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A COMPOSITE APPROACH TO IDENTIFY KEY INDUSTRY SECTORS IN CMR 19

Oriental Geographer

Vol. 60, No. 1&2, 2016

Printed in March 2019

A COMPOSITE APPROACH TO IDENTIFY KEY

MANUFACTURING INDUSTRY SECTORS: A CASE OF

CHATTOGRAM* METRO REGION, BANGLADESH

MD. ANWAR HOSSAIN1

NURUL ISLAM NAZEM2

Abstract: A region provides certain facilities to some industries over others to grow,

which eventually makes the region industrially specialized. These industries termed as

‘key industries’ due to their importance and influences on the growth and development of

that region. Thus, to initiate sustainable industrial development strategies, first, it is

needed to explore the composition of the local economy and its key industry sectors. The

input-output table is a well-established technique to identify the key sectors but lack of

required and up to date data are major constraints to do such analysis in most of the

developing economy. On the other hand, a single indicator such as employment or

production-based methods sometimes do not capable to provide the dynamic behavior of

the economy. Considering the data and methodological limitations the present paper

illustrates an exercise using a composite method to identify the key industry sectors. The

Chattogram Metro Region (CMR) has been taken as a case for this exercise. Eleven

indicators and measures of the three groups were used to perform the analysis.

Correlation between the indicators and composite scores show that the result is highly

statistically significant. The study found that Readymade garment (RMG) is the most

important sector of the CMR’s economy followed by basic metals and chemical industry.

Keywords: Key sectors, Industry clusters, Composite analysis, City economy,

Chattogram

INTRODUCTION

Economic environment is the collected term of localization factors that affect location

behaviour of economic activities in the form of birth, growth, decline and disappearance

(Karlsson, 1999). These localization factors determine the comparative advantages of a

region and offer a platform for the development of a specialized industrial economy.

Economic development occurs in different functional regions with agglomerations and

specializations (Holbrook, 2004) and the specialized economy has higher ability to

attract, retain and expand human capital and infrastructure (Hossain and Nazem, 2016). A

few sectors tend to be localized or agglomerated for their efficient functionality

1 Md. Anwar Hossain is Assistant Professor, Department of Geography and Environment, University of Dhaka, Bangladesh

2 Dr. Nurul Islam Nazem is Professor, Department of Geography and Environment, University of Dhaka, Bangladesh * Chittagong has been renamed as Chattogram recently.

20 ORIENTAL GEOGRAPHER

(Marshall, 1920) in those specialized economic conditions (Karlsson, 1999). This

localization or agglomeration provides those industries (a) a creative environment

(Andersson, 1985), (b) a diversified supply of producer services, (c) an intra-regional

network for information flows about new production techniques, products, producers or

customers (Johansson and Wigren, 1996) and (d) a large differentiated supply of labour

categories (Karlsson, 1999). Agglomeration economies offer an abundance of positive

supply of externalities (Vernon, 1960) while specialization is directly connected with

regional development and economic growth (Dunn et al., 1960). Thus, understanding the

economic structure (Hofe and Bhatta, 2007) and the identification of ‘key sectors’ of a

region is a primary and necessary step to make effective strategic and policy frameworks.

Key industries are not always the largest, fastest growing or technologically most

intensive but defined as the industries which a region has its greatest competitive

advantages, measured by its contribution to local economic growth, contribution to

employment generation and also measured by the higher concentration status of the

industries in the country context. Since key sectors have high backward and forward

linkages with the rest of the economy, investment in these sectors is expected to enhance

economic development prospects (Hewings, 1982; Hirschaman, 1958 and McGilvray,

1977). The growth of key sectors promotes the development of a specialized economy

and encourages overall economic prosperity.

Despite the usefulness of identifying key sectors, especially for development planning,

such analysis has not been widely used in developing countries (Humavindu and Stage,

2013). Perhaps the considerable data requirement is the main reason behind the low level

of use of such analysis. The input-output model is an effective tool to analyze key sectors

but requires an intensive and large volume of data which may not be available in all the

developing countries. Even the input-output table is only compiled every ten years or so

by most developing countries if they have data at all. In addition, more extensive

analyzes require employment data, which are often lacking in developing countries.

Moreover, to determine the key sectors or to analyze the local economy, it is required

national level data as well as regional level data, which is absent in most cases or not up

to date. Thus, analysis of the economic base or key sectors by the input-output method or

any other complex data consuming methods is not an easy task to carry out in a

developing economy like Bangladesh.

