Local Employment Impact of Growth Centres: Evidence from India

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2006 43: 2205Urban StudKala Seetharam Sridhar

Local Employment Impact of Growth Centres: Evidence from India

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Local Employment Impact of Growth Centres:Evidence from India

Kala Seetharam Sridhar

[Paper first received, August 2004; in final form, March 2006]

Summary. Evidence regarding non-tax incentives is sparse. To promote industrialisation in thebackward areas of the country, growth centres providing infrastructure incentives to enable thestates to attract industries, were set up by India in 1988. The impact of GCs on unemployment isexamined, accounting for the simultaneity of GC status. The findings are that the change inpopulation of a district and its growth potential are significant in determining actual GC status.The impact of demographic and socioeconomic factors on changes in a district’s unemployment, isstudied. The novel finding for a developing country is that it is not demand for labour, but thesupply of labour, represented by population, that is the constraint in reducing the unemployment rate.

Introduction

In India, there are substantial interstate andintrastate disparities in the level of growth ofoutput and per capita income. Since, despiteplanning, regional disparities in India havenot reduced over the years, the governmentof India introduced a ‘growth centres’ (GCs)programme in June 1988 to give impetus toindustrialisation in backward regions (seehttp://dipp.nic.in/growth.htm). Under thisprogramme, 71 GCs were set up throughoutthe country and were to be allotted to thevarious states on the basis of a variety ofcriteria consisting of area, population andthe extent of industrial backwardness. TheseGCs provide basic industrial infrastructurelike power, water, telecoms, and banking toenable the states to attract industries.

There is a lot of scepticism in Indianacademic and policy circles regarding GCsand their effectiveness in attracting firms andtheir jobs. Views have varied from a percep-tion of the programme having been a colossalfailure in attracting firms to one that strongly

believes in their effectiveness because of theinfrastructure incentives available to firmsthat locate there. In this study, the localemployment impacts of GCs are evaluated.

Importance of and Motivation for Research

Place-oriented policies such as GCs or specialeconomic zones (SEZs) are geographicalregions that have economic laws differentfrom a country’s typical economic laws.Usually the goal is an increase in privateinvestment. The chosen areas are developedprimarily as experimental, controlled enclaves.SEZs have been established in several countriesin the global South, including China, thePhilippines and North Korea, which have allattempted this to a degree. The originalpurpose was to use these geographically tar-geted areas as a potential economic policyalternative to attract investment, which wouldprovide the seed money and technology to mod-ernise their economy, as McKenney (1993)points out in the case of China.

Urban Studies, Vol. 43, No. 12, 2205–2235, November 2006

Kala Seetharam Sridhar is in the National Institute of Public Finance and Policy, 18/2 Satsang Vihar Marg, Special InstitutionalArea, New Delhi 110 067, India. Fax: þ91 11 26852548. E-mail: [email protected].

0042-0980 Print=1360-063X Online=06=122205–31 # 2006 The Editors of Urban Studies

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In China, for example, in 1980, the term‘special export zones’ was changed to ‘specialeconomic zones’ to reflect their connection toother parts of China’s economy and to thebroad range of activities that the SEZs couldundertake to encourage foreign investmentand economic growth. Following China, Indiarecently took the step of renaming its exportzones as special economic zones (http://sezindia.nic.in). Further, the government ofIndia is actively considering merging the SEZand the GCs in certain areas where GCs exist.

Consistent with the idea of place-orientedpolicies, India’s GCs are similar to the SEZsof China except that GCs offer primarily infra-structure incentives for investors that locatethere.

While the regional development literatureis replete with tax incentives, there are fewstudies that deal with non-tax incentives(see Fisher and Peters, 1998; Lall andChakravorty, 2005). Studies summarise theeffect of taxes, holding public services con-stant (see Bartik, 1991). Frequently, tax incen-tives are defined in the context of publicservices—for example, Garcia-Mila andMcGuire (2002), define tax incentives as ‘atax rate lower than the marginal benefit ofthe public goods and services provided tothe firm’.

