rajiv khosla, manoj sharma - regional disparities in ... disparities in industrial development in...

REGIONAL DISPARITIES IN INDUSTRIAL DEVELOPMENT IN INDIA

Rajiv Khosla*, Manoj Sharma

University School of Business,

Chandigarh University, Gharuan (Punjab) India.

Mob: +919814722870;

Email [email protected]

*Corresponding author

DR. MANOJ K. SHARMA is Senior Professor in the area of Business Economics at

University School of Business, Panjab University, Chandigarh. He has over 27 years of

experience in teaching and research to the Post Graduate classes. He has over 100 research

articles in various national and international journals. Dr. Sharma is a widely travelled person

and has attended 10 international conferences outside India. He has extensively undertaken the

consultancy work for both the corporate sector as well as government departments. He has

guided more than 10 doctoral theses. Currently, he is President, Chandigarh Management

Association and actively engaged in conducting Management Development Programs.

DR. RAJIV KHOSLA is Associate Professor and Head, University, School of Business,

Chandigarh University, Gharuan, Mohali. He has 12 years of experience in the area of Business

Economics in teaching and research. He has 17 publications to his name published in national

and international journals. He has attended 20 conferences. He is in the editorial board of many

journals of repute. He is supervising two doctoral students under his guidance.

Abstract

This article studies the level of industrial development across different states of India,

with the help of composite indices. The study has brought out that huge disparities exist among

the states which have accentuated over a period of time. In order to find out the magnitude of

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Rajiv Khosla, Manoj Sharma - Regional Disparities in Industrial Development in India

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inter-state disparities in industries, 21 major Indian states for the period 1980-81 to 2009-10 have

been examined.

JEL Classification: R15, L16

Keywords: Disparities, weighted indicators, composite index, convergence and divergence

INTRODUCTION

Economic development is a concern of all societies as well as the nations. During the

course of economic development, the determinants of developmental process shape up in the

form of agrarian transformation towards non-agricultural sectors. Also, at some stage of

economic development, increasing urbanization, improved quality of infrastructure, increased

standard of living of the human beings, and the improved quality of overall governance takes

place. However, in this process of development, some regions of the economy grow faster in

terms of these determinants whereas others tend to lag behind. Thus, with the economic

development of an economy, a variety of inter-regional and inter-state variations (related to all

macro indices) prop up. Of course, these differences have become inevitable all over the world,

but the developing countries bear more brunt of such disparities. It is attributable to the fact that

already meager resources in these economies are further invested in few economically efficient

areas in order to generate increased profits.

Myrdal (1957) argued that once growth starts in a locality, "the ever increasing internal

and external economies - tends to sustain the continuous growth at the expense of other localities

and regions where instead relative stagnation or regression became the pattern". This concerted

development fails to permeate even into the edging areas, thereby offering a lopsided

development. Thus, in the process of profit making, regional disparities broaden. Stern

disparities augment differences not only in the level of income, poverty, unemployment and

infrastructure, nevertheless, standard of living of the people also undergo substantial changes.

Segments of the populace in the neglected regions remain devoid of even fundamental services

such as health, education, clean drinking water and sanitation facilities etc. Such misbalanced,

dejected and disorganized development of few regions has its bearing on the overall economic

development as well. Not only this, these inequalities can create damaging effects on the national

unity and harmony also. Considerable differences in economic prosperity of some regions lay


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foundation for discontent among the masses leading to protests and revolts, sooner or later. For a

national economy to prosper, all the regions have to be brought into some sort of harmony.

Hence, balanced regional development is taken as of paramount importance not only to achieve

self-sufficiency at national level, but also to ensure political stability. For balanced and sustained

growth, it is essential that various sectors of the economy namely agriculture, industry, trade etc.

grow harmoniously over the different regions of the country. But for the genuine progress of an

economy and in order to enlarge the gross domestic product (GDP), a shift in economic activity

away from agriculture is required i.e. towards services and manufacturing sectors, owing to

higher elasticity of the latter than that of former sector (Fisher, 1939 and Clark, 1940). On

account of high population growth and the limited capacity of the agriculture sector to absorb

additional labour, industrialization is seen as an effective remedy for uprooting major regional

imbalances also. Moreover, a country’s economic progress is judged and determined by the

progress and development of its industrial sector. Sutcliffe (1971) argues that for the vast

majority, if not for all countries, high levels of income cannot be reached without

industrialization. Industrialization is thus inseparable from substantial and sustained economic

development, because it is both a consequence of high income and a means of higher

productivity. The significance of industrialization for India was summed up by the first Prime

Minister of India late Pt. Jawaharlal Nehru when he said, “real progress must ultimately depend

upon industrialization” (Nehru, 1958). Industrialization is thus, a means for raising the national

income per capita. Henceforth, it may be concluded, that the impact of industrialization not only

remains limited to the economic aspects of the people’s life, but it also impinge upon their social

life.