Considering the above circumstances, this paper illustrates a composite method to

identify the key sectors of a regional economy. This approach may be an alternative to

determine the competitive industry clusters of an economy. The major objective of the

study is to make an exercise with a composite method to identify the key manufacturing

industry sectors of Chattogram Metro Region (CMR), Bangladesh.

MANUFACTURING INDUSTRIES IN CHATTOGRAM METRO REGION

Chattogram, the commercial capital of Bangladesh, is the second largest Metropolitan

area of the country (BBS 2014) and is one of the most competitive cities in Bangladesh

(CUS, 2010).Among the cities of Bangladesh, Chattogram has better facilities than many

other urban centres in the country, particularly the seaport facility and better transport

facilities. Chattogram is also known as industrial or business hub of Bangladesh which


contributed 7.93% to the country’s total GDP in 1999-2000 (BBS, 2005). About 70% of

the country’s total import and export take place through Chattogram (CCCI, 2009). There

were some 0.38 million industrial units in Chattogram and about 1.97 million persons

were engaged in the industrial activities in 2013 (BBS, 2016). The contribution of the

industry sector in Chattogram’s GDP was 35% in 1999-2000 (BBS, 2005). There were

1,822 macro manufacturing (registered) establishments in CMR where 484,800 people

were engaged up to December 2009. Sectoral composition of the manufacturing

industries in CMR indicates that only five manufacturing sectors are dominating both in

the number of factories and employment (Table 1). These are readymade garments

(RMG), food processing, textile, non-metallic mineral products and basic metals

industries. These sectors are also dominating at the national level and the share of these

industries were 80.73% to the macro manufacturing establishments and 89.80% to the

manufacturing employment in 2009, while, at CMR it was 71.30% and 91.24%

respectively (BBS, 2010a).

Table 1: Number of Industrial Establishments and Employment by Sectors in CMR, 2009

BSIC

Code Manufacturing Sectors

Establishments Employment

No. % No. %

10 Food products 296 16.25 19563 4.04

11 Beverages 8 0.44 643 0.13

12 Tobacco products 4 0.22 395 0.08

13 Textiles 192 10.54 69432 14.32

14 Readymade garments 595 32.66 327384 67.53

15 Leather and related products 50 2.74 10963 2.26

16 Wood and wood products 20 1.10 555 0.11

17 Paper and paper products 63 3.46 3462 0.71

18 Printing and reproduction of recorded media 35 1.92 1428 0.29

19 Coke and refined petroleum products 9 0.49 1340 0.28

20 Chemical and chemical products 60 3.29 8031 1.66

21 Pharmaceuticals, medicinal chemicals 12 0.66 1565 0.32

22 Rubber and plastic products 44 2.41 2906 0.60

23 Non-metallic mineral products 119 6.53 10552 2.18

24 Basic metals 97 5.32 15368 3.17

25 Fabricated metal products except machinery 59 3.24 2295 0.47

26 Computer, electronics and optical products 7 0.38 1398 0.29

27 Electrical equipment 24 1.32 2391 0.49

28 Machinery and equipment n.e.c 8 0.44 768 0.16

29 Motor vehicles, trailers and semi-trailers 7 0.38 343 0.07

30 Other transport equipment 19 1.04 1246 0.26

31 Furniture 69 3.79 1209 0.25

32 Other manufacturing industries 9 0.49 1326 0.27

33 Repair and installation of machinery and equipment 16 0.88 237 0.05

Total 1822 100 484800 100

Data source: BBS, 2010a; compiled by authors

There were 2,133 macro manufacturing factories in Chattogram District which covers an

area of about 5283 km2. However, in CMR, there were 1,822 factories that cover only

1,035 km2. About three-fourths of the manufacturing factories were in the core city area

[Chattogram City Corporation (CCC)] which was only 3.18% in terms of the areal extent


to Chattogram District. These figures reveal that there is an agglomeration pattern in the

distribution of the industries within the Chattogram region. Table 2 shows the distribution

of manufacturing industries according to different administrative settings of Chattogram

region and Figure 1 shows the distribution of manufacturing industries in CMR in 2009.