This study is an attempt to fill this gap in theliterature and uses empirical evidence fromIndia’s GCs programme, as representative ofinfrastructure incentives offered to firms bystate and local governments. The GC pro-gramme is important in the context of compe-tition for firms among the Indian states inthe post-liberalisation (1991) period. Variousstates in India, until recently, had been offer-ing tax incentives to investors in order to per-suade them to locate in their states, makeinvestments and create employment. The pro-gressive south Indian state Karnataka’s ChiefMinister had been attempting to woo thehouse of Tatas to explore business opportu-nities in the state. More recently, Karnataka(Bangalore) and another south Indian state,Andhra Pradesh (Hyderabad), were fiercelycompeting with each other to get Microsoft’snewest India facility to their state, with

Andhra Pradesh (Hyderabad) finally‘winning’ the race. In terms of actual taxincentives also, there have been severalinstances of generous abatements during thepast decade in India.

A conference of state chief ministers andthe Union Finance Minister decided inNovember 1999 to stop this tax war amongthe Indian states. The decision was takenbecause the offer of tax incentives, apartfrom affecting the general fiscal health ofthe states, also affects the states’ ability toprovide infrastructure services, given the factthat sales tax revenue accounts for nearlyone-quarter of own-source revenue for themajority of Indian states. The GCs programmeassumes special importance in the light of thisdecision to stop the war of tax incentivesamong the Indian states. The GC approach isthus a test of the alternative to the tax war,as Sridhar (2005) points out.

Research Objectives

The primary objective of this study is toexamine the local employment impacts ofGCs. For this purpose, it studies the effect ofinfrastructure incentives (represented byGCs) on changes in the unemployment rate.The change in area unemployment is esti-mated taking into account district-level datafrom all Indian states. This econometric app-roach provides a benchmark for any effectthat GCs (or infrastructure incentives) andother characteristics may have on the unem-ployment rate of districts containing, andthose not containing, GCs. This econometricapproach is also consistent with what is usedin the literature to evaluate the impact ofsuch policies (see Papke, 1994; Ge, 1995;Pantuosco and Parker, 1998; Sridhar, 1998).

Further, this paper addresses the question ofwhether the area jobs would have been createdin the absence of the GC. This is the counter-factual question, which I attempt to answerthrough field visits.

This paper is organised as follows. The nextsection summarises the past literature on thesubject. Then a description of India’s GC pro-gramme and the secondary data follow. This is

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followed by an exposition of the empiricalmodel that forms the basis of the work, includ-ing a description of the treatment effectsproblem, the data, results from the probitand estimation of unemployment rate. Theeconometric model is supplemented withqualitative work in the form of field visits toanswer the counterfactual. The field visitsare described in the section following theresults from estimation. The final section sum-marises the policy implications of the researchbased on the econometric results and fieldvisits, and contains concluding remarks.

Past Literature

The regional development literature relatingto GCs and geographically targeted program-mes is sparse. Boisier (1981) points out thatthe regional development literature from1975 to 1979, in fact, does not speak ofgrowth poles at all. This may be said of litera-ture in the recent past as well.

Other developing countries have used GCswith objectives similar to those in India. Aproject, representing a joint venture betweenthe geography department of Carleton Univer-sity, Ottawa, Canada, and the University ofNairobi, Kenya, studied all aspects ofKenya’s Muranga district GC with a view tounderstanding how a strategy of GCs canassist in rural development. This study(Kimani and Taylor, 1973) found that, inKenya, GCs are created by policies designedto improve the capacity of rural areas togrow and to enhance their capability to holdtheir productive populations. The study findsthat these GCs were meant to avoid seriousproblems arising from the concentration ofpeople in large urban centres, very similar tothose in India.

