

Footwear Textile Manufacturing Automotive

Tripura

Vulcanized and Fired Materials Local Real Estate, Construction, and Development Tobacco

Clusters: The Drivers of Competitiveness

Uttar Pradesh

123

Livestock Processing Distribution and Electronic Commerce Footwear

Uttarakhand

Local Real Estate, Construction, and Development Trailers, Motor Homes, and Appliances Communications Equipment and Services

West Bengal

Music and Sound Recording Local Household Goods and Services Leather and Related Products

4. THERE IS NO SIGNIFICANT DIFFERENCE IN THE AVERAGE WAGES AND WAGE GROWTH BETWEEN THE TRADED AND LOCAL CLUSTERS AT THE COUNTRY LEVEL

We begin by analyzing the employment and average wages of local clusters in comparison to traded clusters. Surprisingly, traded clusters account for 96 percent of the total employment in India, as shown in Table 11. The main reason for such a high proportion of employment in the traded industries is that the ASI dataset used relates to the manufacturing sector, and most of the industries that are classified as local belong to services sector; for instance, health and financial services. Unfortunately, there exists no reliable source of data available for the service industries to remedy this anomaly. Also, some issues in the ASI dataset itself might also have caused the employment in local industries to be so low. Precisely, data for 72 percent of the industries that form local clusters are missing as against 32 percent of the missing data for traded industries. Table 11: Composition of Indian economy by type of Clusters Traded

Local

920

229

The share of Employment (%)

96.20202267

3.797977334

Average Wages

197090.8465

185992.0749

Wage Growth

8.774509545

8.433467078

Number of Industries

Nevertheless, we can still draw the conclusion that employment in traded clusters for manufacturing industries is quite high in India (even though it might not be as high as 96 percent). However, three regions Andaman & Nicobar Islands, Chandigarh, Delhi are outliers to this trend, as seen in Figure 14. The share of local employment in these regions is 63 percent, 71 percent, and 84 percent respectively. The results are foreseeable as far as Andaman & Nicobar Islands are concerned as the region lies in the Bay of Bengal and is far away from the mainland, thus making it difficult to obtain local product and services from adjacent regions. Therefore, the high presence of local industries is expected.

Clusters: The Drivers of Competitiveness

2500000

Total Employment, 2014

2000000 y = 11380x - 678183 R² = 0.0279

1500000 1000000 500000 0 50

Percentage of Traded Employment, 2014

100

110

Figure 14. Employment Size vs. percentage of traded employment by States Table 11 also shows that there is no significant difference in the average wages for the traded and local clusters. The average wage for the traded clusters is Rs.197090, which is higher than the average wage of local clusters by Rs.11098. This result stands in sharp contrast to what has been observed across the world. Porter (2003) found that for the US economy the wages for local industries are 66 percent of the wages for traded industries. Similarly, Ketels and Prostiv (2016) observed that wages in traded industries are 17 percent higher than the wages in local industries. The higher wages are mainly driven by the higher levels of productivity in traded industries. To understand why India deviates from the trend we look at the state level differences in traded and local wages. As shown in Figure 15, there are significant differences between the local and traded wages at the state level which ranges from 1,45,294 to – 1,28,279.

DIFFERENCE IN TRADED AND LOCAL WAGES

1,50,000 1,00,000 50,000

-50,000 -1,00,000 -1,50,000

Jharkhand Orissa Goa Sikkim Meghalaya Himachal Pradesh Uttarakhand Karnataka Chhattisgarh Pondicherry Uttar Pradesh Madhya Pradesh Rajasthan Gujarat Tamil Nadu Haryana Andaman & Nicobar Islands Daman & Diu India Assam Dadra & Nagar Haveli Andhra Pradesh Maharashtra Jammu & Kashmir Kerala West Bengal Tripura Punjab Nagaland Manipur Bihar Chandigarh Delhi

Figure 15: Differences in Average Traded and Local Wages (Traded – Local)

Clusters: The Drivers of Competitiveness

If we dig deeper and analyze the states where the average wages in local industries are higher, i.e. Delhi, Chandigarh, Bihar we notice the presence of “Local Entertainment and Media” industry. The industry comprises of newspapers, electronic media, movie theaters, book and periodic retailing, video rental, sporting and hobby retailing and electronic and photographic retailing. The wages in Local Entertainment and Media industry are 129 percent higher than the average local wages. It employs 20085 people, and the total wages are 8,56,72,42,746 making it a highly productive industry. The relationship between the percentage of employment in this industry and the difference in traded & local wages is depicted in Figure 16. A weak negative relationship between the two is seen. So, this high paying industry drives the high average local wages which is the reason why India deviates from the trend.

y = -5646.3x + 36090 R² = 0.3364

200000

Difference in Average Traded and Average Local Wages

Jharkhand 150000

Orissa Uttar Pradesh Sikkim Meghalaya 100000 Goa Pondicherry Karnataka Uttarakhand Chhattisgarh 50000 Andhra Pradesh Rajasthan Gujarat Madhya Pradesh AndamanHaryana & Nicobar Daman & Diu Assam Islands 0 West 15 Bengal 0 5 10 Punjab Lakshadweep -50000 Tripura Bihar Nagaland Kerala Maharashtra -100000 Manipur Jammu & Kashmir Delhi Tamil Nadu -150000