Figure 1: Distribution of Manufacturing Industries in CMR, 2009

Source: Compiled by authors, 2017


Table 2: Concentration of Manufacturing Industries by Different Administrative Regions, 2009

(data not mutually exclusive by administrative regions)

Administrative

Region

Area (sqkm) Establishment Employment

Actual

Area

% of Chattogram

District Total

Actual

Number

% of Chattogram

District Total

Actual

Number

% of Chattogram

District Total

CCC3 168.07 3.18 1577 73.93 438346 86.22

CMR (Study area) 1034.95 19.53 1822 85.42 484800 95.36

CSMA4 1044.91 19.78 1897 88.94 488335 96.06

Chattogram District 5283.00 100.00 2133 100.00 508381 100.00

Data source: Compiled by authors fromBBS, 2010a; BBS, 2004; BBS, 2007a&b; BBS 2008a&b

The growth trend shows a declining tendency in the growth of macro size industries in

Chattogram if compared with national growth trends including the leading RMG sector.

Hossain and Nazem (2016) found that 128 thousand employments were required to be

generated in CMR to follow the national growth trend during the period of 2005 – 2009.

On the other hand, to maintain similar regional competitiveness (based on national and

industry mix factors) the region was required to create 212 thousand employments. The

city created about 59 thousand new jobs from 2005 to 2009 which was only about 28% of

expected new jobs. This indicates that the city has failed to hold its competitive strength

and lost more than 150 thousand jobs during the period.

DATA AND METHOD

Data

This study demanded data on the number of factories, employment, fixed assets, gross

outputs and gross value edition by manufacturing sectors at regional as well as national

levels. Macro size5 manufacturing industry data were used in this study due to the lack of

regional level and historical data for all industries (both micro and macro size) for

longitudinal analysis. These data were collected from a Register published by the

Bangladesh Bureau of Statistics (BBS, 2010a). This Register includes all registered

macro-size industries with unique geocode (location), size of employment and year of

inception or establishment until 2010. Besides the existing published data, study

estimated data on fixed assets, gross output or value addition for Chattogram Metro

Region for 2010 due to lack of regional level data. The study estimated those data based

on the assumption that – ‘the working hours, productivity and cost of labour are equal

country-wide’. Following 11 indicators of three different groups were used to asses and

to rank the manufacturing sectors through composite analysis. The indicators were

selected based on how/ whether they represent the industry competitiveness and

availability of data.

A. Establishment related measures

a) Share of the sector to local manufacturing establishments

b) Share of the sector to national sectoral establishments

c) Growth rate of establishments in CMR

d) LQ of the sector in CMR by establishments

3 CCC: Chattogram city Corporation 4 CSMA: Chattogram Statistical Metropolitan Area 5 According to BBS industries which have equal or more than 10 employments are considered as macro-size industry.


B. Employment-related measures

e) Share of the sector to local manufacturing employment

f) Share of the sector to national sectoral employment

g) Growth rate of employment in CMR

h) LQ of the sector in CMR by employment

C. Economy-related indicators

i) Fixed capitals

j) Gross Output

k) Value addition

Empirical approach

Identification of major manufacturing sectors of the CMR’s economy

Firstly, industries were classified into broad classes of manufacturing industries (2-digit

level industries). These two-digit industry classes were termed as industry sectors. In

Chattogram, there were 24 such kind of macro manufacturing sector up to 2010.

Secondly, the study identified 10 major manufacturing sectors for composite analysis

based on their number and size of employment both at the local and national level. This

was done because some small sectors affect the analysis significantly though their

contribution to both local and national economy is very low. A simple rank-sum

technique was used to identify 10 leading sectors. The rank-sum score was calculated by

using the following equation:

Eq. 1: Si = ∑Rij

where,

Si = Score of ‘i’ industry

Rij = Rank of ‘i’ industry according to jth indicators

The sectors were ranked according to the ascending order of scores. The lowest score

indicates the top rank and the highest score indicates the lowest rank. Four indicators

were used to calculate the scores. These are (a) share of the sector to local manufacturing

establishments, (b) share of the sector to national manufacturing establishments, (c) share

of the sector to local manufacturing employment, and (d) share of the sector to national

manufacturing employment. Based on the rank-sum analysis following the 10

manufacturing sectors were chosen for composite analysis (Table 3).

