INDIA CLUSTER MAPPING: DISCUSSION PAPER

CLUSTER MAPPING INDIA Discussion Paper (with inputs from Amit Kapoor, Christian Ketels, Manisha Kapoor, Rich Bryden and Vandana Kumar)

Institute for Competitiveness U 24/8 DLF Phase 3 Gurgaon, Haryana 122002

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PREFACE

In 2017 Institute for Competitiveness, India joined hands with Institute for Strategy and Competitiveness and Department of Industrial Policy and Promotion, India to lay the foundation for their initiative Cluster Mapping India. 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 analysing 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 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 DIPP. The team conducted presentations of their work at DIPP. The Institute is thankful to everyone who has contributed to this effort. 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 Prof. Michael E Porter for the intellectual framework. Thanks to Rich Bryden and Christian Ketels whose expertise has guided us in our journey, Department of Industrial Policy and Promotion for their guidance and suggestions about national priorities.

Many thanks to the team at Institute for Strategy and Competitiveness for their strategic inputs and insights that helped us.

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1. 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 globalised world location holds no relevance. 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 globalization manufacturing was being relocated beyond borders and information was instantaneously beamed across the world. Globalization was seen as a liberating force, ushering in a ‘borderless world.’ O’Brien (1992) referred to this as ‘the end of geography.’ The argument was mainly based on two phenomena; tradability, which separates the provision of services from its point of consumption; and codification, which reduces knowledge to a universally accessible digital form of information (Morgan, 2004). 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. Similarly, due to codification, it became possible to transfer knowledge across long distances at low cost.

In a similar vein, Cairncross (1997) announced the ‘death of distance,’ and Friedman (2005) said that ‘the world is flattening.’

Exhibit 1: Some well-known clusters But despite the twin processes of globalization and digitalization, the world, whether developed or developing, is home to several clusters, i.e. geographic concentrations of interconnected companies (see Exhibit 1). 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 utilise IT professionals highly CLUSTER MAPPING INDIA | 3

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.

And the presence of these clusters makes the “death of geography” argument look far-fetched. Even as old reasons for clustering have diminished in importance with globalization, new influences of clusters1 on competition have taken on growing importance in an increasingly complex, knowledgebased, and dynamic economy (Porter, 2004).

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. This is the reason that today they are being studied by a variety of scholars from different fields including economists, social scientists, and strategists, and also by a growing number of business practitioners and policymakers (Saric, 2011). As a result, the knowledge on the capacity of clusters to promote prosperity and the role of industrial policy in creating clusters has increased rapidly.

This proliferation of studies has led to a multiplicity of definitions and schools of thought, each of which interprets the role of clusters differently. It thus becomes difficult to delineate what constitutes a cluster and how cluster-based economic development should be approached. Also, most of these studies are based on developed economies. This paper tries to fill in the gap in the literature by conducting a systematic statistical analysis of clusters at the regional level in India that can help to frame clusterbased development policies, which has been lacking at the national level in India.

The cluster-based development approach was first endorsed by Abid Hussain Committee set up by Ministry of Small Scale Industry in its report in 1997. But it was only in 2005, eight years after it was first recommended that the Government of India announced a package where cluster development was to be used as a tool to make SMEs globally competitive. The cluster development approach made its way in the Industrial Policy of State Governments of Gujarat, Madhya Pradesh, Andhra Pradesh, Kerala, etc. P. Chidambaram, then Finance Minister said, “The Cluster Development model can be usefully adopted not only to promote manufacturing but also to renew industrial towns and build new industrial townships. The model is now being implemented, in one form or other, in nine sectors falling under different Ministries. The sectors include Khadi and village industries, handlooms, handicrafts, textiles, agricultural products and medicinal plants. It would be advantageous to empower a group to oversee cluster development and monitor progress. Hence, the Prime Minister had decided to constitute an ‘Empowered Group of Ministers’ to lay down a policy for cluster development and oversee the implementation.”

In the 11th five-year plan (2007-2012) emphasis was laid on the development of clusters for enhancing the productivity of MSMEs. Various schemes were launched by the state as well as the central government to implement the cluster development program. For instance, Scheme for Integrated Textile Parks (SITP), Industrial Infrastructure Upgradation Scheme (IIUS) focusing on infrastructure; Craft Village Scheme (Shilpgram Yojna) focusing on employment; Scheme for Assistance to Cluster Development, NABARD Cluster Development Programme focusing on competitiveness.

All these policies were focused only towards MSMEs. But now Department of Industrial Policy and Promotion is focusing on the overall competitiveness of the nation as it is one of the main impediments in India’s growth (see Exhibit 2).

1

The reasons are discussed in Chapter 2.

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Exhibit 2: Strengths and Challenges Source: Department of Industrial Policy and Promotion So, with the vision of enhancing industrial competitiveness DIPP has launched the National Plan for Manufacturing Clusters. This report will help in the policy making process by identifying traded and local clusters present in India, analysing the performance of clusters from 1999 till date, and examining the role of clusters in the Indian economy.

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2. LITERATURE REVIEW This section analyses the historical evolution of the different school of thoughts on 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. Yet surprisingly, Marshall observed that the small businesses were flourishing. This caught his interest and he analysed how the small firms in certain localities were able to successfully compete with large corporations. 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 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 neighbourhood, 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 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 in manufacturing districts in their neighbourhood rather than in the towns themselves (Marshall, 1920). Third, patronage of court. The skilled artisans assembled at places where the wealthy and affluent demanded high-quality 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’ localisation, if it lasts long enough, becomes a ‘more compound’ localisation, that is, it is transformed into an industrial district (Caldari & Belussi, March 2009). It happens because the passage of time allows these localised economies to attain certain advantages of production at a large scale.