Chandigarh

Employment in Local Entertainment and Media as a percentage of Total Local Employment

Figure 16: Relationship between differences in average local and traded wages & the presence of the local entertainment industry We also find that the average level of regional, local wages is weakly associated with the average level of regional traded wages. On an average, local wages are 88 percent of the traded wages. However, there exists no relationship between the percentage of traded employment and average regional wages. In fact, the proportion of traded employment explains just 0.0047 percent of the changes in average regional wage. This implies that average wages in the traded clusters are the main determinants of average wages in local clusters. 5. TRADED CLUSTERS VARY SIGNIFICANTLY IN TERMS OF EMPLOYMENT, AVERAGE WAGES, WAGE GROWTH AND EMPLOYMENT GENERATION Table 12 presents the list of traded clusters along with some select parameters such as wage growth, employment generation, etc. We restrict this analysis to traded clusters due to lack of data for the local clusters. The clusters vary substantially in terms of employment, average wage, wage growth, and employment generation. The largest traded cluster in the Indian economy by employment is “Textile Manufacturing” that employed 1700162 employees in 2014 while the smallest cluster by employment is

Clusters: The Drivers of Competitiveness

â&#x20AC;&#x153;Metal Miningâ&#x20AC;? that employed only 56 people in 2014. Average cluster wages range from Rs.45336 to Rs.592898.

Table 12: Profile of Traded Clusters

Cluster Name Aerospace Vehicles and Defense

Total Employees 2014

CAGR of Employment

Job Growth Rank

Average Wages 2014

CAGR of Average Wages

Wage Growth Rank

11408

0.13

398248

0.09

Agricultural Products, Inputs and Services

157894

-0.01

245075

0.09

Apparel

774067

0.03

127469

0.12

Automotive

874750

0.08

258738

0.11

Biopharmaceuticals

618248

0.10

277723

0.11

Business Services

482

-0.04

253637

0.26

Coal Mining

116

Communications Equipment and Services

123255

95120

0.04

337276

0.05

Construction Products and Services

416004

0.03

216861

0.10

Distribution and Electronic Commerce

47800

0.03

158776

0.12

Downstream Chemical Products

315260

0.02

194080

0.11

Downstream Metal Products

475000

0.07

162065

0.11

Electric Power Generation and Transmission

61519

0.28

294989

0.11

Environmental Services

11036

0.11

120207

0.15

44164

0.06

130073

0.18

1588576

0.02

145497

0.16

Education and Knowledge Creation

Financial Services Fishing and Fishing Products Food Processing and Manufacturing

Clusters: The Drivers of Competitiveness Footwear

252613

0.04

121100

0.15

60352

0.09

212484

0.10

238649

0.03

397489

0.16

Jewelry and Precious Metals

152107

0.02

209679

0.14

Leather and Related Products

78324

0.06

120478

0.12

Lighting and Electrical Equipment

425193

0.06

256869

0.12

25573

0.12

181899

0.14

4157

-0.02

196303

0.14

39274

0.09

184411

0.05

Forestry Furniture Hospitality and Tourism Information Technology and Analytical Instruments Insurance Services

Livestock Processing Marketing, Design, and Publishing Medical Devices Metal Mining

Metalworking Technology

148531

0.06

202919

0.11

Music and Sound Recording

885

0.10

523153

0.07

Nonmetal Mining

10331

0.14

77043

0.15

Oil and Gas Production and Transportation

89103

0.02

592898

0.12

247526

0.01

168531

0.13

Plastics

390770

0.06

174560

0.11

Printing Services

206395

0.09

174954

0.08

Production Technology and Heavy Machinery

548233

-0.03

285298

0.14

Recreational and Small Electric Goods

287777

0.06

203803

0.13

Textile Manufacturing

1700162

0.02

128855

0.13

444652

0.00

45336

0.11

Paper and Packaging

82166

Performing Arts

Tobacco

Clusters: The Drivers of Competitiveness Trailers, Motor Homes, and Appliances

66017

0.10

287610

0.21

Upstream Chemical Products

220413

0.05

267401

0.12

Upstream Metal Manufacturing

986622

0.02

274794

0.10

Video Production and Distribution

2175

-0.03

271698

0.09

Vulcanized and Fired Materials

757367

0.06

123710

0.12

Water Transportation

27986

0.01

293469

0.11

Wood Products

78750

0.03

114869

0.11

Transportation and Logistics

The highest average wages are in clusters such as Oil and Gas Production Transportation, Music and Sound Recording, Aerospace Vehicles and Defence and Communication Equipment and Services. The average wages in high-tech clusters, defined by Aerospace Vehicles and Defence, Biopharmaceuticals, Communication Equipment and Services, Information Technology and Analytical Instruments and Medical Devices, is higher than the average wages in other clusters by Rs.113322.

