Forecasting Regional Economic Potentials for Economic ... · made up of Noida, Ghaziabad, Greater Noida, etc. and adjoining industrial clusters. The region also forms a part of the

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Abstract

Industrial development is primarily linked with increasing investments, generating

employment and socio-economic development of the region. The task has gained a further

significance with the increased focus of governments on specialized economic regions

such as Special Economic Zones (SEZs) and Investment Regions (IRs) as the prime generators

of employment and revenue. This paper tries to address the challenges faced while planning

for these regions such as delineating the area and making the process inclusive. Ascertaining

the top performing and lagging industries of the region is done by using tools like shift

share analysis and delineation tools such as land suitability matrix for delineating the

region. With policy imperatives, which are backed by some quantitative findings, a

successful model for regional economic development can be proposed.

1. INTRODUCTION

Government of India in partnership with Japan under the Special Economic

Partnership Initiative (SEPI) plans to develop special economic regions/investment

regions and industrial clusters along the Dedicated Freight Corridor (DFC),

from Delhi to Mumbai covering an overall length of 1,483 km through six states

(ILFS, 2007: 3). With a view to optimize on the enhanced connectivity being

offered for freight movement, Government of India has further proposed

establishing, promoting and facilitating ‘Delhi Mumbai Industrial Corridor’ or

DMIC along the Western Dedicated Freight Corridor (DFC) between Delhi and

Mumbai. Along this corridor seven investment regions and thirteen industrial

clusters have been identified, which are to be developed with world class physical

and social infrastructure facilities at different places.

Depending upon the inherent potential of the region, an economic activity is

planned to take shape so that it becomes a driver for the economic growth of

the region in terms of employment, investment and other related activities. A

threshold area limit is kept for the planned regions to be developed, 200 sq km

for an investment region and 100 sq km for an industrial cluster.

The present paper aims at building a successful model for industrial development

in this specialized region. The various tasks range from delineating to finding

out an optimum mix, and thereby finding potential nodes for industrial

Purushottam Kesar, Senior Research Associate, Centre for Urban Governance, AdministrativeStaff College of India, Hyderabad. Email: [email protected]. S Chattopadhyay, Associate Professor, Department of Architecture and RegionalPlanning, Indian Institute of Technology, Kharagpur. Email: [email protected]

Forecasting Regional Economic Potentials for

Economic Regions - Special Economic Zones and

Investment Regions

Purushottam Kesar and Prof. S Chattopadhyay


Institute of Town Planners, India Journal 8 - 1, 41 - 55, January - March 2011

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development, assessing the various infrastructural needs of the region. However,

in our paper, we are emphasizing the methodology adopted for finding out the

optimum mix- lagging and top performing industries and regional level

delineation. A few similar models are also studied like Incheon Free Economic

Zone (IFEZ) in South Korea and Pudong Free Economic Zone in China to assess

their roadmap for infrastructural development

Among the identified seven nodes for development of investment regions, the

one such area identified is Dadri-Noida-Ghaziabad node. This node attains

special significance in terms of its proximity to the highly urbanized regions

made up of Noida, Ghaziabad, Greater Noida, etc. and adjoining industrial

clusters. The region also forms a part of the National Capital Region (NCR).

Also, in terms of the administrative jurisdictions, the proposed region, which is

yet to be delineated, forms part of two districts: Gautam Budha Nagar (Dadri,

Noida) and Ghaziabad (see Fig. 1).

In realizing tasks for mix and delineation, intervention seemed important because

land as a resource is scarce and non-renewable. Similarly the economic activities

have also to be planned, keeping in view the regional competitiveness of place

Fig. 1 Ghaziabad and Part of District Gautam Budha Nagar.



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in providing particular goods and services. We adopt two methodologies for

arriving at our goal. Firstly, evolving a Land Suitability Matrix and secondly, a

Shift Share Analysis to find out the regionally competitive goods and services

for the place.