• Skilling: Industrial districts offer a constant market for skill that lead to benefits for both employers and employees by minimising 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.

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•

Specialized Machinery: These areas are also beneficial as they enable small manufacturers to make use of expensive specialised machinery even though the individual capital employed is very small.

The growth of industrial districts also favours the growth of supplementary industries. Specialised manufactures 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.

Industrial districts not only help the firms but also inflict some external diseconomies. If the industrial district specialises 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 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 labour 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 organisation that can compete with vertically integrated firms.

NEW MARSHALLIAN DISTRICTS In 1970s small manufacturers2 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 characterised 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 labour 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 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 minimising risk.

2

Manufacturers employing less than ten employees.

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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 to the firms of all sizes.

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 organisation, CEOs, consultants, and the scientific community alike.

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

Prof. Michael E Porter undertook a study of 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.

Clusters are defined by Porter 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 neighboring countries.”

Clusters encompass an array of linked industries and other entities important to competition. They include, for example, suppliers of specialized inputs such as components, machinery, and services as well as providers of specialized infrastructure. Clusters also often extend downstream to channels or customers and laterally to manufacturers of complementary products or companies related by skills, technologies, or common inputs. Many clusters include governmental and other institutions (e.g., universities, think tanks, vocational training providers, standards-setting agencies, trade associations) that provide specialized training, education, information, research, and technical support. Many clusters include trade associations and other collective bodies involving cluster members.

According to Porter (2000), clusters affect competition in three broad ways: - By increasing the current (static) productivity of constituent firms or industries - By increasing the capacity of cluster participants for innovation and productivity growth - By stimulating new business formation that supports innovation and expands the cluster The productivity within clusters is 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 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 CLUSTER MAPPING INDIA | 8

-

by utilizing complementarities of local innovation partners by reducing transaction costs of innovation

Clusters lead to 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 environment rich in social capital

CLUSTER BASED ECONOMIC DEVELOPMENT THE CASE FOR CLUSTER POLICY Market failures are often used by economists to justify the government interventions. According to Ketels (2009) the local externalities due to which clusters are created lead to a number of market failures such as: - Coordination Failures: Individual companies do not take into account the impact of their actions on others.

- Information Asymmetries: Even if the companies made to co-operate, the knowledge necessary to make social decisions is not available to every participant. For instance, if a company is investing in research and development activities it is important for others to have that knowledge so that complementary investments can be made.

Where cluster policy addresses market failures, it does not reduce global welfare. Under some assumptions, the free competition between rational governments in supporting clusters even leads to the best possible outcome, not a race to the bottom (Ketels, 2009).

APPROACHES TO CLUSTER POLICY There are two opposing approaches to cluster policy that differ based on type of government interventions.

1. The proponents of first approach believe that agglomerations lead to competitiveness, therefore clusters are at the heart of the policy debate. With agglomeration the ultimate goal, efforts to attract companies through incentives — from tax rebates to free infrastructure — naturally come to the forefront of the policy debate (Ketels, 2009). This also implies that government intervention has to be in early phase of cluster emergence.

2. The second approach views clusters as a tool or a policy instrument that can be used to achieve the ultimate goal of enhancing competitiveness. The proponents of this believe that fundamental conditions for economic success are present and active collaboration can become a” turbo” for the use of strengths already in place. Here, government acts as an enabler and focuses on improving the collaboration between different stakeholders.

Many studies have revealed that the policies under the first approach are more likely to fail. For instance, an in-depth evaluation of cluster competitiveness initiatives by USAID showed that preselection of clusters in most developing countries failed because it tended to create a poor psychological contract whereby cluster leaders believed they were receiving a mandated entitlement (International Trade Department, 2009).

Therefore, cluster based economic development should start with cluster mapping projects.

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3. THE ECONOMIC PERFORMANCE OF REGIONS The starting point for a regional development policy is to analyse the regional economic performance.

The starting point for a regional development policy is to analyse the regional economic performance This performance should be measured on multiple parameters that can bring out important insights not only about the current level of prosperity but also about advancements in living standards, change in the innovative capacity 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.

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.

Therefore, we start by exploring the basic facts about the Indian economy.3 The primary aim of this is not to test any hypothesis but to gain a detailed understanding about the regional differences that prevail across India and recognise the factors that explain these variations. This section of the report is inspired by a similar study by Michael E. Porter, The Economic Performance of Regions (Porter, 2003).

PERFORMANCE MEASURES UTILIZED We use average wages, wage growth, employment and employment growth to gain knowledge about the regional economic performance.

Innovation capacity is analysed by using two measures, i.e. patents and State Innovation Score. We use the State Innovation Scores by Institute for Competitiveness which is comprehensive measure of regional innovation based on fifty-four indicators. The study looks at a broad definition of innovation that captures enablers of innovation such as human capital, investments, business environment etc and performers such as research output and knowledge diffusion.

3

Economic Performance

Innovation

Average Wages

Human Capital

Wage Growth

Investments

Unemployment

Knowledge Diffusion

Growth Rate of employment

Research Output

The details about the data used are given in Appendix 1.

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FINDINGS Regions vary significantly in terms of average wages Exhibit 3 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.

300000.00 250000.00 200000.00 150000.00 100000.00 50000.00 0.00

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…

Jammu & Kashmir Punjab Dadra & Nagar Haveli Assam Bihar Manipur Nagaland Tripura

Average Wage, 2014

350000.00

Exhibit 3: 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).