Clusters: The Drivers of Competitiveness

B. Analyzing the linkages that exist between innovation & cluster strength; business environment and cluster strength and understanding the factors that explain the disparity in the economic performance of regions. 1. REGIONS WITH STRONG CLUSTER PORTFOLIO PERFORM BETTER ON INNOVATION

There is a lot of evidence to suggest that clusters provide an environment conducive to innovation and knowledge creation. This trend is also observed in India. Regions that have a strong cluster portfolio also perform better on innovation (Figure 17). It is backed by the theory that cluster participation eases the process of learning and innovation as firms try to create a shared understanding of the industry and its workings. The relationship of firms within clusters allows them to directly observe other firms and universities. This facilitates the process of innovation and upgrading. Every firm within the district benefits from the idea/innovation and thus the productive benefits are realized at a larger scale. The participant firms not only have deeper insights about the evolving technology which helps in innovation, but they have better knowledge about the availability of new inputs and buyer needs. First, the linkages with the downstream firms and suppliers allow better alignment of activities of the industry as a whole. The complementarities arising from such relationships improves the productivity of all the participant firms. Second, firms within a cluster often are able to more clearly and rapidly discern buyer trends. Just as with current buyer needs, firms in a cluster benefit from the concentration of firms with buyer knowledge and relationships, the juxtaposition of firms in related industries, the concentration of specialized information-generating entities, and buyer sophistication (Porter, 2000). Clusters provide an environment that allows easy flow of ideas and creates opportunities for innovation. Competitiveness of the overall economy is affected through these innovations, based on the potential advantages for firms, along with the flexibility and capacity to act on them. y = 0.1846x + 19.26 RÂ˛ = 0.5248

50.00 45.00

Delhi

Tamil Nadu

Innovation Scores

40.00

Goa

35.00

Kerala

Karnataka

Uttar Pradesh Maharashtra

Gujarat Himachal Pradesh West Bengal Punjab Haryana Andhra Pradesh Jammu & Kashmir Rajasthan Manipur Tripura Madhya Pradesh Uttarakhand Nagaland Assam Odisha Meghalaya Chhattisgarh

30.00

Sikkim

25.00 20.00 15.00

Bihar Jharkhand

10.00 5.00 0.00 0

100

120

140

Cluster Strength

Figure17: Innovation & Cluster Strength 52

Clusters: The Drivers of Competitiveness

2. REGIONS THAT HAVE STRONG BUSINESS ENVIRONMENT, BETTER INFRASTRUCTURE FACILITIES, STRONG LEGAL AND DECISION-MAKING INSTITUTIONS HAVE STRONG PRESENCE OF CLUSTERS.

This depicts the theory established in Section 1 that clusters form a part of the microeconomic factors and they both reflect and amplify the dimensions of competitiveness. â&#x20AC;˘ They are affected by the overall business environment and thrive in regions that have a favorable business landscape. For instance, the presence of research & training institutes, ease of access to capital, physical infrastructure, etc. affect the development of clusters. â&#x20AC;˘ They drive the quality of the business environment through spillover effects and linkages. For instance, the regions where clusters are present are more efficient due to the presence of local suppliers as they enable procurement of superior technology at a comparatively lower price.

140

Uttar Pradesh

120

Maharashtra Tamil Nadu

Cluster Strength

100

Karnataka

Gujarat

Uttarakhand Jharkhand Kerala Andhra Pradesh West Bengal Odisha Goa Rajasthan Haryana

Delhi

Bihar

Madhya Pradesh Assam Chhattisgarh Jammu & Kashmir Manipur Meghalaya Nagaland Tripura Sikkim

Himachal Pradesh Punjab

20 0 0

State Competitiveness Rank

Figure 18: Competitiveness and Cluster Strength

3. ECONOMIC SUCCESS OR FAILURE IS NOT DEPENDENT ON THE STARTING LEVEL OF WAGES AND EMPLOYMENT

Another critical attribute of the economic performance of regions is the employment growth over time, which is depicted in Figure 19. It can be seen that the CAGR of employment has varied from 20 percent (Meghalaya) to -2 percent (Delhi) between 2009 and 2014. It also depicts a weak negative correlation of employment growth to the initial employment of the state. Therefore, growth in employment hardly depends on how their historic levels of employment. The same can be said about wages of the state (Figure 20). These findings are desirable in the sense that states having high employment levels and high-paying jobs do not have any evident advantage over other states. There also seems to lack of a strong relationship between average wage growth and average employment growth across states. In fact, states with high employment growth have shown slightly lower growth in wages. More worryingly, most of the states have shown about 5 percent 53

Clusters: The Drivers of Competitiveness

CAGR of wages but an employment growth of less than 5 percent. This is indicative of the trend of jobless growth in India where fast-paced growth has taken place without a commensurate rise in jobs across the country.