McKenney (1993) documents the successof China’s SEZs and how they were allowedto expand gradually in land area. In general,this study evaluates the success of the originalfour SEZs that contributed significantly to thedevelopment of China’s coastal areas andspread the desire for openness and growthamong all of China’s areas. However, thestudy finds that the costs of attracting

foreign investment by the SEZs were alsovery high. One of the key costs was thecapital investment required to support pro-duction. For example, all the SEZs neededinvestment to create the infrastructure necess-ary to support increased manufacturing andtrade, for which huge debts had to be incurredby the central government. Further, the studyhighlights how the non-payment of taxesfrom the SEZs aggravated debt conditions inthe centre and the provinces.

To study the effectiveness of public inter-ventions in addressing significant regionaldisparities (in formal manufacturing concen-tration) in a developing economy, Deichmannet al. (2005) examine the aggregate andsectoral geographical concentration of manu-facturing industries in Indonesia. They esti-mate the impact of factors influencinglocation choice at the firm level. Their find-ings and simulations suggest that improve-ments in transport infrastructure may onlyhave limited effects in attracting industry tosecondary industrial centres outside Java,especially in sectors already establishedin leading regions. This underscores thechallenges for addressing the industrial for-tunes of lagging regions, either throughlocal decentralised policy interventions ornational policies focused on infrastructuredevelopment.

While the literature on GCs is sparse,there is a systematic body of theoretical,empirical and policy literature that dealswith the effects of various incentives to indus-try in the context of India. Shettar (1988)examines the concept and theory of GCswith reference to India, although this studypre-dates the GC policy which is evaluatedhere. The study by Shettar (1988) examinesthe impact of India’s post-independenceindustrial policy on rural and agriculturaldevelopment. For this, the study suggestsgrass-roots involvement and local controlover critical resources.

The first study on sales tax incentives in theIndian context is by Tulasidhar and Rao(1986) which shows both employment andoutput loss due to tax incentives, albeit in apartial equilibrium framework. This study

EMPLOYMENT AND GROWTH CENTRES IN INDIA 2207



examines sales tax incentives in a majorIndian state, Madhya Pradesh, and places therevenue loss at as much as 7–10 per cent ofthe sales tax revenue. Their analysis of alarge number of medium and large industriesindicated that the sales tax incentive, whichever way it is designed, is not the appropriateinstrument to raise the level of investment orspread this to backward areas.

Rajaraman et al. (1999), based on data fromthe same Indian state, Madhya Pradesh, findthat fiscal incentives have a statistically insig-nificant impact on large and medium invest-ment. Conversely, this study finds thatabundant power was an important factorattracting investment into the state duringthe 1980s, highlighting the importance ofinfrastructure in firms’ location decisions.

Mani et al. (1996) find that power avail-ability (rather than its price), reliable infra-structure and factors of production play asignificant role in firms’ location decisionsacross major Indian states. A collaborativestudy of the World Bank and Confederationof Indian Industry (2002) also highlights theimportance of physical and financial infra-structure in improving India’s investmentclimate.

Sridhar (2005) examines the impact ofGCs on local unemployment rate using datafrom India, not controlling for econometricproblems that frequently arise in the evalu-ation of such programmes. This study findsthat GCs do not have a statistically significantimpact on the unemployment rate of areasadopting them.

We have to make several observationsabout the relative importance of tax and infra-structure incentives based on the literature,theory and practice. First, it is only when thelevel of public services is held constant thatbenefits of tax incentives to firms would besignificant. The tax elasticity of businessactivity could differ from country to countrydepending upon the degree to which alterna-tive locations are similar or not from the view-point of prospective businesses. In India,given the heterogeneity in infrastructureacross the states, alternative locations are notconsidered perfect substitutes by firms for

their location decisions and it is likely thattax incentives are not important. This is sup-ported by empirical evidence relating toIndia regarding the relative importance oftax and infrastructure incentives, as has beenpresented in this section.