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 0.00

Food…

Textile…

Construction…

Upstream…

Agricultural…

Biopharmace…

Fishing and…

Apparel Paper and…

Vulcanized…

Tobacco Plastics Upstream…

Production…

Printing…

Downstream…

Electric…

Communicati…

Footwear Oil and Gas… Lighting and…

Automotive Downstream…

Water…

Distribution…

Recreational…

Wood Products Information…

Metalworking…

Jewelry and…

Leather and…

Environmenta…

Furniture Aerospace…

Livestock…

Nonmetal…

Medical…

Marketing,…

Trailers,…

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

Total Employees

Average Wages (In 10)

Exhibit 4: Employment and Wages – Andhra Pradesh

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The third surprising finding from Exhibit 3 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 reveal 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. Exhibit 4 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.

Convergence between the states is on the rise

40 35 30 25 20

National Average = 17.17%

15 10 5

Pondicherry

Sikkim

Uttarakhand

Jharkhand

Chhattisgarh

Tripura

Odisha

Delhi

West Bengal

Dadra & Nagar Haveli

Kerala

Punjab

Maharashtra

Madhya Pradesh

Gujarat

Jammu & Kashmir

Assam

Karnataka

Uttar Pradesh

Andhra Pradesh

Goa

Chandigarh

Tamil Nadu

Andaman & Nicobar Islands

Haryana

Daman & Diu

Bihar

Rajasthan

Nagaland

Himachal Pradesh

Manipur

0

Meghalaya

CAGR of Wages (%), 2009-14

The average state also experienced an annual wage growth of 17.17 percent between 2009 and 2014 as seen in Exhibit 5. 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.

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.

Exhibit 5: CAGR of Wages by States

The reduction in regional disparity is clearly shown in Exhibit 6, 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 higher average wage to begin with grew slower than the ones with 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.

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CAGR of Average Wage, 2009-2014

25 20

Nagaland

Kerala

Manipur

15 10

Tripura

Bihar

5

Haryana Delhi Goa Himachal Pradesh Uttar Pradesh Maharashtra West Bengal Odisha Jharkhandy = -6E-05x + 17.91 Pondicherry R² = 0.4097 Dadra & Nagar Haveli

0 0

50000

100000

150000

Uttarakhand 200000

250000

300000 Sikkim

-5

350000

-10

Average Wage, 2009

Exhibit 6: Average Wage vs CAGR of Average Wage by States A mobility matrix is another insightful way of visualising 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. Exhibit 7 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.

1

1 2

2

Average Wages: Mobility Matrix 2014 4 5

3

Assam, Punjab

Andhra Pradesh

8

9

10

Tamil Nadu Daman & Diu, Meghalaya

4 Andaman & Nicobar

5

6

7

Kerala Jammu & Kashmir

3

2009

6

Nagaland, Tripura, Manipur, Bihar

West Bengal

Haryana

Dadra & Nagar Haveli

7

Rajasthan

Uttar Pradesh

Delhi Madhya Pradesh, Gujarat

Pondicherry

8

Karnataka

Himachal Pradesh

9 10

Chandigarh Orissa

Uttarakhand

Sikkim

Goa, Maharashtra Chhattisgarh, Jharkhand

Exhibit 7: Mobility Matrix

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Economic success or failure is not dependent on the starting levels of wages or employment Another important attribute of economic performance of regions is the employment growth over time, which is depicted in Exhibit 8. 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 starting level of wages of the state. Exhibit 9 shows how the growth in employment between 2009 and 2014 is weakly correlated to the average wages in 2009. 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.

CAGR of Employment, 2009-2014

25 20

Meghalaya Manipur Sikkim

15 10

Himachal Pradesh Bihar

Nagaland

Uttarakhand y = -3E-06x + 6.3364 R² = 0.0736

Rajasthan

Uttar Pradesh Gujarat West Bengal Andhra Pradesh Punjab Pondicherry Haryana Karnataka Chhattisgarh Kerala 0 Delhi 0 500000 1000000 5

-5

Jharkhand

Maharashtra

1500000

Tamil Nadu

2000000

Starting Employment, 2009

Exhibit 8: Starting Employment vs CAGR of employment by 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 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.

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CAGR of Average Wage (%), 2009-2014

25 20

Nagaland

Kerala

Haryana

Delhi Rajasthan Punjab Uttar Pradesh Himachal Pradesh Madhya Pradesh West Bengal Karnataka Jharkhand Pondicherry Dadra & Nagar Haveli

15 10 5 0

Uttarakhand 500000

0

y = 2E-06x + 10.631 R² = 0.0402 Andhra Pradesh Gujarat

1000000

Tamil Nadu

Maharashtra

1500000

2000000

Sikkim

-5 -10

Starting Employment, 2009

Exhibit 9: Starting Employment vs CAGR of Average Wages It is a common assertion in economic development circles that large regions 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. Exhibit 10 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.

CAGR of Employment, 2009-2014

25 20

Meghalaya Manipur

15

Sikkim Himachal Pradesh

10

Bihar Nagaland

5

Tripura 0 0 -5

50000

Dadra & Nagar Haveli

Uttarakhand

y = 8E-06x + 4.3769 R² = 0.0072

Rajasthan Maharashtra Uttar Pradesh West Bengal Chandigarh Jharkhand Punjab Goa Pondicherry Chhattisgarh Kerala100000 150000 200000 250000 Delhi

Assam

300000

Average Wages, 2009

Exhibit 10. Employment Growth vs Initial Average Wage by States, 2009-14 Next, as seen in the Exhibit 11, 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 wages of that region.