CAGR of Employment, 2009-2014

25 20 15 10 5 0 -5

Meghalaya Manipur Assam Sikkim Odisha Himachal Pradesh Uttarakhand Dadra & Nagar Haveli Andaman & Nicobar y = -3E-06x + 6.3364 Bihar Islands RÂ˛ = 0.0736 Jharkhand Madhya Pradesh Rajasthan Maharashtra Nagaland Uttar Pradesh Gujarat Jammu & Kashmir West Bengal Andhra Pradesh Punjab Goa Pondicherry Haryana Chhattisgarh Kerala 0 500000 1000000 1500000 Delhi Karnataka Tripura

Daman & Diu

Tamil Nadu 2000000

Starting Employment, 2009

Figure 19: Starting Employment vs CAGR of Employment

CAGR of Average Wage (%), 2009-2014

Chandigarh

Nagaland

Kerala GoaDelhi Manipur Rajasthan Punjab Uttar Pradesh Himachal Pradesh Daman & Diu Madhya Pradesh Meghalaya Jammu & Kashmir Assam West Bengal Karnataka Bihar Chhattisgarh Tripura Odisha Jharkhand Andaman & Nicobar Pondicherry Islands Dadra & Nagar Haveli

y = 2E-06x + 10.631 RÂ˛ = 0.0402

Haryana

Andhra Pradesh Gujarat

Tamil Nadu Maharashtra

0 500000 Uttarakhand

0 -5

-10

1000000

1500000

2000000

Sikkim

Starting Employment, 2009

Figure 20: Starting Employment vs CAGR of Average Wages 54

Clusters: The Drivers of Competitiveness

It is a common assertion in economic development circles that large regions (by employment) that support diverse economies will be advantaged. (Porter, 2003). However, our results suggest that the employment situation of a state hardly plays a significant role in determining its wages and wage growth over time in India. Figure 21 shows that a weak positive correlation exists between the average wages and employment growth of a state. Therefore, larger states based on employment size have no clear advantage over smaller ones in wage levels, as is usually the case. Wage growth also does not depend on the initial employment levels of a state. There exists a weak positive correlation between wage growth between 2009 and 2014 and the initial employment levels of 2009. Therefore, the data shows that the size of the state (by employment) has no impact on the wages of that region.

CAGR of Employment, 2009-2014

Meghalaya Manipur

Sikkim

Gujarat Bihar Nagaland

Tripura 0 0 -5

50000

Himachal Pradesh Uttarakhand Andaman Dadra && Nicobar Nagar Haveli Islands

y = 8E-06x + 4.3769 RÂ˛ = 0.0072

Andhra Pradesh Rajasthan Maharashtra Assam Pradesh Jammu & Uttar Kashmir Odisha West Bengal Chandigarh Jharkhand Punjab Goa Karnataka Pondicherry Haryana Chhattisgarh 150000 200000 250000 Kerala100000 Delhi

Tamil Nadu

300000

Madhya Pradesh Average Wages, 2009

Figure 21. Employment Growth vs Initial Average Wage by States, 2009-14

4. INNOVATION WITHIN INDIAN STATES IS NOT SIGNIFICANT ENOUGH TO BE A DETERMINING FORCE FOR WAGES PAID TO EMPLOYEES.

All the measures considered until now provide a basic idea of the regional economic performance across India. A more defining aspect of the same is the number of innovative activities taking place in a region. Higher innovative capabilities provide a region with a considerable competitive advantage over other regions. For the same, we look at two measures. One, patenting activity and second, the state innovation score developed in Chapter 2.2. For patents, we have to exclude Delhi from the dataset as it was an outlier having received nine times more patent applications than the next highest state. Figure 22 shows that the next three 55

Clusters: The Drivers of Competitiveness

Patents pwe 100000 employees

states with the highest patents per 100000 employees are Karnataka, Maharashtra, and Kerala. It is again surprising that Jharkhand and Meghalaya have made it into the top 10 in this list. The fact that patenting intensity is high in these states bodes well with their respective long-term prospects. 200 180 160 140 120 100 80 60 40 20 0

Figure 22: Patents by State

Further, it seems to be the case that larger states by employment size show higher innovative tendencies. A moderately strong relationship between average patents by the state and its employment levels. It might be so because of network effects between employees which might enhance their innovative capabilities. This underscores the importance of locations in determining regional performance yet again. However, innovation still does not have a clear impact on average wages of regions. A lack of a clear relationship between patenting activity and wages is observed. This is also observed when we look at the relationship between State Innovation Scores and average wages.

In a nutshell, Indian states display widespread contrasts in terms of their average wages, wage growth, and employment growth. This variation is neither explained by the starting level of growth nor by the innovative capacity of the state.

We, therefore, move towards the regionâ&#x20AC;&#x2122;s competitiveness and cluster strength to look for an explanation for the regional economic disparity. 5. POSITIVE RELATIONSHIP IS OBSERVED BETWEEN CLUSTER STRENGTH AND ECONOMIC DEVELOPMENT

A positive relationship is observed between regional competitiveness and economic development as well as cluster strength and economic development. This justifies the theory that clusters enhance innovation and productivity of regions, ultimately improving the standard of living of citizens.