2. PHYSICAL AND DEMOGRAPHIC AND ECONOMIC CHARACTERISTICS OF

THE REGION

Ghaziabad district consists of four sub–districts Ghaziabad, Modinagar, Hapur

and Garhmukteshwar with a total area of 1,148 sq km. The Ghaziabad sub-

district takes a lead in the percentage share of total population of the district

(16,29,510 or 49.52 percent). The highest percentage of urban population (76

percent) and highest percentage of literacy rate of 74.4 percent (see Table 1).

In terms of workforce mix, the percentage of main workers oscillates around

25 percent for all the sub districts with percentage of marginal workers approx

2-2.5 percent for Hapur, Ghaziabad and Modinagar urban areas and increasing

upto 8 percent of the total workers in the case of Garhmukteshwar (Table 2),

which has primarily agriculture as the major employment activity. Ghaziabad

sub-region has an increased edge in terms of the factors which are particularly

responsible for industrial clustering like workforce participation i.e. main workers,

Table 1 Demographic Characteristics of Ghaziabad District

Source : Census of India, 2001

Sub District Modi Nagar Ghaziabad Hapur Garhmukteshwar

Population 5,55,420 16,29,510 7,73,895 3,31,757

Urban Population (%) 16.88 49.52 23.52 10.08

Literacy Rate (%) 69.4 74.4 64.8 58.2

Table 2 Work force participation, Ghaziabad District

Source : Census of India, 2001

Name Rural/Urban “% age Main “% age Marginal ‘Work

Workers’’ Worker’’ Participation

Rate’

Hapur Urban 23.7 2 25.6

Ghaziabad Urban 24.8 2.3 27

Modinagar Urban 24.2 2.4 26.6

Garhmukteshwar Urban 24.9 4.6 29.5

Ghaziabad Rural 23.2 5.4 28.7

Hapur Rural 23.4 5.4 28.8

Modinagar Rural 25.4 7.7 33.1

Garhmukteshwar Rural 24.9 8.8 33.6



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metro proximity and infrastructure, although the qualitative and quantitative

aspects of infrastructure are debatable.

Another part of the region, which intended to be part of the proposed investment

region, falls in Gautam Budha Nagar district, which consists of three major sub

districts Dadri, Jewar and Gautam Budha Nagar. District Gautam Budha Nagar

was formed in 1997 by taking entire Dadri tehsil, 6 villages of Hapur tehsil both

from Ghaziabad, 152 villages and 3 towns of Sikandrabad, 104 villages and 3

towns of Khurja tehsil (both from Bulandshahr). A closer look at the major

demographic features points towards the lead in almost all sub heads for Dadri

sub district (Table 3). This is primarily because of NOIDA (New Okhla Industrial

Development Authority) census town, which is a planned township with an

ultimate size of 10,000 small scale industrial units and an employment to 41,000

industrial workers. It is planned as an integrated township for 375,000 workers

and has already reached a population of 3,98,448 as per Census 2001.

There are 1,391 registered industrial units in Ghaziabad district, and a majority

is small scale industrial units (SSI). Of the total main workers, the trend has

been towards increasing employment percentage in industries. Employment in

industries has shown a 100 percent increase from 54,558 to 1,10,411 over the

previous decade. In terms of the mix or composition, the major employment

generating sectors have been manufacturing, trade and commerce, transport

and communication and construction respectively. A sizeable percentage (80,956

or 26.8 percent) of the total enumerated employees is employed in the service

sector made up of Information Technology Enabled Services or ITES, finance

and real estate.

In the case of Gautam Budha Nagar, there are 1,060 SSI and 34 registered

heavy industries. The majority of industries under SSI are hosiery and garments,

basic metal industries, misc. mfg. industries, and paper and printing industries

respectively, whereas under category of heavy industries, majority are electrical

machinery and apparatus, machinery and part, paper and printing and hosiery

and garments. (Source: District Industries Centre -DIC).