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350000 Jharkhand Goa Chhattisgarh Chandigarh

Average Wage, 2014

300000 250000

y = 0.0243x + 166437 R² = 0.0382

Haryana

Sikkim Delhi

Karnataka Madhya Pradesh Gujarat Uttar Pradesh Rajasthan Uttarakhand Meghalaya Andhra Pradesh West Bengal Kerala Punjab Assam Bihar

200000 150000 100000

Maharashtra

Tamil Nadu

Manipur

50000

Tripura 0 0

500000

1000000

1500000

2000000

2500000

Employment, 2014

Exhibit 11: Average Wage vs Employment

Innovation within Indian states is not significant enough to be a determining force for wages paid to employees. India has a long climb ahead of it on the innovation ladder.

Patents pwe 100000 employees

All the measures considered until now provide a basic idea about the regional economic performance across India. A more defining aspect of the same is the amount of innovative activities taking place in a region. Higher innovative capabilities provide a region with a considerable competitive advantage over other regions. Patenting is the best available measure for quantifying this aspect. We had to exclude Delhi from the dataset since it was an outlier having received 9 times more patent applications than the next highest state. Exhibit 12 shows that the next three 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

Exhibit 12: Patents by State

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Further, it seems to be the case that larger states by employment size show higher innovative tendencies. This can be seen in Exhibit 13 which shows 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. Exhibit 14 points to the lack of a clear relationship between patenting activity and wages. This is also observed when we look at the relationship between State Innovation Scores and average wages.

Innovation vs Average Wages

y = 2E-05x + 22.987 R² = 0.0446

50 Tamil Nadu

State Innovation Scores

45

Punjab Kerala

40 35 Tripura

30 25 20 15

Maharashtra Goa

Karnataka

Gujarat Jammu & Kashmir Himachal Pradesh West Bengal Haryana Andhra Pradesh Rajasthan Manipur Assam SikkimChhattisgarh Bihar Jharkhand Meghalaya

10

Uttar Pradesh Delhi

Uttarakhand

Nagaland

Odisha

Madhya Pradesh

5 0 0

50000

100000

150000

200000

250000

300000

350000

Average Wages, 2014

Exhibit 13: Innovation vs Average Wages

IMPLICATION Distinctive Strategy for all 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 a decentralised economic policy and how a 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.

CLUSTER MAPPING INDIA | 17

4. COMPOSITION OF REGIONAL ECONOMIES Regional economies comprise of two types of industries (Exhibit 14).

Exhibit 14: Types of Industries

Traded industries are those that concentrate in particular regions but sell products across regions and countries (Delgado, Bryden, & Zyontz). Examples of traded industries include apparel, automotive, textiles etc. In principle they can be located anywhere. But similar traded industries tend to locate in nearby locations. And as they grow beyond the needs of the local market, they are more sophisticated and hence productive. Local industries are those that are dispersed throughout the nation. Their presence in a particular region is generally proportional to the region’s size as they primarily serve the local market (Delgado, Bryden, & Zyontz). Examples of local industries are real estate services, hospitals, etc.

Regional economies are profiled on the mix of clusters, traded clusters are formed by grouping traded industries and likewise, the groups of local industries form local clusters.

DEFINING CLUSTERS 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 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 Coagglomertaion 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 CLUSTER MAPPING INDIA | 18

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 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) 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.

Although the definitions are based on the industry patters in 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 cluster4. 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 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.

4

The dataset used is ASI. CLUSTER MAPPING INDIA | 19

We proceed by mapping the U.S. NAAIC Codes to the NIC 2008 Codes. The mapping procedure provides us with the Indian Cluster definitions, presented in Exhibit 15 and 16 below.

EXHIBIT 15: TRADED CLUSTERS Cluster Code

Cluster Name

Cluster Code

Cluster Name

1

Aerospace Vehicles and Defense

27

Lighting and Electrical Equipment

2

Agricultural Products, Inputs and Services

28

Livestock Processing

3

Apparel

29

Marketing, Design, and Publishing

4

Automotive

30

Medical Devices

5

Biopharmaceuticals

31

Metal Mining

6

Business Services

32

Metalworking Technology

7

Coal Mining

33

Music and Sound Recording

8

Communications Equipment and Services

34

Nonmetal Mining

9

Construction Products and Services

35

Oil and Gas Production and Transportation

10

Distribution and Electronic Commerce

36

Paper and Packaging

11

Downstream Chemical Products

37

Performing Arts

12

Downstream Metal Products

38

Plastics

13

Education and Knowledge Creation

39

Printing Services

14

Electric Power Generation and Transmission

40

Production Technology and Heavy Machinery

15

Environmental Services

41

Recreational and Small Electric Goods

16

Financial Services

42

Textile Manufacturing

17

Fishing and Fishing Products

43

Tobacco

18

Food Processing and Manufacturing

44

Trailers, Motor Homes, and Appliances

19

Footwear

45

Transportation and Logistics

CLUSTER MAPPING INDIA | 20

20

Forestry

46

Upstream Chemical Products

21

Furniture

47

Upstream Metal Manufacturing

22

Hospitality and Tourism

48

Video Production and Distribution

23

Information Technology and Analytical Instruments

49

Vulcanized and Fired Materials

24

Insurance Services

50

Water Transportation

25

Jewelry and Precious Metals

51

Wood Products

26

Leather and Related Products

EXHIBIT 16: 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

CLUSTER MAPPING INDIA | 21

FINDINGS There is no significant difference in the average wages and wage growth between the traded and local clusters We begin by analysing 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 Exhibit 17. 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.