Table 3: 10 Major Manufacturing Sectors of the CMR’s Economy

BSIC Code Manufacturing Sector Name Rank-sum

Score

Overall

Rank

14 Readymade garments 6 1 13 Manu. of textiles 8 2 10 Manu. of food products 11 3 23 Manu. of non-metallic mineral products 17 4 24 Manu. of basic metals 27 5 15 Manu. of leather and related products 28 6 17 Manu. of paper and paper products 35 7 20 Manu. of chemical and chemical products 36 8 22 Manu. of rubber and plastic products 37 9 25 Manu. of fabricated metal products except for machinery 37 10


Composite analysis

The study adopted the method proposed by Narain et al. (1991) to identify the sectoral

competitive performance to determine the key industry sectors or clusters. The score of

composite analysis is non-negative (lies between ‘0’ to ‘1’ where ‘0’ is the mean of the

distribution of indicators among the sectors and ‘1’ is the standard deviation of the

distribution). A smaller value of the composite score indicates the higher importance of

the sectors in the local economy and higher values indicate lower importance. The

composite score was calculated in two steps: (a) standardization of indicators values and

(b) calculation of the composite score.

a. Indicators value standardization

The parameters (indicators) included in this analysis were taken from various

distributions and these were recorded in different levels of measurements. Thus, the

values were needed to be transformed into standardized values. Equation 2 was used to

calculate the standardized values.

Eq. 2: Zij=Xij-Xj

Sij

where

Zij = standardize value of ‘j’ indicators of ‘i’ cluster

Xij = actual or row value of ‘j’ indicators of ‘i’ cluster

Xj = mean of the ‘j’ indicator

Sj = standard deviation of the ‘j’ indicator

The best value of all indicators was identified from the calculated standard values, which

denoted as ‘Zoj’. The best value is either the maximum value or minimum value of

indicator depending upon the direction of impact of the indicator on the ranking of

sectors. In this case, all the indicators considered for this analysis had positive impacts on

the results.

b. Calculation of composite score

Following formula was used to calculate the composite score for the major industry

sectors:

Eq. 3: Dj= 𝐶𝑖

𝐶

where

C = C + 2𝑆

where, C is the mean of Ci

S is the standard deviation of Ci

and

𝐶𝑖 = (Zij − Z0j)2

𝑘

𝑗=1

12

where

Zij = standardize value of ‘j’ indicators of ‘i’ industry

Z0j = best standardize value set of ‘j’ indicator


RESULTS AND DISCUSSION

Results of the composite analysis are discussed under four major headings based on

measure or indicator groups: a) sectoral rank by establishment related indicators, b)

sectoral rank by employment-related indicators, c) sectoral rank by economic indicators

and d) sectoral rank by all indicators. The study found a strong correlation between the

composite score and indicators (also with the indicator groups). The r value is 0.885,

0.948 and 0.875 for the correlation between overall score and establishment score,

employment score, economic score respectively. All these correlations are significant at

0.01 significant level. This result reveals that the composite score can significantly

present the strength and contribution of any sector to an economy.

Table 4: Correlation Between Major Indicator Groups and Scores

Indicator Groups Establishment

Score

Employment

Sore

Economic

Score

Overall

Score

Establishment score

Pearson Cor. 1 .786** .597 .885**

Sig. (2-tailed)

.007 .068 .001

N 10 10 10 10

Employment score

Pearson Cor. .786** 1 .784** .948**

Sig. (2-tailed) .007

.007 .000

N 10 10 10 10

Economic score

Pearson Cor. .597 .784** 1 .875**

Sig. (2-tailed) .068 .007 .001

N 10 10 10 10

Overall score

Pearson Cor. .885** .948** .875** 1

Sig. (2-tailed) .001 .000 .001

N 10 10 10 10

**. Correlation is significant at the 0.01 level (2-tailed).

Sectoral rank by establishment related indicators

Composite scores on the basis of establishment’s related indicators show that the three

most competitive sectors are RMG industries (1st), basic metal manufacturing industries

(2nd) and paper and paper production industries (3rd) (Table 5). In terms of share to the

local manufacturing sector, the RMG industry ranked top (32.66% to CMR’s total macro

manufacturing units). On the other hand, according to the share of CMR by sector to

national industrial units, basic metal industries occupied the top position (CMR’s share

was 24.62% to national) although it was 2nd among the sectors in terms of the share in

CMR. Paper and paper products sector occupied 2nd position according to share to

national. On the basis of growth of establishments two sectors occupied the top position.

These are paper and paper products industries and textile industries. According to LQ,

basic metals sector positioned top which is indicating that CMR has higher locational

advantages to the growth of the basic metal sectors than the others.