Secondly, it is not always the case thatincentives to industry will harm the develop-ment of infrastructure. An example is theKarnataka government’s proposal to replacefinancial with infrastructure incentives to theauto industry. Karnataka promised traininginstitutions, schools, colleges, office com-plexes, housing, a globally well-knit telecomnetwork, roads and dedicated power andwater supply necessary for the developmentof automobile manufacturing units as wellas vendors and dealers in the state. If otherstates also follow this example, competitionwill work to enhance the infrastructurecompetitiveness of the states.

Last but not the least, as Bartik (1991)points out, poorer states/regions are justifiedin offering (infrastructure) incentives toattract industry and employment. If distressedareas, with the provision of infrastructure,were to be successful in attracting firms toinvest and create employment, greater netbenefits would accrue to the area. Netbenefit or economic rent from a job is theextent to which actual wage is higher thanthe wage at which a person is willing toaccept a job (which is referred to as aperson’s reservation wage) and is defined asthe wages received minus the reservationwage.

It follows that if earnings (wages) were tobe constant across areas,1 net benefits wouldbe higher if the reservation wage were to belower—that is, if persons are willing toaccept jobs at lower wages. This is not tosay that it is beneficial for persons to acceptjobs at lower wages. However, it is easy toimagine that in poorer, high-unemploymentareas, where job opportunities are difficult tocome by, unemployed persons value theimportance of having a job. They wouldaccept a job, if it becomes available, at awage lower than what a person would acceptin relatively richer, low-unemployment




areas. Therefore, other things remainingconstant, if an unemployed person in a high-unemployment area were to be offered a job,net benefits derived from this job would behigher than that from a similar job created ina low-unemployment area. Sridhar (1996)finds evidence that net benefits from jobs aregreater if they are relocated to high-unem-ployment areas. However, Haurin andSridhar (2003) find no impact of higher localunemployment rate on individuals’ reser-vation wages. Nevertheless, as they suggest,it is sensible to attack clusters of high unem-ployment with policies that increase thedemand for workers.

The issue of greater net benefits fromemployment assumes even more significancein developing countries such as India, wherethe disparities between the richer, southernstates and poorer, north-eastern states havewidened enormously. Because of this, andgiven the futility of tax incentives in India(shown by the earlier empirical evidence),infrastructure incentives used to attract employ-ment by distressed areas can become a tool toincrease jobs along with their net benefits andthus reduce the disparities between them andthe richer states. Thus infrastructure incentivesare justified if they are offered by poorerstates. In fact, India’s Finance Minister recentlyproposed several new policy measures forstates in the north-eastern region—steps inthe right direction, justified until at least thewide regional disparities in the distribution ofincome and employment within the countrynarrow. So we have to think about stimulatingcompetition in the provision of infrastructureby all the states. From this viewpoint, GCsencourage competition among the states in theprovision of infrastructure which are quite criti-cal to firms.

This study contributes to this debate by ana-lysing the effect of infrastructure incentiveson changes in the area’s unemployment rate.The contributions/strengths of this work arethat it presents empirical and qualitative evi-dence regarding the employment impacts ofa regional development tool from a large anddeveloping country like India regardingwhich data and the literature are sparse.

Description of GC Programme

GCs in India, as in other countries, were orig-inally conceived to move industrial develop-ment away from the urban centres. Criteriafor selection of GCs are described in a notifi-cation dated 8 December 1988, circulated bythe Department of Industrial Policy andPromotion (DIPP), Ministry of Commerceand Industry, Government of India, whichadministers the programme at the centralgovernment level. These criteria, designedfor Indian cities of the 1980s, were as follows

(1) GCs shall not be located

(a) Within 50 kilometres of the boundaryof 7 cities in the country with a popu-lation above 2 500 000.

(b) Within 30 kilometres of the 2 citieswith a population between 1 500 000and 2 500 000.

(c) Within 15 kilometres from the bound-ary of the 12 cities in the country witha population between 750 000 and1 500 000.

(2) The GCs should be located close todistrict/sub-divisional/block/sub-districtheadquarters or developing urban centres.