Exhibit 17. Composition of Indian economy by type of Clusters Traded

Local

920

229

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 clearly outliers to this trend, as seen from Exhibit 18. 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 Bay of Bengal and is far away from the mainland, thus making it difficult to obtain local product and services from adjacent regions. Therefore, high presence of local industries is expected.

2500000

Total Employment, 2014

2000000 y = 11380x - 678183 R² = 0.0279

1500000 1000000 Andaman & Nicobar Islands

500000

Chandigarh Delhi

0 50

60

70

80

90

Percentage of Traded Employment, 2014

100

110

Exhibit 18. Employment Size vs. percentage of traded employment by States

CLUSTER MAPPING INDIA | 22

Exhibit 18 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. Traded clusters also have almost the same wage growth as the local clusters.

We also find that the average level of regional local wages is weakly associated with the average level of regional traded wages, as depicted in Exhibit 19. On an average, local wages are 88 percent of the traded wages. However, there exists no relationship between percentage of traded employment and average regional wages. In fact, as presented in Exhibit 20, proportion of traded employment explains just 0.47 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.

4,00,000.00

Average Local Wage

3,50,000.00 y = 0.5257x + 63935 R² = 0.3047

3,00,000.00 2,50,000.00 2,00,000.00 1,50,000.00 1,00,000.00 50,000.00 0.00 0.00

50,000.00

1,00,000.00 1,50,000.00 2,00,000.00 2,50,000.00 3,00,000.00 3,50,000.00

Average Traded Wage

Exhibit 19. Average local wages vs average traded wages, 2014

350000

Average Wages

300000 250000 200000 150000 y = -601.82x + 234260 R² = 0.0047

100000 50000 0 60

65

70

75

80

85

90

95

100

105

Percentage of Traded Employment

Exhibit 20. Change in average wage with percentage of traded employment

CLUSTER MAPPING INDIA | 23

Traded clusters vary substantially in terms of employment, average wage, wage growth and employment generation Exhibit 21 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 “Metal Mining” that employed only 56 people in 2014. Average cluster wages range from Rs.45336 to Rs.592898.

Exhibit 21. Profile of Traded Clusters

Cluster Name Aerospace Vehicles and Defense Agricultural Products, Inputs and Services

Total Employees 2014

CAGR of Employment

Job Growth Rank

Average Wages 2014

CAGR of Average Wages

Wage Growth Rank

11408

0.13

3

398248

0.09

38

157894

-0.01

38

245075

0.09

36

Apparel

774067

0.03

25

127469

0.12

19

Automotive

874750

0.08

12

258738

0.11

26

Biopharmaceuticals

618248

0.10

6

277723

0.11

25

Business Services

482

-0.04

42

253637

0.26

1

Coal Mining Communications Equipment and Services Construction Products and Services Distribution and Electronic Commerce Downstream Chemical Products Downstream Metal Products Education and Knowledge Creation Electric Power Generation and Transmission Environmental Services

116

Financial Services Fishing and Fishing Products Food Processing and Manufacturing Footwear

123255

95120

0.04

22

337276

0.05

41

416004

0.03

28

216861

0.10

33

47800

0.03

24

158776

0.12

18

315260

0.02

32

194080

0.11

28

475000

0.07

13

162065

0.11

31

61519

0.28

1

294989

0.11

32

11036

0.11

5

120207

0.15

6

44164

0.06

15

130073

0.18

3

1588576

0.02

34

145497

0.16

4

252613

0.04

23

121100

0.15

8

60352

0.09

10

212484

0.10

35

Forestry Furniture

CLUSTER MAPPING INDIA | 24

Hospitality and Tourism Information Technology and Analytical Instruments Insurance Services Jewelry and Precious Metals Leather and Related Products Lighting and Electrical Equipment

238649

0.03

27

397489

0.16

5

152107

0.02

29

209679

0.14

10

78324

0.06

18

120478

0.12

16

425193

0.06

17

256869

0.12

22

Livestock Processing Marketing, Design, and Publishing

25573

0.12

4

181899

0.14

9

4157

-0.02

39

196303

0.14

12

Medical Devices

39274

0.09

9

184411

0.05

42

Metal Mining Metalworking Technology Music and Sound Recording Nonmetal Mining Oil and Gas Production and Transportation

56

82166

148531

0.06

20

202919

0.11

24

885

0.10

8

523153

0.07

40

10331

0.14

2

77043

0.15

7

89103

0.02

30

592898

0.12

21

247526

0.01

35

168531

0.13

14

Plastics

390770

0.06

14

174560

0.11

27

Printing Services Production Technology and Heavy Machinery Recreational and Small Electric Goods

206395

0.09

11

174954

0.08

39

548233

-0.03

41

285298

0.14

11

287777

0.06

19

203803

0.13

13

Textile Manufacturing

1700162

0.02

31

128855

0.13

15

Tobacco Trailers, Motor Homes, and Appliances Transportation and Logistics Upstream Chemical Products Upstream Metal Manufacturing Video Production and Distribution Vulcanized and Fired Materials

444652

0.00

37

45336

0.11

29

66017

0.10

7

287610

0.21

2

220413

0.05

21

267401

0.12

20

986622

0.02

33

274794

0.10

34

2175

-0.03

40

271698

0.09

37

757367

0.06

16

123710

0.12

17

Water Transportation

27986

0.01

36

293469

0.11

30

Wood Products

78750

0.03

26

114869

0.11

23

Paper and Packaging Performing Arts

CLUSTER MAPPING INDIA | 25

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.