Table 5: Composite Indexes of Industry Clusters for All Indicators

BSIC

Code Major Manufacturing Sectors

Ranks by Final

Score

Final

Rank Establishment

s

Employment Economic

10 Manu. of food products 5 7 5 0.75 7

13 Manu. of textiles 8 6 3 0.71 4

14 Readymade garments 1 1 1 0.41 1

15 Manu. of leather and related products 9 3 6 0.73 5

17 Manu. of paper and paper products 3 5 10 0.74 6

20 Manu. of chemical and chemical products 4 4 4 0.68 3

22 Manu. of rubber and plastic products 7 8 7 0.80 8

23 Manu. of non-metallic mineral products 10 10 8 0.90 10

24 Manu. of basic metals 2 2 2 0.50 2

25 Manu. of fabricated metal products except

for machinery 6 9 9 0.83 9

Sectoral rank by employment-related indicators

Composite score on the basis of the employment-related indicators shows that the top

three key sectors are RMG industries (1st), basic metal manufacturing industries (2nd)

and leather and leather product industries (3rd) (Table 5).Leather sector ranked 10th

according to establishments but in terms of employment-related indicator, it is 3rd. This

reveals that the average size of leather factories by employment is larger than many other

sectors. The RMG industries occupied the top of the rank because this sector consumed

more than two-thirds of the manufacturing employment in Chattogram. Basic metal

manufacturing industries occupied the top position on the basis of the share to national

employment and also in employment LQ. The share of CMR in the basic metal sector is

about 44% to the national and the LQ value is about 3.11.

Sectoral rank by economic indicators

In terms of the amount of fixed capitals, textile industries occupied the top position

though this industry is 3rd on the basis of the gross output. RMG industry and basic metal

manufacturing sector positioned respectively 2nd and 3rd by the amount of fixed capitals.

In terms of gross output of the industries, the RMG positioned top because this is the

leading manufacturing sector in Chattogram by the number of factories and by

employment involvement. The basic metal manufacturing sector is positioned 2nd,

though it was ranked 5th on the basis of the number of industries and 3rd on the basis of

the employment size. It indicates that this sector is relatively less labour intensive than

the RMG and textile industries. On the basis of value addition, the basic metal

manufacturing industries ranks 1st, food products sector and chemical product sector

ranked 2nd and 3rd respectively. In overall ranking on the basis of economic indicators,

the RMG sector occupied the top position, basic metal manufacturing industry positioned

2nd and textile industry positioned 3rd respectively (Table 5). Gross output of RMG

industry was higher but the value addition ratio was found very low. Basic metal

manufacturing industry’s value addition capability is much higher than the other sectors.


Overall Score and Ranking of Key Sectors

RMG sector occupied the top position based on composite scores, followed by the basic

metal manufacturing industry(2nd) and the chemical production industry(3rd) (Table 5).

The score of the RMG sector is 0.41 means that this is the most dominant sector in the

region by generating employment or contribution to the local economy. The basic metal

manufacturing sector’s score is 0.50 means this industry is also the most potential sector

in CMR. This sector ranked 2nd by each of the indicator groups. Although the

contribution of this sector is not big like RMG sector, the study found that Chattogram is

mostly specialized for this sector.

CONCLUSIONS

Over the last few decades, regional industrial cluster development has gained significant

popularity as an effective economic development strategy to enhance the competitiveness

in a globalized economy. This approach is claimed as an ultimate policy panacea by some

of the policymakers and academician. Thus, identification of the influential key clusters

will help addressing the priorities to enhance the regional economic growth in the context

of globalization of markets.

Although there are several methods to determine the key clusters, due to lack of proper

and up to date data it was difficult to use such complex but effective methods especially

in the contexts of developing countries. This paper presented the output of an exercise

with composite method to determine the key industry clusters and found that the result of

the analysis was highly significant. Thus, the method may be an alternative approach to

use in such analysis where data availability is limited.

Based on the composite analysis, the study found that the economy of Chattogram Metro

Region is dominated by a few sectors: Readymade garments RMG), basic metal and

textile industries. These sectors dominate in generating employment and economic

prosperity. Little change in the competitiveness of these sectors affects the economy

significantly such as the RMG sector has failed to create more than 150 thousand new

jobs between 2005 and 2009 (Hossain and Nazem, 2016).As a result, the growth rate of

manufacturing employment decreased to lower than the national average at that time. It is

recommended that further study is required to identify the reasons behind the

agglomeration of some sectors and growth pattern of industry sectors to initiate effective

measures for cluster-based urban economic growth.