In India, regional imbalance has been one of the major concerns of policy makers and

planners since independence. Throughout the planning era, colossal efforts have been undertaken

to reduce the inter-state disparities, but the menace has continued unabated. The advocates of

non-protectionist policies for industries, who succeeded in opening the Indian industries towards

competition also could not gain much out of their efforts. The disparities have continued to

broaden unabated. A World Bank report (2008) pointed out that disparity in income distribution

in India has increased (Gini-coefficient in this connection has risen from 0.3152 in 1993-94 to

0.3676 in 2004-05) during 1993-2005. A brief account of the analysis of the industrial policies is

presented in the following paragraphs.


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After independence, policy makers of the country consented on rapid industrialization of

the economy to gain economic sovereignty. Hence, specific priorities for industrial development

were laid down in the First Five Year Plan. However, the first comprehensive statement on the

strategy of industrial development took place in the Industrial Policy Resolution of 1956. It

reflected the desire of the Indian State to achieve self sufficiency in industrial production. This

policy was delineated by the Mahalnobis Model of growth focusing on the huge state

investments in heavy industries. Policy emphasized on the objectives of accelerating economic

growth and boost up the process of industrialization in order to achieve a socialistic pattern of the

society. With scarce capital and narrow entrepreneurial base, government was given the task to

encourage domestic production in order to achieve a socialistic pattern of the society. To remove

regional disparities emphasis was laid on the development of regions with low industrial base by

equipping them with adequate infrastructure. Further, all sorts of assistance to small and cottage

industries for more equitable distribution of income were given. However, in this process some

big industrial houses succeeded in obtaining disproportionately larger share of licenses. It led to

the concentration of economic power and wealth into fewer hands.

Accordingly, Dutt Committee was constituted by the government in the year 1967 which

advocated for an ungenerous approach in giving the licenses to big industrial houses. It

recommended that licenses to big houses should be given only for setting up industry in core and

heavy investment sectors. This context led to the Monopolies and Restrictive Trade Practices

(MRTP) Act, issued in 1969 to comprehensively control the concentration of economic power.

According to the Act, large industries were designated as MRTP companies and were eligible to

participate in industries that were not reserved for the government or the small scale sector.

Further, the industrial licensing policy of 1970 restricted large business houses to the core, heavy

and export oriented industries.

This strategy of giving philanthropic penchant to the large industrial houses ignored the

small and medium entrepreneurs. Thus, in the Industrial policy statement of 1973, preferential

treatment was accorded to the small and medium entrepreneurs over the large houses and foreign

companies in setting up new capacities particularly in the production of mass consumption

goods. Licensing requirements for setting up fixed assets for new undertakings were waived off

for an amount up to Rs.10 million. Another industrial policy statement was released in the year

1977 generously expanding the list of items reserved for production by the small sector from 180


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to 500. To promote the Indian industries to respond to the emerging challenges effectively, a new

industrial policy statement was issued in the year 1980 that focused on giving autonomy to the

public sector units. A wave of deregulation gripped the industrial sector of India. In 1988, all

industries (except 26 industries on the negative list), were exempted from licensing though they

were subject to investment and locational limitations. Thus, before the onset of reform process in

1991, industrialization in India transpired in a protected environment. Deliberately, the tariffs

and quantitative controls were kept stringent to create a barrier for the foreign competition on the

domestic market.

The industrial policy announced in 1991 unleashed the Indian industry from bureaucratic

controls. Strict industrial licensing hitherto being followed was dispensed with giving way to

liberal foreign investment and technology transfers. Only six industries were such that required

compulsory licensing. Similarly, three industries were exclusively reserved for the public sector.

Successive policies primarily focused on industrial restructuring besides giving freedom and

flexibility to the Indian industries to respond to the market forces. Though, the liberalization

wave has been able to boost up the growth rate of GDP yet it has not been successful in reducing

regional disparities in industrial development. New industries are being located in developed

states like Maharashtra, Gujarat, West Bengal, Andhra Pradesh, Tamil Nadu and Karnataka.

Other states like Kerala, Uttar Pardesh, Bihar and Punjab failed to foster in terms of

industrialization. Hence, industrial policies since liberalization have perpetuated regional

inequalities in the industrial scenario of the country. The present study is an attempt to find out

regional inequalities in terms of industrialization in India before and after liberalization.

Specifically, our endeavor is to:

1. Find out if industrialization has transpired in all states or a few states of the union;

2. Identify the states which have exhibited dismal performance in terms of

industrialization in the time period taken for this study;

3. Assess if there is a potential for regional divergence of industries instead of regional

convergence.