(3) GCs shall have access to basic facilities—proximity to railheads, national or statehighways, water supply, power, tele-communications, educational and healthfacilities. If such facilities are notreadily available, it should be ensuredthat they are developed with ‘priorityand commitment’.

The GCs were selected on the assumption thattheir influence would cover a radius of about20–25 kilometres. Further, as much as poss-ible, the government guidelines specifiedthat development of GCs should not lead toloss of fertile agricultural lands; neithershould GCs be located on ecologically sensi-tive land or lead to denudation of forests.

For the programme, central funds areleveraged based on the understanding thatstate funds will be released. The centralgovernment provides INR 100 000 000(US$2 283 626) as equity with the concernedstate government contributing INR




50 000 000 (US$1 141 813). Further, financialinstitutions contribute INR 40 000 000(US$913 450) and banks contribute INR10 000 000 (US$228 362), making for a totalfinance of INR 200 000 000 (US$4 567 252).Market borrowings contribute an additionalINR 100 000 000 (US$2 283 626), makingfinancing for each selected GC, about INR300 000 000 (US$6 850 878).

The states use the central plus leveraged statefunds under the programme to acquire lands upto 400–800 hectares in the chosen areas,develop and lease them to firms. The cost ofland acquisition for the states consists ofpayment of compensation to landowners(Sridhar, 2005, summarises primary data onthe cost of land acquisition in GCs of somestates). Development of land refers to the con-struction of access roads, provision of watersupply, effluent disposal systems, upgrading ofexisting schools, the housing stock, colleges,industrial training institutes, hospitals, pro-vision of telecommunication facilities anddistribution network for power within theGC. The provision of funds for augmentationof power supply is to be made through therespective state’s plan allocation and notthrough GC funds. For telecommunications, amaximum of INR 20 000 000 (US$456 725)may be provided as part of GC infrastructure.The allowance for water supply is limitedto INR 78 000 000 (US$1 781 228) from GCfunds. Any expenditure higher than theseamounts is to be financed out of the concernedstate’s plan funds. The maintenance and pro-vision of services to GCs are the responsibilityof the local urban authority within whose juris-diction the GC is located.

Description of Secondary Data on GCs

Secondary data available on the various GCs,from DIPP, contain information on the date oftheir approval, approved project cost, amountsof central and state releases, final totalexpenditures, land acquired, number of plotsdeveloped and allotted, the number of firmsestablished, capital invested and employmentcreated by them. In addition, data regardingwages and related labour costs for 2001/02,

in the various states (not available at thedistrict or GC level) were obtained from theAnnual Survey of Industries, published bythe Ministry of Statistics and ProgrammeImplementation, Government of India(http://mospi.nic.in).

Currently, while on paper, GCs are in placein 71 of the 560 districts of the country, onlysome (26) of them are completely functional,with firms having located there. Table 1describes data for these functional GCs inthe country. The table shows that a majorpart (75 per cent) of the total GC expenditureis leveraged by funds from the states, whichshows the decentralised nature of and stateinterest in the programme. On average, thetotal expenditure is less than the approvedproject cost, as we expect. The average sizeof a plot developed for an industrial unitin the GCs is roughly 3.7 acres, based onthe land acquisition and developed plotsdata in Table 1. On average, the number ofplots allotted to firms (155) significantlylags behind the number developed (about278). This implies that the GCs need tomarket themselves to businesses as goodplaces to invest.2 On average, the number ofunits (firms) established in the GCs is evenless, being about 38, with a capital invest-ment of US$90 million on average. Theaverage (total) employment creation per GCis 1270. It may be noted that the employmentcreated in the GCs is a small part of theeconomic activity occurring in the districts,accounting for less than 1 per cent onaverage, but no more than 5 per cent oftotal (agricultural and non-agricultural)employment in the districts where they havelocated.3

Labour cost data for states containing theGCs indicate that, generally, wage andrelated costs should not be a constraint forfirm activity in these areas, since wages andemoluments constitute only 5 per cent of thetotal value of output produced in thesestates. The expenditure per job shows that,on average, these GC jobs are quite expensivefor the governments, costing about INR654 874 (or US$14 955) per job. However,GCs have several attractions for industries




and government policy that are faced with thechanges that occur as a city grows.