Clusters: The Drivers of Competitiveness

140 UTTAR PRADESH

120

MAHARASHTRA 100

TAMIL NADU KARNATAKA

GUJARAT 60

WEST BENGAL

DELHI

KERALA UTTARAKHAND ANDHRA PRADESH RAJASTHAN HIMACHAL PRADESH HARYANA

40 BIHAR 20

MADHYA ODISHA PRADESH JHARKHAND ASSAM PUNJAB CHHATTISGARH PUDUCHERRY JAMMU AND KASHMIR MEGHALAYA MANIPUR NAGALAND TRIPURA

0 0

50000

100000

150000

200000

GOA

CHANDIGARH SIKKIM 250000

300000

350000

400000

Figure 23: Economic Development and Cluster Strength Clusters confer advantages for the firms as well as the economy that helps them in persisting over time. These advantages, which we call building blocks of clusters, are listed below: A. SKILLING The prime benefit that geographically proximate firms acquire over isolated firms is labor market pooling. A cluster attracts people with required skillset as workers find it advantageous to be located in a place with a large number of firms in order to minimize their risk of layoffs. This was highlighted extensively in Marshall’s work: “Employers are apt to resort to any place where they are likely to find a good choice of workers with the special skill which they require; while men seeking employment naturally go to places where there are many employers who need such skill as theirs and where therefore it is likely to find a good market. The owner of an isolated factory, even if he has access to a plentiful supply of general labor, is often put to great shifts for want of some specially skilled labor; and a skilled workman, when thrown out of employment in it, has no easy refuge.” Therefore, clusters create a strong market for specialized skilled labor that benefits both the firms as well as the job seekers. B. SUPPLIER SOPHISTICATION The firms that operate within clusters become more efficient than the isolated firms due to the presence of local suppliers as they enable procurement of superior technology at a comparatively lower price. Even if the suppliers are not in attendance within the region, firms have strong alliances with outside entities. These vertical linkages help with importing specialized inputs such as machinery from distant locations. The connections not only help the firms but also the suppliers as they get a ready market for their products. 57

Clusters: The Drivers of Competitiveness

Private investments in cluster-specific goods are common by suppliers because the benefits of such investments will be perceived by all the firms within the clusters. They are enabled through industry associations or other institutes for collaborations. The benefits to the firms operating in clusters due to supplier sophistication have been well documented in the literature. A leading characteristic of Italian industrial districts has been the successive specialization of firms in different steps in the production process (Bianchi, Miller, and Bertini 1997). Similarly, Klier (1999) and other researchers have documented the location patterns of first-tier suppliers to the automobile industry. Apart from private investments, the presence of clusters makes otherwise costly goods into public or quasi-public goods. C. SOCIAL INTERACTIONS The role played by social interactions and culture has been noted by observers of clusters. Granovetter (1985) argued that it is often difficult to describe economic systems separate from social systems. This happens because people are not simply workers or managers; they are also consumers, citizens, church-goers, kin, and community members (Institution, 2006). The social relationships and the culture of the region impact the initiation as well as the development of clusters. Since ages, if the people residing in a region have carried out no entrepreneurial activity, they might not be acceptable towards it. This behavior hinders the development of clusters in such regions. For instance, Feldman (2004) attributes the relative weakness of Baltimore in developing a biotechnology cluster (despite Johns Hopkins being the nation's leading medical research institution) to a persistent culture unwelcoming of entrepreneurship. The local culture plays an important role as it shapes the economic decisions of local actors such as risk-taking, information sharing, etc., the decisions that are important for the growth of clusters. Saxenian's (1994) comparative analysis of Route 128 in Boston and Silicon Valley highlights fundamental cultural differences between the two areas and attributes Californiaâ&#x20AC;&#x2122;s superior economic performance to its openness and tolerance of failure. D. BUSINESS FORMATION It has been suggested by many scholars that clusters promote new businesses and entrepreneurial activity. For instance, Rosenthal and Strange (2005) studied the geography of new firm formation in major industry groups in the New York metropolitan area and found that localization, measured by employment in a new establishmentâ&#x20AC;&#x2122;s own industry, is positively associated with the rate of new firm formation and associated employment growth. This happens mainly because of two reasons. First, due to the easy flow of information, the information about new opportunities within clusters is readily available. The mere existence of a cluster itself is a signal to the firms as the regions with the presence of clusters are highly innovative. Second, the barriers to entry are low compared to an isolated location. This happens as the basic facilities such as a skilled labor pool, public infrastructure, raw material suppliers, etc. are already in place. A significant local market is also present. The presence of an established cluster not only lowers the barriers to entry to a location facing outside firms but also reduces the perceived risk. Therefore, already established companies that are based on other locations are also driven to these locations. The accumulation of industrial space that leads to new business formation generates employment opportunities. This, in turn, leads to the development of the region as more and more people migrate to such regions in search of jobs.

