A COMPOSITE APPROACH TO IDENTIFY KEY MANUFACTURING...
Transcript of A COMPOSITE APPROACH TO IDENTIFY KEY MANUFACTURING...
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
A COMPOSITE APPROACH TO IDENTIFY KEY INDUSTRY SECTORS IN CMR 21
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
22 ORIENTAL GEOGRAPHER
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
A COMPOSITE APPROACH TO IDENTIFY KEY INDUSTRY SECTORS IN CMR 23
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.
24 ORIENTAL GEOGRAPHER
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
A COMPOSITE APPROACH TO IDENTIFY KEY INDUSTRY SECTORS IN CMR 25
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
26 ORIENTAL GEOGRAPHER
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.
A COMPOSITE APPROACH TO IDENTIFY KEY INDUSTRY SECTORS IN CMR 27
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.
28 ORIENTAL GEOGRAPHER
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.
A COMPOSITE APPROACH TO IDENTIFY KEY INDUSTRY SECTORS IN CMR 29
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:
Bangladesh Bureau of Statistics.
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:
Bangladesh Bureau of Statistics.
BBS (2010a). Register of Establishment, Size: 10+, (Updated Up to December 2009), Dhaka:
Bangladesh Bureau of Statistics.
BBS (2010b). 2009 Statistical Yearbook of Bangladesh. Dhaka: Bangladesh Bureau of Statistics.
BBS (2012). Population and Housing Census-2011, Community Series, Zila: Chittagong. Dhaka:
Bangladesh Bureau of Statistics.
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.
30 ORIENTAL GEOGRAPHER
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.