SECTION II

DATABASE AND METHODOLOGY

In order to fulfill the above mentioned objectives secondary data related to the number of

factories, workers, employees, fixed capital, invested capital, wages, emoluments, total output,


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profits, net value added and gross value added has been obtained from the Annual Survey of

Industries reports for the years 1980-81, 190-91, 2001-02 (data for the year 2000-01 could not be

obtained despite best efforts) and 2009-10. Fifteen structural and technical ratios have been

analyzed to facilitate comparison. Primarily, the ratios have been formed keeping in view the

physical, productivity, profitability and efficiency parameters.

1. Factories per unit of population (X1)

2. Factories per unit of area (X2)

3. Invested capital per unit of population (X3)

4. Invested capital per unit of area (X4)

5. Wages per unit of population (X5)

6. Total emoluments per unit of population (X6)

7. Employment per unit of population (X7)

8. Gross value added per unit of population (X8)

9. Net value added per unit of population (X9)

10. Gross value added per unit of invested capital (X10)

11. Net value added per unit of invested capital (X11)

12. Profits per unit of invested capital (X12)

13. Profits per unit of net value added (X13)

14. Output per unit of invested capital (X14)

15. Invested capital to factories (X15)

For studying the industrial development among different states, use of composite index

has been made. It is because 15 indicators selected for this study do not give a true picture of the

overall development of industrialization individually. In case of one indicator a state may be at

the top and in another at the bottom. Henceforth, a comprehensive index pertaining to different

aspects of industrialization has been sketched. The composite index is defined as,

Ci = W1x11 + W2x12 + W3x13 +…………. + Wnx1n

or Ci = ΣWj xij

where, Ci is the composite index for the ith

observation, Wj is the weight assigned to the jth

indicator and xij is the observation value after elimination of the scale bias.

From the above formula, it is evident that to compute the composite index, weights

assigned to the indicators need to be known. Variables chosen for analysis are converted into


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standard comparable units in order to minimize the chances of biasness. The method adopted to

standardize the variables is:

xij = (Xij/σ) x 100

where, xij is the scale free observation, Xij is the original observation and σ is the standard

deviation. The transformed series will have a standard deviation of unity. Though the bias gets

removed still the crucial problem remains of assigning appropriate weights to the selected

indicators. To derive the weights, we have made use of the principal component technique given

by Hagood (1943) to reduce the dimension of analysis.

SECTION III

ANALYSIS AND INTERPRETATION

Table 1 shows the standardized indicators of industrial development for the selected

states in the year 1980-81. It is clear from the table that in seven indicators i.e. X3, X5, X6, X8,

X9, X10 and X11 Maharashtra ranked first followed by Delhi and Himachal Pardesh, which

secured first rank in five (X1, X2, X4, X7 and X14) and three indicators (X12, X13 and X15)

respectively. Other states that excelled in the time period taken for this study included Gujarat,

Punjab and West Bengal. However, states like Jammu and Kashmir, Bihar, Uttar Pardesh,

Orissa, Rajasthan, Andhra Pardesh and Madhya Pardesh showed a dismal performance either in

all or the majority of the indicators. Over a period of time i.e. until 2009-10 (table 4) the same

phenomena existed wherein some states headed in terms of few indicators whereas others

remained at an equally low level. It is understandable also as various indicators pertaining to

efficiency, productivity and profitability reflected huge variations. From tables 1 to 4, it clearly

emerges that the Indian states have shown huge disparities in terms of selected indicators of

industrial development. But the study of overall level of development calls for the building up of

a composite index that incorporates all the dimensions of industrial development. The creation of

composite index brings into picture the technique of factor analysis to arrive at the weights. The

task has been achieved in tables 5 to 8.

Table 5 shows the results of factor analysis. It follows from the table that during 1980-81,

1990-91, 2001-02 and 2009-10, the first principal component could explain 64 percent, 70

percent, 78 percent and 85 percent of variations among the variables set pertaining to industrial

development. Some variables having exhibited negative results made them redundant for

analysis. To avoid this problem, squaring of the results is carried out and shown in parentheses.


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Further, on the basis of first factor the relative weights of 15 indicators have been calculated and

shown in table 6. It clearly follows from the table that X6, X5, X3, X9 and X8 are the most

important variables for explaining the variation in industrial development across Indian states.


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Table 1

Standardized indicators of industrial development for the selected states: 1980-81