Table 3 Demographic Characteristics of Gautam Budha Nagar District

Source : Census of India

Sub District Dadri Gautam Budha Nagar Jewar

Population 6,92,259 2,59,445 2,50,326

Urban Popn. (%) 53.9 7.5 22.7

Literacy Rate (%) 73.9 63.2 59.7

Work Participation (%) 31.1 29.8 28.3

Main Workers (%) 27.6 23.7 22.5



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To get a more comprehensive outlook of the industrial scenario and employmentscenario, the data from the Fourth Economic Census and Fifth Economic Census

is taken (Central Statistical Organization or CSO).

3. RESEARCH APPROACH

In evaluating the region for proposing the industrial mix, the following steps

are followed:

• Forming a land Suitability Matrix;

• Evaluating the results of the Fourth Economic Census and Fifth Economic

Census; and

• Finding out the performance of different industrial sectors in terms of

employment by using Shift Share Analysis.

3.1 Forming a Land Suitability Matrix

A land suitability matrix is prepared by overlapping of land use sensitivity, air

pollution sensitivity, water pollution sensitivity and ground water pollution

sensitivity maps. The whole region is divided into 2 x 2 km square cells and

sensitivities are marked from a very high with ‘0’ score to lowest sensitivity for

Fig. 2 Approach



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score ‘3’ (see Fig. 3, 4 and 5 for details). The lower the score the less is the

brightness of the cell and vice versa.

In case of land use sensitivity, flood plains, prime agricultural land, river beds,

basins, forests and protected regions, urbanized regions and legally restricted

land uses are given a highest sensitivity ‘0’. While the wastelands, which can

be scrub land, marshy/swampy land, ravenous land, sand dunes/sandy tracts,

salt affected land and water logged lands are given a highest priority ‘3’. The

wastelands can be suitable for industrial development.

Similarly in the case of air pollution sensitivity, high sensitivity areas ‘1’ are not

fit for locating any air polluting industry. A close inspection of the region, ‘1’s

are mostly fall under the close congeniality in urbanized parts of the district

and the prime agricultural lands. In view of the limited pollution receiving

potential and urbanized nature, the region is not suitable for siting highly air

polluting industries like large scale cement, fertilizers, petrochemicals, and

integrate iron and steel industries, etc. (Fig. 3).

In the case of surface water pollution sensitivity, a majority of the region falls

between high to medium sensitivity, a few medium sensitivity regions are

located around Hapur. As far as ground water pollution sensitivity is concerned,

factors like ground water table, ground water potential and contamination risk

Fig. 3 Air Pollution Sensitivity Matrix for the Region



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Fig. 4 Surface Water Pollution Sensitivity Matrix for the Region

Fig. 5 Ground Water Pollution Sensitivity Matrix for the Region



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Fig.6 Resultant Land Suitability Matrix - Region

Fig.7 Proposed Delineated Region



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are evaluated. There are almost no areas with a very low sensitivity, but the

region can be equally divided among the high and medium potential sensitivities,

the high sensitivities confined along the urbanized corridor (Fig. 4 and 5).

Now we overlap all these grid maps and obtain a land suitability matrix with

brightest parts of 3s being the high potential sites and the darkest 1s being the

more fragile sites. The land use sensitivity map was prepared taking a due

cognizance of land uses for different parts of the district. Similarly potentialities

were earmarked for access to highways, railways, infrastructure like power

and water availability are done and overlapped to get the final grid scores/

potentialities (see Fig. 6 for details).

The proposed region, which can suitably fit into the formation of an investment

region of around 200 sq km falls between the NH-91 and the Upper Ganga

Canal in close proximity to the proposed alignment of dedicated freight corridor

as shown in Fig. 7.

3.2 Industry Mix

Industrial mix for the region is another important task, which needs to be

addressed. For arriving at the decisions, Shift Share Analysis is done to find out

the best industrial mix for the region under study based upon the regional

competitiveness of different performing sectors.