Clusters: The Drivers of Competitiveness

6. EMPLOYMENT GROWTH DOES NOT SHOW A RELATIONSHIP WITH PRESENCE OF HIGH-TECH CLUSTERS

The overall impact of the high-tech clusters on the regional economy is very small. We analyzed the relationship between regional high-tech share and average wages. The proportion of high-tech employment explains 6 percent of the variation in average wages and 7.6 percent of the variation in local average wages. Also, there does not exist a significant relationship between the high-tech clusters and employment growth (Figure 24).

Employment in High-Tech Clusters vs Growth in Employment 250000

Employment in High-tech Clusters

200000

Tamil Nadu

Gujarat

150000

Dadra & Nagar Haveli Karnataka

100000

Uttarakhand

Andhra Pradesh

Jharkhand

Himachal Pradesh Rajasthan

Delhi

50000

Uttar Pradesh

Haryana Madhya Pradesh Kerala Goa West Bengal Punjab Daman & Diu Pondicherry Assam Chandigarh 0 Chhattisgarh Tripura Nagaland -5

y = -328.64x + 29522 RÂ˛ = 0.0015

Maharashtra

Odisha

Jammu & Kashmir Sikkim Andaman & Nicobar Bihar Islands 15 10

ManipurMeghalaya 20

CAGR of Total Employees

Figure 24: Growth in Employment vs. Employment in High-tech Clusters For instance, the job growth rank of the Communication Equipment and Services is 22, and that of Information Technology and Analytical Instruments is 27. This helps us to conclude that rather than merely focusing on high-tech clusters regions, there is a need to develop the clusters that are present in their region. So, the next step is to identify the cluster portfolio of regions and their strength.

Clusters: The Drivers of Competitiveness

C. How can policymakers make use of the cluster mapping data to enhance competitiveness and economic development? 1. Regional Policymakers The Cluster Mapping analysis conducted in Chapter 2.1provides profiles of each state that can help the regional policymakers in understanding the clusters that are present in the region, analyze their performance, know about the emerging clusters and based on these they can take steps for enhancing competitiveness. Here we depict the analysis for Maharashtra. Maharashtra, the third largest state regarding area, is home to 9.28 percent of the total Indian population. Itâ&#x20AC;&#x2122;s the richest state in terms of Gross Domestic Product, with SGDP equal to Norway. The state is ranked at 3rd position in the State Competitiveness Report by Institute for Competitiveness, India. Figure 25 depicts Diamond for the state of Maharashtra. The main strengths of the state are in its demand conditions and context for strategy and rivalry.

Factor Conditions + Superior Infrastructure Facilities + Presence of research institutes + Social Infrastructure - Long administrative processes - Problems in electricity

Related and Supporting Industries + Institutional Support + presence of Special Economic Zones

Context for Firm Strategy and Rivalry + High FDI inflows + High manufacturing output reflecting string industrial base

Demand Conditions + Population with high per capita income that can drive consumption + High rates of urbanization - High inequality

Figure 25: Diamond Model for Maharashtra

Clusters: The Drivers of Competitiveness

Cluster Portfolio Maharashtra has one of the strongest cluster portfolios among Indian states.

Figure 26: Cluster Portfolio of Maharashtra11 Cluster Portfolio of all major states is given in Appendix 2. For cluster portfolio of all the states visit https://clustermapping.in/regional-cluster-portfolios/ 11

Clusters: The Drivers of Competitiveness

Table 13: Cluster Portfolio State Name

Total Stars

One Star Clusters

Two Star Clusters

Three Star Clusters

Four Star Clusters

Maharashtra

109

Top 3 clusters by LQ

• • •

Video Production and Distribution Music and Sound Recording Local Commercial Services

There are four 4-star clusters i.e. they are in the top twenty percent in the country by specialization, size, productivity and dynamism. There are fourteen 3-star clusters and twenty-two 2-star clusters. Policymakers can focus on these 3-star and 2-star clusters, identify the challenges that the firms within these clusters are facing and work towards removing them. For instance, take the case of automotive cluster. Automotive is a 3-star cluster in Maharashtra. The table below shows some famous automotive cluster belts in Maharashtra. Table 14: Cluster Belts Auto Parts

Supply of Auto Parts

Assembly

Marketing

Research

Pune

Nagpur

Mumbai

Chakan

Kolhapur

Nashik

Nashik, Chakan

SWOT Analysis of the cluster reveals the following: Table 15: SWOT Analysis

Strength

•

Presence of cluster belts in auto parts, assembly and marketing.

Weakness

• •

Long administrative processes creates problems for firms Low labor productivity

Opportunity

• •

Government has taken steps to increase the FDI Increase in living standards

Threats

•

Indian firms face competition from other low cost countries

Clusters: The Drivers of Competitiveness

Cluster Map for Automotive Cluster in Maharashtra:

Figure 27: Cluster Map

Based on the state diamond, SWOT analysis and cluster map, following recommendations can be provided to the policymakers: 1. The two IFCs i.e. Society of Indian Automobile Manufacturers (SIAM) and Auto Component Manufacturers Association of India (ACMA) can contribute better by building institutional linkages between industry and academic institutions for research and development in cost saving and green technologies. 2. The timing of administrative processes should be reduced. 3. Problem of ensuring consistent electric supply should be addressed soon. 4. Ease of doing business should be improved.