Andhra Pradesh Assam Bihar Gujarat Haryana

Himachal Pradesh

Jammu & Kashmir Karnataka Kerala

Madhya Pradesh

X1 67.32 28.34 19.65 106.27 62.18 13.06 17.65 46.83 38.71 21.60

X2 19.14 9.52 11.54 26.99 26.54 1.47 0.69 13.24 37.03 3.71

X3 122.64 63.74 153.11 309.47 258.28 127.65 52.34 132.93 128.23 138.66

X4 33.30 20.45 85.87 75.05 105.29 13.69 1.97 35.90 117.14 22.76

X5 87.62 36.13 80.50 236.12 139.64 43.86 37.48 109.30 104.13 69.26

X6 93.18 37.82 78.66 231.76 172.40 53.64 35.43 124.14 98.85 70.79

X7 153.09 77.20 64.98 244.10 170.53 61.83 55.35 126.93 130.70 74.03

X8 77.47 46.95 73.45 253 227.60 103.88 25.64 114.23 104.00 80.33

X9 72.69 42.80 61.17 222.79 222.33 96.61 19.97 108.29 102.35 76.96

X10 115.62 134.85 87.81 139.91 161.30 148.96 89.67 157.30 148.46 106.05

X11 115.36 130.72 77.76 140.13 167.56 147.31 74.27 158.57 155.37 108.03

X12 0.14 1.19 0.19 0.67 1.68 2.05 0.03 0.67 0.58 0.62

X13 0.02 0.14 0.04 0.08 0.16 0.22 0.01 0.07 0.06 0.09

X14 117.97 136.84 73.75 165.15 142.61 63.66 84.28 127.75 155.91 82.40

X15 0.53 0.65 2.26 0.84 1.20 2.83 0.86 0.82 0.96 1.86

Contd……….


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Maharashtra Orissa Punjab Rajasthan Tamil Nadu Uttar Pradesh

West Bengal Delhi Mean

X1 80.18 19.16 109.49 25.43 68.71 20.85 37.65 166.95 52.78

X2 23.89 4.74 53.31 3.72 37.35 11.47 33.82 134.25 25.14

X3 316.86 110.43 254.56 120.89 188.56 93.02 183.05 269.13 167.98

X4 90.17 26.09 118.36 16.88 97.89 48.85 157.01 206.67 70.74

X5 302.03 72.99 111.35 63.56 180.74 64.19 269.44 278.26 127.03

X6 326.32 68.51 134.04 68.05 177.53 60.83 254.52 281.72 131.57

X7 253.47 60.08 166.61 66.36 195.36 82.69 207.13 267.68 136.56

X8 315.11 59.79 166.57 69.06 172.68 45.39 164.07 203.86 127.03

X9 317.18 50.12 153.35 65.02 169.37 45.07 168.00 203.56 122.09

X10 182.04 99.11 119.78 104.57 167.64 89.31 164.06 138.66 130.84

X11 194.84 88.34 117.26 104.69 174.84 94.32 178.64 147.22 131.96

X12 1.10 0.32 0.59 0.38 0.89 -0.70 0.19 0.34 0.61

X13 0.09 0.06 0.08 0.06 0.08 -0.12 0.02 0.04 0.07

X14 176.20 85.58 141.82 94.70 176.86 89.11 145.94 195.84 125.35

X15 1.14 1.67 0.67 1.38 0.79 1.29 1.41 0.47 1.20

Source: Calculated from suppliment to Annual Survey of Industries 1980-81


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Table 2


Andhra Pradesh Assam Bihar Gujarat Haryana

Himachal Pradesh

Jammu & Kashmir Karnataka Kerala

Madhya Pradesh

X1 107.45 32.46 24.83 124.50 87.64 25.63 14.09 61.77 56.27 28.14

X2 12.94 4.62 8.47 13.06 16.25 1.19 0.25 7.21 20.98 2.09

X3 190.41 42.26 98.57 281.47 230.66 157.28 11.56 108.72 81.41 128.30

X4 53.82 14.12 78.96 69.34 100.40 17.08 0.48 29.80 71.25 22.38

X5 117.83 34.60 126.78 221.93 225.72 114.71 17.41 143.97 118.44 86.49

X6 115.99 33.52 123.57 237.27 242.61 160.44 16.73 160.94 113.75 101.25

X7 179.46 69.72 80.10 234.53 220.40 148.63 24.85 133.61 134.06 90.40

X8 129.73 86.46 111.44 299.04 261.65 188.53 24.98 165.37 107.91 128.87

X9 116.74 85.24 104.86 281.68 258.80 190.13 25.34 160.33 109.37 118.31

X10 0.05 0.16 0.09 0.08 0.09 0.09 0.17 0.12 0.10 0.08

X11 0.00 0.01 0.00 0.00 0.00 0.01 0.01 0.01 0.01 0.00

X12 -0.04 24.78 4.29 4.36 4.62 8.64 21.80 6.54 5.92 0.43

X13 -0.10 20.31 6.67 7.20 6.81 11.82 16.43 7.33 7.28 0.77

X14 0.24 0.55 0.37 0.43 0.47 0.25 0.89 0.46 0.46 0.30

X15 0.01 0.01 0.03 0.01 0.02 0.04 0.01 0.01 0.01 0.03

Contd……….