The costs of infrastructure in the majorcities of the country make it difficult forIndian companies to win the price war. Insmaller and medium-sized towns, where theGCs are located, firms are able to get an ade-quately trained labour force at relatively lowcost. This is because the cost-of-livingadjusted wage is lower in smaller andmedium towns, holding quality constant.Industries can utilise this advantage.

The other advantage if industry were tolocate to semi-urban and rural areas (theemphasis of the GC programme) is that itwould change the economic base of theseareas. Development models show that, indeveloping countries, population migrationfrom rural to urban areas occurs because ofthe existence of employment opportunities inurban areas (or because of their lack in ruralareas). Data from India’s 2001 Census showthat more than 28 per cent of those thatmigrated from rural to urban areas in India

during 1991–2001 did so for the sake ofemployment. Relocation of industry to ruraland semi-urban areas helps the rural poorand surplus labour to find alternative employ-ment in their own areas. This acts as a checkon urban migration that causes criticalshortages in urban housing and prevents thecreation of slums. Besides, the relocation ofindustry would help to co-ordinate the govern-ment’s poverty alleviation programmes in abetter manner, matching the demand for andsupply of required skills. So far, it has beenfound that recipients of various governmentemployment training programmes have notbeen able to find suitable jobs in rural areas.

Finally, the development of smaller andmedium towns, encouraged by the GCs, mightimply that they are self-contained communities,but eventually transport links have to developbetween urban areas and their satellite towns.Thus, the development of roads and highwayswould get the attention they need.

Table 2 summarises relevant socioeconomiccharacteristics of the (districts of) functional

Table 1. Description of data for functioning GCs (N ¼ 26)

Characteristic Average Maximum MinimumStandarddeviation

Approved project cost (in US$) 7 612 562.58 10 568 622.97 3 704 042.02 1 519 349.90Central release (in US$)a 1 954 784.20 2 518 839.92 456 725.28 618 013.27State release (in US$) 4 928 175.56 19 294 062.57 410 618.86 5 369 592.41Total expenditure (in US$) 6 620 816.13 21 577 688.97 867 344.14 5 733 297.40Land acquired (in acres) 1025.96 3111.70 164.00 747.74Plots developed 278.38 1328.00 5.00 271.34Plots allotted 155.24 1024.00 1.00 215.86Number of units established 37.73 89.00 1.00 32.02Capital invested (in US$) 86 848 463.82 994 062 571.36 0.00 200 997 675.97Total employment 1272.12 7723.00 0.00 1940.70Labour cost (ASI) 0.05 0.09 0.03 0.02Regional dummy 0.69 1.00 0.00 0.47Expenditure per job (in US$) 14 954.88 79 042.46 608.63 20 791.74Employment per GC firm 55.86 292.50 0.00 68.53GC duration 143.15 165.00 84.00 21.56GC employment as

percentage of district’stotal employment

0.35 4.21 0.00 0.86

aAll monetary data in INR (Indian rupees) in this table and others reported in this paper have been converted to US$ by using

the exchange rate US$1 ¼ INR 43.79, the exchange rate quoted by the Reserve Bank of India when this paper was revised.

Sources: Data obtained from the Department of Industrial Policy and Promotion, Ministry of Commerce, Government of

India, Central Statistical Organisation, Ministry of Statistics and Programme Implementation, Government of India, National

Council of Applied Economic Research and the Census of India.