REFERENCES

Anderson, A.E. (1985). Creativity and Regional Development. In Papers of the Regional Science

Association. Vol. 56. Pp. 5-20

BBS (2004). Population Census-2001 National Report (Provisional). Dhaka: Bangladesh Bureau

of Statistics.

BBS (2005). 2004 Statistical Yearbook of Bangladesh. Dhaka: Bangladesh Bureau of Statistics.


BBS (2007a). Economic Census 2001 & 2003 National Report. Dhaka: Bangladesh Bureau of

Statistics.

BBS (2007b). Population Census-2001, Community Series, Zila: Chittagong. Dhaka: Bangladesh

Bureau of Statistics.

BBS (2007c). Register of Establishment, Size: 10+, (Updated Up to December 2005), Dhaka:

Bangladesh Bureau of Statistics.

BBS (2007d). Report on Bangladesh Census of Manufacturing Industries (CMI) 2001-02, Dhaka:


BBS (2008a). Economic Census 2001 & 2003 Zila Series: Chittagong. Dhaka: Bangladesh Bureau

of Statistics.

BBS (2008b). Population Census-2001, National Series, Volume-3, Urban Area Report, Dhaka:


BBS (2010a). Register of Establishment, Size: 10+, (Updated Up to December 2009), Dhaka:


BBS (2010b). 2009 Statistical Yearbook of Bangladesh. Dhaka: Bangladesh Bureau of Statistics.

BBS (2012). Population and Housing Census-2011, Community Series, Zila: Chittagong. Dhaka:


BBS (2014). Population and Housing Census 2011, National Volume-3: Urban Area Report,

Dhaka: Bangladesh Bureau of Statistics.

BBS (2016). Economic Census 2013 District Report: Chittagong. Dhaka: Bangladesh Bureau of

Statistics.

CCCI (2005), Economic Landscape of Chittagong. Chittagong: Chittagong Chamber of

Commerce & Industry. [Available at: http://www.chittagongchamber.com/economics_landscape.php]

CUS (2010). City Cluster Economy Development. Case Study: Dhaka Capital Region,

Bangladesh. Dhaka: Centre for Urban Studies.

Dunn, E.S., Lampard, E.E., Muth, R.F. and Perloff, H.S. (1960). Regions, Resources and

Economic Growth. Lincoln: University of Nebraska Press.

Hewings, G. J. D. (1982). The Empirical Identification of Key Sectors in an Economy: A Regional

Perspective. Dev Econ 20:173–195

Hirschman, A. (1958). The Strategy of Economic Development. Yale University Press, New

Haven

Hofe, R.V. and Bhatta, S. D. (2007). Method for Identifying Local and Domestic Industrial

Clusters using Interregional Commodity Trade Data. The Industrial Geographer. Volume 4,

Issue 2, pp. 1-24.

Holbrook, J. A. (2004). An Analysis of Industrial Clusters in Burnaby. Simon Fraser University:

Centre for Policy Research on Science and Technology.


Hossain, M.A and Nazem, N.I. (2016). Cluster Based Urban Economic Growth in the Chittagong

Metro Region: A Case of Manufacturing Industries. Oriental Geographer. 57 (1&2): 1-26.

Johansson, B. and Wigren, R. (1996). Production Milieu and Competitive Advantage. In

Infrastructure and the Complexity of Economic Development. Edited by Batten and Karlsson.

Berlin: Springer-Verlag: 187-211.

Karlsson, C. (1999). Spatial Industrial Dynamics in Sweden: Urban Growth Industries, Growth

and Change, 30: 184-212.

Marshall, A. (1920). Principles of Economics. Eighth edition. London: Macmillan.

McGilvray, J. (1977) Linkages, Key Sectors and Development Strategy. In: Leontief W (ed)

Structure, system and economic policy. Cambridge University Press, Cambridge, pp 49–56.

Narain, P., Rai, S.C.and Shanti, S. (1991). Statistical Evaluation of Development on Socio-

Economic Front. J. Ind. Soc. Agri/. Statist., 43:329-345.

Vernon, R. (1960). Metropolis 1985. Cambridge MA: Harvard University Press.

Vom Hofe, R. and Chen, K. (2006). Whether or Not Industrial Cluster: Conclusions or

Confusions? The Industrial Geographer, 4(1): 2-27.

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