Clusters: The Drivers of Competitiveness

2. National Policymakers National policymakers can identify a cluster that they want to work on and then analyze its presence across regions, compare its performance overtime and study policies in the regions where the cluster has shown growth. Here we analyze Automotive Cluster in India. The composition of the Automotive Cluster (By Employment): The automotive cluster comprises of manufacturers of cars, motor vehicles, commercial vehicles, parts & accessories of vehicles, etc. It is observed that in 2014 more than 50 percent of the people employed in the Automotive Cluster were working in one industry, i.e., manufacture of diverse parts and accessories for motor vehicles such as brakes, steering wheels, etc.

Figure 28: Composition of Automotive Cluster

Clusters: The Drivers of Competitiveness

Basic Facts: The automotive cluster in India employs 874750 people, which makes it one of the highest employment providing traded cluster in India (6.7 % of the total traded employment). Table 16: Automotive Cluster Profile

Cluster Name

Total Employees 2014

CAGR of Employment

Job Growth Rank

Average Wages 2014

CAGR of Average Wages

Wage Growth Rank

Automotive

874750

0.08

258738

0.11

Evolution of the Automotive Cluster (Employment in 2009 is 100): It has grown by more than 40 percent since 2009 while the growth in overall traded employment has been just 20 percent. Evolution of the Automotive Cluster 160.00 140.00 120.00 100.00 80.00 60.00 40.00 20.00 0.00 2008

2009

2010

2011

Automotive

Traded

2012

2013

2014

2015

Overall Economy

Figure 29: Evolution of Automotive Cluster

Leading Regions (by cluster strength based on four-star methodology):

Clusters: The Drivers of Competitiveness

Figure 30: Top Regions, Automotive There exist five three stars automotive clusters in India in Haryana, Jharkhand, Maharashtra, Karnataka, and Uttarakhand. Apart from three-star clusters, there is one two-star and seven one-star Automotive Clusters in India.

Figure 31: Performance of Automotive Cluster 66

Clusters: The Drivers of Competitiveness

Employment and Growth

Figure 32: States by Cluster Share If we look at the size of the cluster (by employment), the leading regions are Tamil Nadu, Maharashtra, Haryana, Karnataka, Uttarakhand, and Gujarat. Tamil Nadu and Maharashtra together have a cluster share of nearly 45 percent. But the growth of automotive cluster is the highest in Meghalaya, Uttarakhand, Karnataka.

Clusters: The Drivers of Competitiveness

D. Policy Implications of the Results 1. WIDEN THE INNOVATIVE CAPACITY TO ALL THE CLUSTERS

Majority of the employment is concentrated within the clusters that are not considered to be hightech. Therefore, to enhance regional prosperity, innovative capacity must be built in many clusters. 2. DISTINCTIVE STRATEGY FOR ALL THE REGIONS

Indian states display widespread contrasts in terms of their average wages, wage growth, and employment growth. This variation is neither explained by the starting level of growth nor by the innovative capacity of the state. National economic performance is only an aggregate of these varying regional outcomes. Therefore, no single policy will work for all regions. These results reveal the benefits of decentralized economic policy and how national policy is necessary but not always sufficient. India has a long way to climb up the developmental ladder, and the states seem to be its best bet. 3. BUILDING ON ESTABLISHED CLUSTERS

The focus of the government should be on upgrading the already established and emerging clusters rather than seeding new clusters. This is because clusters are formed automatically based on the comparative strengths of a region. Investing in such a cluster that has already passed the test of early development stages will yield better results as it is known beforehand that the basic conditions for economic success are present. The first step in cluster upgrading is to recognize that a cluster exists and then the next steps are to remove obstacles, eliminate inefficiencies that are impeding the growth of a cluster. Constraints include human resource, infrastructure, and regulatory constraints. Some of these can be addressed to varying degrees by private initiatives. Other constraints, however, are the result of government policies and institutions and must be addressed by the government (Porter, 2000). For instance, The Automotive Cluster is concentrated in Madhya Pradesh, but it is neither highly productive nor is growing at a fast rate compared to other regions in the country. The government should focus on identifying the challenges faced by the companies in Madhya Pradesh as there are locational benefits due to which cluster emerged in that region. 4. UPGRADING NOT ABANDONING

Clusters offer opportunities for regions to grow and enhance their productivity but sometimes it is observed that some clusters are adding no value to national productivity. Here, it should be kept in mind that not all clusters contribute directly to national productivity, these clusters might be providing support to other industries. Therefore, efforts should be made to upgrade them instead of abandoning. Efforts to upgrade clusters might have to be sequenced for practical reasons, but the goal should be to eventually encompass all of them. Upgrading in some clusters will reduce employment as firms move to more productive activities, but market forces--and not government decisions--should determine which clusters will succeed or fail (Porter, 2000). 5. FOCUS ON OVERALL ENVIRONMENT RATHER THAN HELPING SPECIFIC FIRMS OR INDUSTRIES