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Maharashtra Orissa Punjab Rajasthan Tamil Nadu Uttar Pradesh West Bengal Delhi Mean

X1 92.86 21.75 144.95 35.87 122.99 35.19 38.70 172.27 68.19

X2 11.86 2.20 29.07 2.30 26.30 8.28 14.78 71.53 14.08

X3 278.90 121.98 249.38 97.48 197.08 93.67 114.36 116.02 144.42

X4 83.63 28.99 117.40 14.65 98.94 51.74 102.54 113.09 59.37

X5 344.84 76.19 261.20 82.53 236.50 85.28 188.10 205.21 149.32

X6 369.10 79.89 261.83 87.25 238.63 84.58 189.17 236.12 158.48

X7 225.17 69.42 283.57 78.66 247.18 81.35 156.12 220.09 148.74

X8 402.38 105.87 252.36 98.60 275.82 92.95 120.96 270.92 173.55

X9 396.01 94.82 238.47 92.06 270.07 86.58 122.35 280.96 168.45

X10 0.11 0.07 0.08 0.08 0.11 0.08 0.08 0.18 0.10

X11 0.01 0.00 0.00 0.00 0.01 0.00 0.00 0.01 0.01

X12 0.69 3.56 0.02 2.58 8.30 2.06 -0.77 14.42 6.23

X13 0.80 7.58 0.04 4.51 10.01 3.68 -1.18 9.84 6.66

X14 0.50 0.23 0.44 0.36 0.45 0.36 0.38 0.93 0.45

X15 0.02 0.04 0.01 0.02 0.01 0.02 0.02 0.00 0.02



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Table 3


Andhra Pradesh Assam Bihar Chhattisgarh Gujarat Haryana

Himachal Pradesh

Jammu & Kashmir Jharkhand Karnataka Kerala

X1 121.72 34.76 11.60 39.93 179.37 136.71 53.60 22.35 34.58 86.13 98.46

X2 5.64 1.97 1.71 1.03 7.75 10.93 0.98 0.17 1.95 3.97 13.49

X3 85.23 28.93 6.61 120.86 335.19 155.93 129.99 9.36 112.35 121.95 52.39

X4 25.02 10.42 6.17 19.73 91.79 79.00 15.03 0.45 40.23 35.60 45.47

X5 158.90 54.16 11.54 120.26 259.58 261.60 96.34 38.68 263.47 178.61 162.95

X6 144.07 46.03 10.12 151.40 288.06 307.92 110.04 33.44 214.41 193.56 139.13

X7 198.19 70.03 12.69 75.73 236.69 228.57 100.38 40.59 97.66 155.27 161.26

X8 114.72 31.13 6.33 96.67 329.38 263.65 178.89 14.34 115.30 157.84 86.01

X9 118.40 31.83 6.63 99.40 290.95 268.91 184.65 13.97 105.90 161.41 93.15

X10 4.59 3.67 3.26 2.73 3.35 5.76 4.69 5.22 3.50 4.41 5.59

X11 2.43 1.92 1.75 1.44 1.52 3.01 2.48 2.61 1.65 2.31 3.11

X12 2.85 2.35 -2.20 0.73 1.56 4.93 6.27 -0.97 -1.59 2.06 3.23

X13 4.93 5.13 -5.27 2.15 4.33 6.87 10.60 -1.56 -4.06 3.74 4.37

X14 0.09 0.10 0.11 0.05 0.08 0.13 0.07 0.15 0.06 0.08 0.13

X15 1.63 1.94 1.32 7.04 4.34 2.65 5.64 0.97 7.55 3.29 1.24

Contd……….


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Madhya Pradesh Maharashtra Orissa Punjab Rajasthan