Employment growth does not show a relationship with the presence of high-tech clusters The overall impact of the high-tech clusters on the regional economy is very small. We analysed 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.

Employment in High-Tech Clusters vs Growth in Employment 250000 y = -328.64x + 29522 R² = 0.0015

Maharashtra

Employment in High-tech Clusters

200000

Tamil Nadu 150000

Dadra & Nagar Haveli Karnataka

100000

Uttarakhand

Andhra Pradesh

Delhi

-5

Gujarat

Himachal Pradesh

Uttar Pradesh Rajasthan 50000 Haryana West Bengal Kerala Goa Chandigarh Chhattisgarh Tripura 0 0 5

Bihar

Sikkim

10

Manipur Meghalaya

15

20

25

CAGR of Total Employees

Exhibit 22: 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.

CLUSTER MAPPING INDIA | 26

The composition of regional economies differs significantly Regions differ greatly in terms of the clusters that compose the economy (Exhibit 23). Every region has some employment in the vast majority of these clusters, but they are strong in only few of them.

Exhibit 23: Cluster Profile of selected regions

IMPLICATION Widen the innovative capacity to all the clusters Majority of the employment is concentrated within the clusters that are not considered to be high-tech. Therefore, to enhance regional prosperity, innovative capacity must be built in many clusters.

CLUSTER MAPPING INDIA | 27

5. CLUSTER ASSESSMENT: PERFORMANCE OF CLUSTERS

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

Exhibit 24: 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 region’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?‘†â„Žđ?‘Žđ?‘&#x;đ?‘’ đ?‘œđ?‘“ đ?‘&#x;đ?‘’đ?‘”đ?‘–đ?‘œđ?‘›đ?‘Žđ?‘™ đ?‘’đ?‘šđ?‘?đ?‘™đ?‘œđ?‘Śđ?‘šđ?‘’đ?‘›đ?‘Ą đ?‘–đ?‘› đ?‘&#x;đ?‘’đ?‘”đ?‘–đ?‘œđ?‘›đ?‘Žđ?‘™ đ?‘–đ?‘›đ?‘‘đ?‘˘đ?‘ đ?‘Ąđ?‘&#x;đ?‘Ś đ?‘œđ?‘&#x; đ?‘?đ?‘™đ?‘˘đ?‘ đ?‘Ąđ?‘’đ?‘&#x; đ?‘†â„Žđ?‘Žđ?‘&#x;đ?‘’ đ?‘œđ?‘“ đ?‘–đ?‘›đ?‘‘đ?‘˘đ?‘ đ?‘Ąđ?‘&#x;đ?‘Ś ′ đ?‘ đ?‘œđ?‘&#x; đ?‘?đ?‘™đ?‘˘đ?‘ đ?‘Ąđ?‘’đ?‘&#x; ′ đ?‘ đ?‘’đ?‘šđ?‘?đ?‘™đ?‘œđ?‘Śđ?‘šđ?‘’đ?‘›đ?‘Ą đ?‘–đ?‘› đ?‘›đ?‘Žđ?‘Ąđ?‘–đ?‘œđ?‘›đ?‘Žđ?‘™ đ?‘’đ?‘šđ?‘?đ?‘™đ?‘œđ?‘Śđ?‘šđ?‘’đ?‘›đ?‘Ą Where, Share of regional employment in regional industry =

đ?‘‡đ?‘œđ?‘Ąđ?‘Žđ?‘™ đ??¸đ?‘šđ?‘?đ?‘™đ?‘œđ?‘Śđ?‘šđ?‘’đ?‘›đ?‘Ąđ?‘–,đ?‘&#x; đ?‘‡đ?‘œđ?‘Ąđ?‘Žđ?‘™ đ??¸đ?‘šđ?‘?đ?‘™đ?‘œđ?‘Śđ?‘šđ?‘’đ?‘›đ?‘Ąđ?‘&#x;

Share of industry’s employment in national employment =

đ?‘‡đ?‘œđ?‘Ąđ?‘Žđ?‘™ đ??¸đ?‘šđ?‘?đ?‘™đ?‘œđ?‘Śđ?‘šđ?‘’đ?‘›đ?‘Ąđ?‘– đ?‘‡đ?‘œđ?‘Ąđ?‘Žđ?‘™ đ??¸đ?‘šđ?‘?đ?‘™đ?‘œđ?‘Śđ?‘šđ?‘’đ?‘›đ?‘Ą

CLUSTER MAPPING INDIA | 28

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 increase in the number of participants.

Productivity reflects how well is cluster operating. This is measured by identifying top 20 percent of the locations by gross value added and average wages.

Dynamism, captured by top 20 percent of the locations by employment growth, reflects whether a cluster is benefitting from strong cluster effects from its development.

To reach at 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 region’s cluster portfolio is measured by summing up the performance across its individual clusters. For instance, if a state has 4 one-star clusters, 7 two-star clusters, 4 three-star clusters and 5 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 the they tend to have employment 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.

FINDINGS Cluster profile of regions differs significantly, with Southern region outperforming rest of the country The map below 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 competitiveness and increase prosperity in the region.

CLUSTER MAPPING INDIA | 29

Exhibit 25: Cluster Strength Exhibit 26 below presents the complete cluster portfolio of a state. Each state has a distinct profile of clusters creating prosperity.