Table 4 Consolidated Results of 4th and 5th Economic Census.

Source : Economic Census, MoSPI, CSO

Activity Employment Employment Region Region

E(t) 2005 E(t-1) 1997 e(t)-2005 e(t-1)-1997

Mining and Quarrying 5,81,830 452900 19,591 -NA-

Manufacturing 2,54,81,715 1,54,81,300 2,17,511 1,82,302

Electricity, Gas and Water 4,37,456 4,27,700 1,397 1,199

Construction 7,44,288 4,92,600 2,070 1,438

Wholesale trade 21,29,451 13,46,600 7,487 4,720

Retail trade 2,51,36,113 73,61,000 1,34,812 26,923

Hospitality 37,80,025 19,13,900 10,545 6,146

Transport 28,99,832 11,94,600 8,174 4,970

Communications 14,92,902 6,94,700 9,544 2,999

Financial, insurance, real 44,21,211 25,10,700 46,882 6,084

estate and business services

Community, Social and 2,09,90,424 1,75,59,100 37,807 29,543

Personal Services

Other (unspecified) activities 5,459 1,800 13,871 11,176

Total 8,81,00,706 4,94,36,900 5,09,691 2,77,500



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Data from the Fourth (1997) and Fifth Economic Census (2005) were analyzed

and interesting results were obtained from it. Four sets of data are computed

as classified in the Economic Census according to activity classification done

under the National Industrial Classification (NIC) for computing the results:

Referring to Table 4, we can obtain:

E(t) or Country(t) = Employment in a particular sector in 2005, at country

level

E(t-1) or Country(t-1) = Employment in a particular sector in 1997, at country

level

e(t) or (i)Local (t) = Employment in a particular sector(ith) in 2005, for

the region under study

e(t-1) or (i)Local(t-1) = Employment in a particular sector(ith) in n 1997, for

the region under study

The most visible results include a positive shift in employment, finance and real

estate, retail, and in communication services vis-à-vis manufacturing sector,

which was the sole provider of employment in the previous decade.

The first step in analyzing the combined data is computing National Share or

NS.

National Share (NS): NS = (i)Local (t-1) * Country(t)/Country(t-1) Net Gain/

Loss =[(i)Local(t-1)] - NS

In terms of National Share i.e. the growth in a particular sector and the

Table 5 Top Performing Sectors as Compared to National Growth Rates


Sector Region e(t) NS Net Gain/Loss Rank

Retail 1,34,812 47,979 86833 1

Finan. And RE 46,882 10,842 36040 2

Mining 19,591 NA 19591 3

Communications 9,544 5,344 4200 4

Hospitality 10,545 10,953 -408 5

Construction 2,070 2,563 -493 6

Transportation 8,174 8,857 -683 7

Electrical goods and services. 1,397 2,137 -740 8

W wholesale Trade 7,487 8,411 -924 9

Other 13,871 19,917 -6046 10

Community AND Social Service 37,807 52,648 -14841 11

Manufacture 2,17,511 3,24,877 -107366 12



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corresponding growth of the same sector at the country level. For example,

manufacturing sector has grown at the same rate as the overall growth in

employment at the country level, there would have been an addition of 3,24,877

manufacturing jobs (Table 5), but in 2005, in the region under study, the

employment in manufacturing stood at 2,17,511 i.e. there was a net loss of

1,07,366 manufacturing jobs which shifted to some other region or regions.

National Share represents the employment levels in a particular sector in terms

of the total national employment growth. A smaller value of NS as compared to

the Region e (t) value points towards a growth in the sector more than the

national employment growth and vice-versa. From these data a distinction

between performing and non performing sectors can be made out, and sectors

in which particular emphasis in terms of policy changes and incentives are

required could be sorted out. Net gain or loss of employment can be computed

in terms of a particular sector which is the difference between NS and Region

e(t) values and future targets can be set. The top performing sector apart

from retail is finance and real estate, communication, hospitality and construction

(Fig 8). The manufacturing sector has shown the lowest growth (high negative

value for net gain/loss: -1,07,366). The community and social sector include

people involved in administration, sanitation, sporting, recreational activities,

etc.