Tamil Nadu

Uttar Pradesh Uttaranchal

West Bengal Delhi mean

X1 32.59 120.06 30.25 193.88 60.87 197.44 35.90 53.57 50.34 160.68 83.56

X2 1.07 6.32 1.20 15.68 1.68 15.84 18.65 0.32 7.60 32.94 7.18

X3 47.67 165.88 60.53 94.05 51.26 127.72 40.71 66.55 62.47 48.89 91.64

X4 9.89 55.32 15.16 48.19 8.97 64.92 134.00 2.48 59.79 63.50 39.58

X5 67.02 299.41 85.96 231.53 70.96 289.40 53.40 134.99 167.39 149.47 150.27

X6 68.90 325.60 78.27 206.97 69.68 261.14 55.47 150.81 140.72 174.97 150.99

X7 58.28 201.90 52.87 240.83 69.04 295.48 51.95 81.02 114.46 143.77 127.94

X8 82.84 252.99 51.49 178.85 73.80 198.01 51.85 81.38 68.05 119.48 121.57

X9 86.18 264.87 46.17 194.59 74.52 204.56 52.57 84.81 68.16 134.28 123.14

X10 5.92 5.20 2.90 6.48 4.90 5.28 4.34 4.17 3.71 8.33 4.67

X11 3.16 2.79 1.33 3.62 2.54 2.80 2.26 2.23 1.91 4.80 2.46

X12 6.71 2.84 -1.01 6.82 3.36 3.09 3.48 0.31 -1.38 7.84 2.44

X13 8.92 4.27 -3.19 7.92 5.56 4.64 6.48 0.59 -3.04 6.86 3.34

X14 0.12 0.10 0.06 0.15 0.10 0.11 0.09 0.08 0.08 0.21 0.10

X15 3.40 3.21 4.65 1.13 1.96 1.50 2.64 2.89 2.88 0.71 2.98



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Table 4


Andhra Pradesh Assam Bihar Chhattisgarh Gujarat Haryana

Himachal Pradesh

Jammu & Kashmir Jharkhand Karnataka Kerala

X1 138.60 50.44 13.48 55.94 181.21 125.50 155.76 36.67 44.35 98.50 116.86

X2 8.30 3.81 2.71 1.94 10.57 13.96 3.69 0.37 3.39 5.92 20.22

X3 88.98 20.82 3.36 78.97 251.62 110.69 214.13 17.26 61.75 92.42 28.04

X4 36.28 10.71 4.60 18.70 99.98 83.87 34.57 1.20 32.16 37.86 33.05

X5 126.37 37.92 6.11 74.38 215.17 308.39 178.02 40.62 93.44 152.63 93.42

X6 113.15 33.48 4.90 86.39 236.04 307.07 211.57 33.07 89.16 157.75 78.26

X7 151.10 55.19 10.12 72.31 222.83 261.44 214.41 51.33 51.00 169.05 119.61

X8 60.16 19.33 2.81 65.42 165.39 130.71 266.15 25.22 56.36 76.46 24.47

X9 51.16 18.30 2.63 60.53 144.11 121.82 258.18 24.81 53.14 69.79 22.93

X10 2.74 3.76 3.38 3.35 2.66 4.78 5.03 5.91 3.69 3.35 3.53

X11 1.18 1.81 1.61 1.58 1.18 2.26 2.48 2.96 1.77 1.55 1.68

X12 0.87 1.60 1.45 1.51 1.09 1.66 2.97 3.05 1.48 1.25 0.70

X13 1.76 2.11 2.16 2.28 2.21 1.74 2.85 2.46 1.99 1.92 1.00

X14 2.54 4.71 7.04 2.89 3.47 5.15 2.93 5.34 2.83 3.48 13.06

X15 1.85 1.19 0.72 4.07 4.01 2.54 3.97 1.36 4.02 2.71 0.69

Contd……….


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Madhya Pradesh Maharashtra Orissa Punjab Rajasthan

Tamil Nadu

Uttar Pradesh Uttaranchal

West Bengal Delhi Mean

X1 33.56 118.61 34.47 254.20 69.06 271.48 37.78 161.98 52.17 108.19 102.80

X2 1.52 8.41 1.75 27.11 2.65 27.40 27.40 1.29 10.22 33.89 10.31

X3 27.30 112.97 112.19 75.85 35.18 126.55 23.46 198.46 41.11 20.48 82.93

X4 8.42 54.57 38.86 55.09 9.19 87.01 115.91 10.80 54.85 43.70 41.49

X5 44.92 182.36 78.93 181.95 56.13 291.05 37.53 278.43 69.56 60.31 124.17

X6 45.68 222.98 69.46 157.56 56.71 276.70 39.18 260.29 59.26 82.07 124.80

X7 44.08 152.61 63.18 232.66 65.79 316.64 43.23 272.76 72.34 75.29 129.38

X8 25.19 122.75 45.00 63.65 35.81 108.76 19.01 203.29 26.09 23.46 74.55

X9 23.56 115.62 40.03 58.77 33.10 96.51 17.10 197.13 23.80 22.81 69.32

X10 3.73 4.40 1.62 3.40 4.12 3.48 3.28 4.14 2.57 4.63 3.69

X11 1.77 2.11 0.73 1.59 1.94 1.57 1.50 2.04 1.19 2.29 1.75

X12 1.48 1.79 0.60 0.95 1.72 0.89 1.11 2.17 0.73 0.92 1.43

X13 1.98 2.03 1.95 1.42 2.11 1.35 1.76 2.53 1.46 0.95 1.91

X14 3.61 4.01 1.17 4.57 3.73 3.58 3.93 3.29 3.48 7.13 4.38

X15 2.35 2.75 9.39 0.86 1.47 1.34 1.79 3.54 2.27 0.55 2.54



107

during 1980-81. Careful categorizations of these factors highlight that more or less physical and

somewhat productivity indicators explain the variations in industrialization significantly.