Exhibit 26: Cluster Profile State Name Andhra Pradesh

Total Stars 47

One Star Clusters 5

Two Star Clusters 10

Three Star Clusters 6

Four Star Clusters 1

Top 3 clusters by LQ Fishing and Fishing Products Agricultural Products, Inputs and Services Construction Products and Services

Assam

27

15

3

2

0

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

Bihar

28

16

3

2

0

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

Chandigarh

19

8

4

1

0

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

Chhattisgarh

21

11

2

2

0

Upstream Metal Manufacturing Electric Power Generation and Transmission Tobacco

Dadra & Nagar Haveli

19

12

2

1

0

Printing Services Plastics Textile Manufacturing

Daman & Diu

18

14

2

0

0

Plastics

CLUSTER MAPPING INDIA | 30

Lighting and Electrical Equipment Recreational and Small Electric Goods Delhi

58

12

8

6

3

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

Goa

45

16

10

3

0

Medical Devices Water Transportation Communications Equipment and Services

Gujarat

73

18

14

5

3

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

Haryana

42

14

5

6

0

Medical Devices Recreational and Small Electric Goods Automotive

Himachal Pradesh

43

18

5

1

3

Business Services Trailers, Motor Homes, and Appliances Biopharmaceuticals

Jammu & Kashmir

15

8

2

1

0

Upstream Chemical Products Biopharmaceuticals Furniture

Jharkhand

28

9

3

3

1

Upstream Metal Manufacturing Local Utilities Electric Power Generation and Transmission

Karnataka

82

19

14

9

2

Local Hospitality Establishments Aerospace Vehicles and Defense Business Services

Kerala

55

14

10

3

3

Fishing and Fishing Products Water Transportation Local Entertainment and Media

Madhya Pradesh

30

10

5

2

1

Business Services Environmental Services Local Utilities

Maharashtra

109

7

22

14

4

Video Production and Distribution Music and Sound Recording Local Commercial Services

Manipur

10

6

2

0

0

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

Meghalaya

12

8

2

0

0

Construction Products and Services Wood Products Local Commercial Services

Nagaland

8

4

2

0

0

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

Orissa

30

9

4

3

1

Coal Mining Metal Mining Upstream Metal Manufacturing

Pondicherry

18

10

4

0

0

Information Technology and Analytical Instruments Upstream Chemical Products

CLUSTER MAPPING INDIA | 31

Furniture Punjab

22

9

5

1

0

Recreational and Small Electric Goods Vulcanized and Fired Materials Metalworking Technology

Rajasthan

43

16

6

5

0

Construction Products and Services Furniture Electric Power Generation and Transmission

Sikkim

5

5

0

0

0

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

Tamil Nadu

100

17

15

11

5

Footwear Textile Manufacturing Automotive

Tripura

6

4

1

0

0

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

Uttar Pradesh

123

16

13

6

2

Livestock Processing Distribution and Electronic Commerce Footwear

Uttarakhand

53

16

5

9

0

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

West Bengal

58

13

12

3

3

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

Clusters enhance prosperity Exhibit 27 plots the cluster portfolio strength with the economic development of a region. A positive relationship between the two is observed backing the claim that clusters lead to prosperity.

Economic Development

250000 200000

Dadra & NagarChhattisgarh Haveli

y = 238.84x + 78966

Orissa Delhi

150000 Punjab

Jammu & Kashmir Daman & Diu Sikkim

Meghalaya Goa 100000 Maharashtra Nagaland Tamil Nadu MadhyaGujarat Pradesh Chandigarh Haryana Assam Kerala 50000 Andhra Pradesh Manipur Jharkhand Bih‌ 0 0 20 40 60

Karnataka Pondicherry

Himachal Pradesh

80

100

Rajasthan

120

140

Cluster Strength

Exhibit 27: Economic Development & Cluster Strength CLUSTER MAPPING INDIA | 32

Regions with a strong cluster portfolio perform better on innovation There is a lot of evidence to suggest that clusters provide 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.5 y = 0.1846x + 19.26 R² = 0.5248

50.00 45.00

Delhi

Tamil Nadu

Innovation Scores

40.00 Goa

35.00

Uttar Pradesh Maharashtra

Kerala

Karnataka

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

20

40

60

80

100

120

140

Cluster Strength

Exhibit 28: Innovation & Cluster Strength

5

We use the Innovation scores by Institute for Competitiveness, India to depict this relationship.

CLUSTER MAPPING INDIA | 33

STATE PROFILE: MAHARASHTRA Maharashtra, the third largest state in terms of area is home to 9.28 percent of the total Indian population. It’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. Exhibit 29 depicts Diamond for the state of Maharashtra. The main strengths of the state are in its demand conditions and context for strategy and rivalry.

Exhibit 29: Diamond Model for Maharashtra

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

Exhibit 30: Cluster Portfolio State Name

Total Stars

One Star Clusters

Two Star Clusters

Three Star Clusters

Four Star Clusters

Maharashtra

109

7

22

14

4

Top 3 clusters by LQ Video Production and Distribution Music and Sound Recording Local Commercial Services

By concentration, the top clusters are Video Production and Distribution, Music and Sound Recording and Local Commercial Services. The concentration of Video Production and Distribution, Music and Sound Recording shows the presence of Bollywood Cluster in Mumbai. It produces the largest shares of films in India.

Exhibit 31: Employment & Wages - Maharashtra CLUSTER MAPPING INDIA | 34

Exhibit 32: GVA - Maharashtra

Exhibit 33: Traded Cluster Portfolio - Maharashtra

CLUSTER MAPPING INDIA | 35

CLUSTER WISE ANALYSIS: AUTOMOTIVE CLUSTER 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.