Industry Mix: IM = [ (i)Local (t-1) * (i)Country(t)/ (i) Country(t-1) ] - NS

In terms of industry mix (IM) i.e. the sectoral level employment changes in the

country and the consequent change at the regional level in the particular sector.

Here the results represent overall employment changes in a particular sector at

Fig. 8 Results of Shift Share Analysis – NS



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Fig. 9 Results of Shift Share Analysis – IM

the regional level compared to the sectoral growth of the same sector at the

national level. For example, in the case of manufacturing sector, there was a

net loss of 24,814 jobs (Table 6). In fact, manufacturing sector did not grow at

the rate at which the manufacturing sector grew at the country level. If, it had

grown at the same level, the resultant IM value would have been zero. The

results represent a net gain or loss of employment compared to the sectoral

growth at the national level unlike the overall growth at the national level,

which is taken as a yardstick in case of computing National Share values.

Visible results for sectoral level competitiveness of the region points towards

retail, hospitality, transportation, communications, finance and real estate

and construction (Fig. 9).

3.3 Regional Shift (RS)

(i) Local (t-1) * [(i) Local (t)/ (i) Local (t-1) - (i) Country (t)/ (i) Country (t-

1)]

The difference between the national share and industry mix is the regional

shift. As the equation for Regional Shift (RS) represents a net gain or loss for a

particular sector at the region level, the values obtained for RS for different

sectors represent the net gain or loss of employment. The regional shift indicates

that local conditions were responsible for the region’s competitive position in

particular sectors where there are high positive values. The gain or loss is over

and above the national growth and industry mix gains or losses. Factors attributed

to RS values are local competitiveness factors like infrastructure, skill,

technological advantage, local tax structures, etc. (Table 7).

A comparison of the results helps us in knowing the most competitive sector at

the regional level. The regional shift is an excellent measure to get an idea of



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Table 6 Top Performing Sectors - IM


Activity Region e(t) IM (Gain) Rank

Retail 1,34,812 43,956.8 1

Other 13,871 13,977.8 2

Transportation 8,174 3,207.5 3

Hospitality 10,545 1,185.9 4

Communications. 9,544 1,100.3 5

Finance & Real Estate 46,882 -128.6 6

Construction 2,070 -389.9 7

Electrical services and goods 1,397 -910.4 8

Wholesale Trade 7,487 -947.4 9

Community & Social Service 37,807 -17,331.9 10

Mining 19,591 -19,591.0 11

Manufacturing 2,17,511 -24,814.3 12

most competitive sector for a region and consequently it can guide the policy

level changes for industrial development. Regionally the most completive sectors

are finance and real estate, communications, community and social services,

and electrical goods and services. Whereas the performance of manufacturing

sector is abysmal as far as the regional employment generation is concerned

vis-à-vis national growth levels (see Fig. 10).

Table 7 Top performing sectors - RS

Activity Region e(t) RS(Gain) Rank

Retail 1,34,812 42876.2 1

Finance & Real Estate 46,882 36168.4 2

Communications 9,544 3099.2 3

Community & Social Service 37,807 2490.8 4

Electrical services and goods 1,397 170.7 5

W trade 7,487 23.0 6

Mining 19,591 0.0 7

Construction 2,070 -102.7 8

Hospitality 10,545 -1593.6 9

Transportation 8,174 -3890.4 10

Other 13,871 -20023.3 11

Manufacturing 2,17,511 -82552.1 12



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4. CONCLUSIONS

The shift-share analysis provides a simple and straightforward approach to

separating out the national and industrial contributions to local economic growth.