Table 2. Characteristics of functional GCs

Number State, GC (district)Land area of

district (sq km)District

populationPopulation density(persons/sq km)

Incomedummy

GC jobs aspercentage ofdistrict jobs

1 Andhra Pradesh, Hindupur (Anantpur) 19 130 3 639 304 190 0 0.102 Andhra Pradesh, Bobbili (Vizianagarm) 6 539 2 245 103 343 0 0.033 Goa, Electronic-City (Verna-Plateau) 1 966 586 591 298 1 4.214 Haryana, Bawal (Rewari) 1 582 764 727 483 1 0.455 Himachal Pradesh, Kangra (Kangra) 5 739 1 338 536 233 1 0.176 Karnataka, Dharwad (Dharwar) 4 260 1 603 794 376 1 0.107 Karnataka, Hassan (Hassan) 6 814 1 721 319 253 1 0.298 Kerala, Kannur-Kozhikode

(Kannur-Kozhikode)5 310 5 290 863 996 1 0.02

9 Kerala, Alappuzha-Malappuram(Alappuzha-Malappuram)

4 964 5 734 989 1 155 1 0.01

10 Chhattisgarh, Borai (Durg) 8 549 2 805 576 328 0 0.1511 Chhattisgarh, Siltara (Raipur) 13 083 3 011 379 230 0 0.1912 Madhya Pradesh, Ghirongi (Bhind) 4 459 1 426 951 320 0 1.8413 Madhya Pradesh, Kheda (Dhar) 8 153 1 740 577 213 0 0.2914 Maharashtra, Akola (Akola) 5 429 1 629 305 300 1 0.1215 Maharashtra, Nanded (Nanded) 10 528 2 868 158 272 1 0.0116 Punjab, Bathinda (Bathinda) 3 382 1 181 226 349 1 0.0017 Rajasthan, Abu-Road (Sirohi) 5 136 850 756 166 1 0.1218 Rajasthan, Khara (Bikaner) 27284 1 673 562 61 1 0.1319 Rajasthan, Dholpur (Dholpur) 3033 982 815 324 1 0.0920 Rajasthan, Jhalawar (Jhalawar) 6219 1 180 342 190 1 0.1121 Tamilnadu, Erode (Periyar) 8 209 2 574 067 314 0 0.0822 Uttar Pradesh, Jamaur (Shahjahanpur) 4 575 2549458 557 1 0.0923 Uttar Pradesh, Pakbara (Moradabad) 3 647 3 749 630 1 028 1 0.0024 Uttar Pradesh, Satharia (Jaunpur) 4 038 3 911 305 969 1 0.1825 Uttar Pradesh, Shajanwa (Gorakhpur) 3321 3 784 720 1 140 1 0.2826 West Bengal, Malda (Malda) 3733 3 290 160 881 0 0.01

Average 6 888 2 389 816 460 0.69 0.35

Sources: Data obtained from the Census of India, National Council of Applied Economic Research and the Department of Industrial Policy and Promotion, Government of India.

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GCs, selected to date, for which data wereavailable. On average, population density ishigher in districts containing GCs (460persons per square kilometre) when comparedwith the national average (336 persons persquare kilometre for 2001). For the purposeof determining how many of the GCs arelocated in low-income states, the author com-puted weighted rural income (the weightsbeing number of households in everyincome-group), based on data published bythe National Council of Applied EconomicResearch, for all states. Whenever thisweighted rural income for the state containingthe GC was higher than the all-India weightedrural income, the GC was considered to be in arelatively prosperous area. When the weightedrural income for the state was below thenational average weighted rural income, theGC was considered to be in a low-incomeregion. Defined in this way, we find thatmore than two-thirds of GCs are located inhigh-income states (Table 2). This is curiousif the objective of GCs is to promote the indus-trialisation of backward areas in the country.

Table 3 summarises the size and othercharacteristics of the 26 functional GCs.Table 3 shows that, on average, GCs havebeen in existence for nearly 12 years. Giventhese data, on average, only 37 firms havebeen established during the 12-year period,with these firms having created an averageof 1270 jobs. Other aspects are worth men-tioning. Although the average expenditureby the central and state governments onthese GCs is only US$6.6 million, they havebeen able to attract an average investment ofUS$86 million (Table 3). Note that thisaverage investment is more than 10 timesthe average expenditure on these GCs(compare this with the concern regardingChina’s SEZs in McKenney’s (1993) study).