As discussed above, sometimes governments use policy instruments such as subsidies and grants to develop clusters and enhance the competitiveness of certain industries. These approaches do not align with the modern competition. Setting policies to benefit individual firms distorts markets and uses government resources inefficiently. Focusing policy at the industry level presumes that some industries are better than others and runs 68

Clusters: The Drivers of Competitiveness

grave risks of distorting or limiting competition (Porter, 2000). For instance, the earlier cluster policies mainly focused on the small and medium enterprises, development of handloom clusters, etc. However, the correct approach is to identify the strengths of every region and then focus on providing the right environment that will enhance the productivity.

Clusters: The Drivers of Competitiveness

BIBLIOGRAPHY -

(Kapoor, Bryden, Ketels, & Kapoor, 2018)

Amin, A. (1989). Flexible specialisation and small firms in Italy: myths and realities. Antipode, 21(1), 13-34.

Becattini, G. (4). The Marshallian industrial. Industrial districts, 37.

Caldari, K., & Belussi, F. (March 2009). At the origin of the industrial district: Alfred Marshall and the Cambridge school. Cambridge Journal of Economics, 335-355.

Cairncross, F. (1997). The death of distance: How the communications revolution will change our lives.

Cortright, J. (2006). Making Sense of Clusters. The Brookings Institution Metropolitan Policy Program.

Delgado, M., Ketels, C., Porter, M. E., & Stern, S. (2012). The determinants of national competitiveness (No. w18249). National Bureau of Economic Research.

Friedman, T. L. (2005). It's a Flat World, After All. Retrieved from The New York Times: http://query.nytimes.com/gst/fullpage.html?res=9F06E7D8153FF930A35757C0A9639C8B63&pag ewanted=all

Hoover, E. M., &Giarratani, F. (1971). An introduction to regional economics.

Ketels, C., Solvell, O., & Lindqvist, G. (2009). EU Cluster Mapping and Strengthening Clusters in Europe.

Ketels, C., & Protsiv, S. (2014). European Cluster Panorama.

Ketels, C., & Protsiv, S. (2016). European Cluster Panorama.

Ketels, C., & Protsiv, S. (2017). Priority Sector Report: Circular Economy.

Ketels, C. (2017). Cluster Mapping as a Tool for Development. Labour Bureau. (2012-13). Report on Absenteeism, Labour Turnover, Employment & Labour Cost. Chandigarh: Ministry of Labour & Employment.

Marshall, A. (1920). Principles of Economics. Cosimo Classics; 8th ed. edition.

Morgan, K. (2001). The exaggerated death of geography: localised learning, innovation and uneven development. Technology, 20, 23.

O'brien, R. (1992). Global financial integration: The end of geography. Royal Institute of International Affairs.

Porter, M. E. (2000). Location, competition, and economic development: Local clusters in a global economy. Economic development quarterly, 14(1), 15-34.

Porter, M.E. (2001). Clusters of Innovation: Regional Foundations of U.S. Competitiveness

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Porter, M. E. (2004). Location, Competition and Economic Development: Local Clusters in a Global Economy. Economic Development Quarterly.

Singh, A. (2010). Clusters in India. - (2009). Clusters for Competitiveness. International Trade Department.

Clusters: The Drivers of Competitiveness

APPENDIX

Clusters: The Drivers of Competitiveness

APPENDIX 1: DATA SOURCES Analysis

Data Source

Cluster Mapping

Annual Survey of Industries

Regional Competitiveness

Telecom Regulatory Authority of India Ministry of Statistics & Program Implementation NITI Statistics All India Survey of Higher Education Department of Industrial Policy & Promotion Annual Survey of Industries National Crime Records Bureau

Economic Performance of Regions

Annual Survey of Industries & National Sample Survey Organisation

Innovative Capacity

Ministry of Statistics & Program Implementation

Clusters: The Drivers of Competitiveness

APPENDIX 2: CLUSTER PORTFOLIOS

Clusters: The Drivers of Competitiveness

Institute for Competitiveness, India is the Indian knot in the global network of the Institute for Strategy and Competitiveness at Harvard Business School. Institute for Competitiveness, India is an international initiative centered in India, dedicated to enlarging and purposeful disseminating of the body of research and knowledge on competition and strategy, as pioneered over the last 25 years by Professor Michael Porter of the Institute for Strategy and Competitiveness at Harvard Business School. Institute for Competitiveness, India conducts & supports indigenous research; offers academic & executive courses; provides advisory services to the Corporate & the Governments and organizes events. The institute studies competition and its implications for company strategy; the competitiveness of nations, regions & cities and thus generate guidelines for businesses and those in governance; and suggests & provides solutions for socio-economic problems.

The Institute for Competitiveness U24/8 DLF Phase 3 Gurgaon 122 002 Haryana, India

Phone: +91 124 437 6676 Email:info@competitiveness.in www.competitiveness.in