It is useful for targeting industries that might offer significant future growth

opportunities.

From three levels of discussion at National Share (NS), Industrial Mix (IM) and

Regional Shift (RS), we can get a mix of industries which is most suited for the

region. These industries would be able to utilize the maximum potential of the

region in terms of its competitive edge. Common sectors in which the region is

performing satisfactorily are retail, finance and real estate, and

telecommunication services (see Table 8). The region is also performing most

unsatisfactorily in sectors like manufacturing and electrical services and goods.

Therefore, when planning for an investment region, due cognizance of the top

performing sectors, which can be drivers for the employment at the regional

Fig. 10 Results of Shift Share Analysis – RS

Table 8. Comparative Results

Comparative Results : Top Performing Sectoral Industries

National Share (NS) Industry Mix (IM) Regional Share (RS)

Finance & Real Estate Other Finance & Real Estate

Mining Transportation Communications

Communications Hospitality Community & Social Service

Hospitality Communications. Electrical services and goods

Construction Finance & Real Estate Wholesale Trade

Transportation Construction Mining



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level, must be taken. Manufacturing and electrical goods sectors which have

been the mainstay for the region for a long time, needs special emphasis in

terms of fiscal and policy level incentives.

Comparative results point towards the two sectors which have emerged in all

the three analysis (NS, IM and RS) – finance and real estate and communications.

Other sectors which are partially visible in at least two analysis (NS, IM or RS)

are hospitality, construction and transportation sector.

REFERENCES

Bascetin (1991) A decision support system using analytical hierarchy process (AHP) for the

optimal environmental reclamation of an open pit mine

Bendavid-Val, A. Regional and Local Economic Analysis for Practitioners, Prager Publishers,

Westport, CT.

Blair, J.P. (1995) Local Economic Development: Analysis and Practice, Sage, Thousand

Oaks, CA.

Central Statistical Organization (1997) Fourth Economic Census-1997, Government of India,

Ministry of Statistics and Programme Implementation.

Central Statistical Organization (2005) Fifth Economic Census-2005, Government of India,

Ministry of Statistics and Programme Implementation.

Central Pollution Control Board Zoning Atlas for Siting of Industries (Based on EnvironmentalConsiderations), Central Pollution Control Board, New Delhi.

Chongjin K. A study on the development plan of Incheon free economic zone, Korea: bybased on a comparison to a free economic zone in Pudong, China. University of Oregon

Friedman, J. (1982) Regional Development and Planning, Elsevier, London

IFDC (2007) Executive Summary, June 2007, Second Draft Paper, Delhi Mumbai IndustrialCorridor (DMIC), Infrastructure Development Corporation Limited, Mumbai.

Ghaziabad Development Authority Master Plan for Ghaziabad -2021, Ghaziabad Development

Authority, Ghaziabad.

Government of India (2007) Economic Survey 2007, Ministry of Finance Economic Division,

Government of India Press, New Delhi.

Houston, D.B. (1967) The shift and share analysis of regional growth: a critique, SouthernEconomic Journal, Vol.33, No.4, pp.577-581.

Leontief, W. (1986) Input Output Economics, Second Edition. Oxford University Press,

London.

Lyndhurst, C. and Walker, D. Location Dynamics of Manufacturing Activity

New Okhla Development Authority Master Plan for NOIDA - 2021, New Okhla Development

Authority, Noida.

Serck, J. (1970) Optimal Patterns of Location, North-Holland Company, Amsterdam.

Stevens, Benjamin H. and Craig L. Moore (1980) A critical review of the literature on shift-

share as a forecasting technique, Journal of Regional Science, Vol.20, No.4, pp.419-437.



Documents

Forecasting Regional Economic Potentials for Economic ... · made up of Noida, Ghaziabad, Greater Noida, etc. and adjoining industrial clusters. The region also forms a part of the