Abysmally weak X13, X15 and X12 indicators bring to light the feeble magnitude of

profitability indicators in explaining the variations in industrialization in the Indian economy. In

the year 1990-91, once again the physical factors dominated in explaining the variation in

Table 5

Rotated component matrix

1980-81 1990-91 2001-02 2009-10

X1 0.644

(0.415) 0.698

(0.487) 0.777

(0.604) 0.850

(0.722)

X2 0.557

(0.310) 0.408

(0.167) 0.288

(0.083) 0.324

(0.105)

X3 0.900

(0.811) 0.882

(0.779) 0.849

(0.721) 0.833

(0.693)

X4 0.750

(0.562) 0.620

(0.385) 0.587

(0.344) 0.570

(0.325)

X5 0.937

(0.878) 0.928

(0.862) 0.943

(0.890) 0.952

(0.907)

X6 0.945

(0.893) 0.957

(0.915) 0.960

(0.922) 0.960

(0.922)

X7 0.857

(0.735) 0.872

(0.760) 0.891

(0.794) 0.956

(0.913)

X8 0.894

(0.798) 0.990

(0.981) 0.926

(0.858) 0.830

(0.689)

X9 0.898

(0.807) 0.987

(0.974) 0.917

(0.841) 0.811

(0.657)

X10 0.549

(0.301) -0.019

(0.00001) 0.116

(0.013) 0.097

(0.009)

X11 0.603

(0.363) -0.005

(0.00001) 0.090

(0.008) 0.071

(0.005)

X12 0.079

(0.006) -0.270 (0.073)

0.172 (0.030)

0.082 (0.007)

X13 -0.027 (0.001)

-0.240 (0.058)

0.211 (0.045)

0.069 (0.005)

X14 0.636

(0.404) 0.044

(0.002) -0.020

(0.00001) -0.156 (0.024)

X15 -0.079 (0.006)

0.010 (0.00001)

0.139 (0.019)

0.140 (0.020)

Note: Figures in parentheses indicate the squaring of the result to make the results positive and

meaningful

Source: Computed from tables 1 to 4

industrial development across Indian states. In the post liberalization period also, weights of X1

and X7 factors assumed increased importance. On the whole, only five variables i.e. X1, X5, X6,

X7 and X15 have shown a consistently increasing trend. Thus, it becomes clear from the table


108

that only physical indicators like number of factories, wages, total emoluments and invested

capital have gone up. Efficiency (output), profitability and productivity (gross and net value

added) indicators have shown a declining trend.

In order to find out the trend in disparities among the Indian states, a coefficient of

variation for all the four time periods has been calculated (table 7). The results show an

increasing trend over disparities. Especially, after liberalization the coefficient of variation has

increased at a greater pace. Coefficient of variation that increased by 4.25 percentage points from

48.35 percent in 1980-81 to 52.60 percent in 1990-91, increased by 7.2 percentage points each in

time periods 1990-91 to 2001-02 and 2001-02 to 2009-10. Further, in as many as 10 variables the

Table 6

Standardized Weights of industrial development

1980-81 1990-91 2001-02 2009-10

X1 0.41 0.49 0.60 0.72

X2 0.31 0.17 0.08 0.11

X3 0.81 0.78 0.72 0.69

X4 0.56 0.38 0.34 0.33

X5 0.88 0.86 0.89 0.91

X6 0.89 0.92 0.92 0.92

X7 0.73 0.76 0.79 0.91

X8 0.80 0.98 0.86 0.69

X9 0.81 0.97 0.84 0.66

X10 0.30 0.0004 0.01 0.01

X11 0.36 0.00002 0.01 0.01

X12 0.01 0.07 0.03 0.01

X13 0.001 0.06 0.04 0.005

X14 0.40 0.002 0.0004 0.02

X15 0.01 0.0001 0.02 0.02


coefficient of variation has experienced an increase. Only in case of five indicators i.e. X1, X2,

X4, X12 and X13 there has been a decrease in coefficient of variation over time (from 1980-81


109

to 2009-10). This has resulted into wide variations in industrial development of Indian states.

This may be due to the existence of more efficient industries which are localized into fewer

hands and benefited from the advantages of privatization and liberalization. Similarly, national

industrial policies of different states might have promoted few industries and their growth which

has resulted into fewer higher performances in few states in India. To portray differences among

states in terms of industrial disparities, weighted composite index have been developed.

Table 7

Trends in regional disparities in industrial development

1980-81 1990-91 2001-02 2009-10

X1 78.60 71.38 72.22 70.22

X2 123.41 118.34 115.21 101.98

X3 48.25 53.91 78.64 83.61

X4 79.45 64.77 86.96 78.37

X5 69.09 57.70 59.23 73.05

X6 67.89 57.76 61.06 73.86

X7 53.79 51.10 62.05 70.61

X8 62.85 56.51 70.60 92.41

X9 66.10 57.84 68.27 94.83

X10 23.04 35.16 28.61 25.59

X11 27.52 38.77 33.22 28.55

X12 103.48 116.89 121.12 46.71

X13 107.06 86.64 133.73 24.98

X14 32.23 42.99 37.38 55.42

X15 52.20 58.52 64.64 77.19

Coefficient of Variation 48.35 52.60 59.82 67.02


In the year 1980-81 five states that dominated the other states in terms of composite index

comprised of Maharashtra, Delhi, Gujarat, West Bengal and Haryana. Similarly, low performing

five states comprised of Jammu and Kashmir, Uttar Pardesh, Orissa, Assam and Rajasthan. In the

year 1990-91 among the top five ranked states, three states i.e. Maharashtra, Delhi and Gujarat