Exhibit 34: Composition of Automotive Cluster Basic Facts: The automotive cluster in India provides employment to 874750 people, which makes it one of the highest employment providing traded cluster in India (6.7 % of the total traded employment).

Exhibit 35: Automotive Cluster Profile Total Employees Cluster Name 2014

CAGR of Employment

Job Growth Rank

Average Wages 2014

CAGR of Average Wages

Wage Growth Rank

Automotive

0.08

12

258738

0.11

26

874750

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.

CLUSTER MAPPING INDIA | 36

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

Exhibit 36: Evolution of Automotive Cluster Leading Regions (by cluster strength based on four-star methodology):

Exhibit 36: 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.

CLUSTER MAPPING INDIA | 37

Employment and Growth

Exhibit 37: States of 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.

Specialization and Productivity The cluster is highly concentrated in Haryana, Jharkhand, Uttarakhand, Tamil Nadu, Maharashtra and Madhya Pradesh. But the productivity of the clusters varies across regions. Infact, many of the specialized regions don’t even make it to the list of most productive regions.

CLUSTER MAPPING INDIA | 38

Exhibit 38: States by Location Quotient, Automotive Cluster

Exhibit 39: States by Average Wages, Automotive Cluster

CLUSTER MAPPING INDIA | 39

Exhibit 40: States by per worker GVA, Automotive Cluster

CLUSTER MAPPING INDIA | 40

6. DETERMINANTS OF REGIONAL COMPETITIVENESS

The quality of region’s business environment is embodied in four broad areas (Exhibit 41).

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 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 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.

Exhibit 41: Diamond Model. Source: Michael E Porter

CLUSTER MAPPING INDIA | 41

•

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 subpillar by evaluating the institutions of higher education and research as well as reforms taken by 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 population ultimately decide the nature of business and economic activities that can successfully run in a region. To evaluate this subpillar, 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 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 increases 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 sub-pillar.

• 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 subpillar.

CLUSTER MAPPING INDIA | 42

FINDINGS

Impact of Business Environment on Innovation, Productivity and Cluster Strength A positive relationship between the state competitiveness and cluster strength is observed (Exhibit 42)6. Areas that have strong business environment, better infrastructure facilities, strong legal and decision-making institutions have strong presence of clusters.

140

Uttar Pradesh

Cluster Stregth

120

Maharashtra Tamil Nadu

100 Karnataka Gujarat

80 Delhi

60

West Bengal Uttarakhand

Kerala Goa

Andhra Pradesh Haryana

Rajasthan

Jharkhand Odisha

Bihar Madhya Pradesh Assam Punjab Chhattisgarh Jammu & Kashmir Himachal Pradesh ManipurMeghalaya Nagaland SikkimTripura

40 20 0 0

5

10

15

20

25

30

35

State Competitiveness Rank

Exhibit 42: Competitiveness & Cluster Strength

Some factors important for cluster development are common across regions Although regions differ in their strengths and weaknesses on competitiveness index, there are certain factors that are important for all regions to grow.

140 120 100 80 60 40 20 0

Cluster Strength

Knowledge Creation

Exhibit 43: Cluster Strength & Knowledge Creation

We use the State Competitiveness Rank by Institute for Competitiveness, India to depict this relationship. The trend line is negative as State Competitiveness ranks are used instead of scores and a higher rank represents a lower score.

6

CLUSTER MAPPING INDIA | 43

Universities and knowledge creation are the driving force behind regions They are instrumental in providing the right kind of environment that is favourable for encouraging innovation and development of clusters. A correlation of 0.45 is observed between the measure of cluster strength and knowledge creation.

However, there are certain outliers to this trend such as Uttar Pradesh (Exhibit 43). Most of them can be explained due to the correlation between area and cluster strength. Most of the medium and large sized regions have low levels of true specialisation in any one of the clusters.

CLUSTER MAPPING INDIA | 44

7. ROLE OF THE GOVERNMENT

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 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.

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).

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 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 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.

CLUSTER MAPPING INDIA | 45

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APPENDIX 1: DATA ASI The core dataset used is Annual Survey of Industries (ASI), which is the principal source of industrial statistics in India. It provides information about the growth, employment, wages, composition and structure of organised manufacturing sector comprising activities related to manufacturing processes, repair services, gas and water supply and cold storage.

The ASI extends to the entire country except 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.

ASI follows a circular systematic sampling procedure. All the industrial units in the universe are categorized into two sectors i.e. census and sample.

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. 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

NSSO The other dataset used for total and sectoral employment is NSSO. NSSO collects information on employment and unemployment through comprehensive surveys conducted quinquennially. The first survey on employment and unemployment was carried out during 1972-73 (27th Round). The NSSO classifies workers by three approaches namely, usual status, current weekly status, and current daily status and further by demographic, social, economic and spatial characteristics. (Labour and Employment Statistics, 2018).

CLUSTER MAPPING INDIA | 47

LEVEL OF INDUSTRY ASI uses the National Industrial Classification (NIC) to classify the activity of factories in the ASI frame in their appropriate industry groups and provides data from one digit to five-digit levels. We make use of the five-digit level data. The latest NIC classification done in 2008 is a revised version of the NIC 2004 which is adjusted to present data according to categories of International Standard Industrial Classification.

PERIOD OF OUR STUDY The period of our 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 grouping of activities in the two systems. For instance, some of the 5digit 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 7 union territories. Our study covers 26 states and 6 union territories. The geographical areas that we leave out include Arunachal Pradesh, Mizoram and Lakshadweep as they are beyond the 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.

CLUSTER MAPPING INDIA | 48