110

continued to rule the roost. But Haryana and West Bengal were replaced by Punjab and Tamil

Nadu among the top five slots in the year 1990-91. So far the low performing states are

concerned there was no change experienced overtime. In the year 2001-02 top performing states

comprised of Gujarat, Haryana, Maharashtra, Tamil Nadu and Punjab. By comparison to the year

1990-91, there was only one change experienced that was the inclusion of Haryana in the top

five states instead of Delhi which went to the seventh slot.

Table 8

COMPOSITE INDEX

1980-81 Rank 1990-91 Rank 2001-02 Rank 2009-10 Rank

Andhra Pradesh 1076.09 10 1024.53 8 988.40 9 785.03 9

Assam 767.36 15 448.81 17 324.35 19 265.18 19

Bihar 870.74 12 769.04 12 72.39 21 67.07 21

Chhattisgarh DNE DNE 739.13 13 530.27 11

Delhi 2494.65 2 1711.59 5 1056.73 7 486.68 14

Gujarat 2135.86 3 1774.91 3 2033.93 1 1541.54 4

Haryana 1859.30 5 1656.13 6 1736.58 2 1481.59 5

Himachal Pradesh 880.70 11 1024.46 9 899.65 10 1556.70 3

Jammu & Kashmir 495.63 18 174.99 18 179.80 20 251.63 20

Jharkhand DNE DNE 992.95 8 500.52 12

Karnataka 1256.97 9 986.19 10 1110.24 6 874.65 8

Kerala 1322.47 8 827.23 11 869.98 11 557.51 10

Madhya Pradesh 857.14 13 707.84 13 482.67 17 269.16 18

Maharashtra 2580.62 1 2206.86 1 1710.77 3 1107.96 7

Orissa 746.99 16 612.58 15 426.63 18 499.36 13

Punjab 1647.86 7 1838.80 2 1430.70 5 1119.65 6

Rajasthan 804.75 14 596.93 16 499.20 16 378.71 16

Tamil Nadu 1809.30 6 1732.38 4 1671.92 4 1614.33 1

Uttar Pradesh 745.56 17 625.82 14 513.78 15 373.96 17

Uttrakhand DNE DNE 666.20 14 1602.14 2

West Bengal 1964.94 4 1045.60 7 743.15 12 421.09 15 Note: DNE stands for ‘Did Not Exist’


Table 8 gives the weighted composite index of various states. Similarly, among the lowest

performing states Madhya Pardesh and Bihar led to the ouster of Uttar Pardesh and Rajasthan.

Perhaps the birth of Chhattisgarh and Jharkhand respectively, from the states of Madhya Pardesh

and Bihar led to their downfall. In 2009-10 top five states were Tamil Nadu, Uttrakhand,

Himachal Pardesh, Gujarat and Haryana. Punjab and Maharashtra that went down to sixth and

seventh slot respectively gave way to two hill states i.e. Uttrakhand and Himachal Pardesh.


111

Bihar, Jammu and Kashmir, Assam, Madhya Pardesh and Uttar Pardesh continued to be the low

performing states.

CONCLUSIONS

Broad conclusions emerging from the study are presented in the following points:

1. With reference to overall industrialization it emerges that industrialization in India has

hovered around a few states i.e. Gujarat, Maharashtra, Tamil Nadu, Haryana, Punjab and

Delhi. The neo-liberal regime has seen an increase in private investment in those areas

which yielded more profits than the regions desperately crying for investments.

2. Bihar, Jammu and Kashmir, Rajasthan, Orissa, Assam, Madhya Pardesh and Uttar

Pardesh have shown a dismal performance in terms of the majority of indicators.

3. State of Delhi has borne the brunt of liberalization the most in terms of industrialization.

4. It becomes imperative to note that only physical indicators like number of factories,

wages, total emoluments and invested capital have gone up. Efficiency, profitability and

productivity indicators have shown a declining trend in the time period covered by this

study.

5. The entrance of states like Himachal Pardesh and Uttrakhand in the top states in the

recent past has shown that there is a potential for balanced regional development in India.

To wind up the discussion, it is pertinent on the part of various states to identify

the key core competence industries and promote them while inefficient industries needs

to be restructured so that they can withstand the pressure of liberalization and

privatization. States which have very low productivity and efficiency indicators need to

diversify those economic activities that have potential for growth, while traditional

industries should be regenerated.

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