Past research and intuition suggest that suchgeographically targeted programmes arelikely to have measurable employmentimpact on the areas that adopt them. This isthe basis for the estimation of the change inunemployment rate as being dependent onvarious socioeconomic characteristics includ-ing the existence of the GC. The following

section describes the theory and model forthe estimation of changes in the unemploy-ment rate, taking into account characteristicsthat indicate the presence of GCs.

The Empirical Model

The regional development literature(Pantuosco and Parker, 1998; Sridhar, 2000,2005) shows the unemployment rate inreduced form as dependent on various socio-demographic characteristics. Literature onthe evaluation of programmes such as these(for instance, Papke, 1994; Sridhar, 1998)shows that the outcome variable of interestis usually estimated as function of a dummyrepresenting the programme to be evaluatedand other exogenous characteristics.

Consistent with this literature, the change inunemployment rate is estimated as dependenton changes in socio-demographic and econ-omic base characteristics, and a programmedummy for the GC, using district-level datafor India. Socio-demographic characteristicstaken into account are average age, proportionof minorities (in the Indian context, scheduledcastes and/or scheduled tribes (SC/ST)), pro-portion male and literacy rate. Economic basecharacteristics taken into account are proportionemployed in manufacturing and services.

The expectation from theoretical models thathave been developed in the literature (Ge, 1995,and Sridhar, 1998) is that areas with targetedprogrammes see a reduction in their unemploy-ment rate. Some reasons why we may expecttargeted programmes to reduce the unemploy-ment rate are that they enable the areas toattract firms that increase the demand forworkers in those areas, which were hithertonot successful in attracting firms. GCs do thiswith the help of infrastructure sops. Thus theunemployment model in this study is not justdriven by gender, human capital and economicstructure. The model also shows how GCsimpact employment.

In the empirical model, a dummy is includedfor whether or not a GC exists in a district(in states with GCs). This methodology,while allowing us to control for all other vari-ables that affect an area’s unemployment rate,




Table 3. Size and other features of functional GCs

State, GC (district) DurationExpenditure

(US$)Land acquired

(acres)Units

developedUnits

allottedUnits

established

Capitalinvested(US$) Jobs

Andhra Pradesh, Hindupur (Anantpur) 153 867 344 712.52 243 140 36 3 585 476 938Andhra Pradesh, Bobbili (Vizianagarm) 153 2 417 150 1 239.33 388 18 2 760 904 271Goa, Electronic-City (Verna-Plateau) 142 4 700 411 729.30 398 319 82 71 166 179 7 723Haryana, Bawal (Rewari) 153 21 577 689 1 212.00 556 212 23 228 362 640 925Himachal Pradesh, Kangra (Kangra) 94 1 637 543 808.00 350 168 51 3 423 156 574Karnataka, Dharwad (Dharwar) 155 14 078 511 2 205.00 315 190 88 165 334 551 573Karnataka, Hassan (Hassan) 155 16 715 049 1 825.00 234 66 75 994 062 571 2 023Kerala, Kannur-Kozhikode

(Kannur-Kozhikode)130 7 804 133 572.00 88 37 3 4 697 420 266

Kerala, Alappuzha-Malappuram(Alappuzha-Malappuram)

130 7 310 779 523.00 9 1 1 1 370 176 100

Chhattisgarh, Borai (Durg) 165 5 412 240 1 079.43 68 37 37 26 649 920 1 415Chhattisgarh, Siltara (Raipur) 153 6 288 742 3 111.70 29 21 21 145 617 721 1 779Madhya Pradesh, Ghirongi (Bhind) 165 9 779 425 1 769.24 42 42 42 278 096 026 7 296Madhya Pradesh, Kheda (Dhar) 165 4 939 484 594.94 98 11 6 151 296 278 1 755Maharashtra, Akola (Akola) 153 5 147 910 1 544.50 495 495 56 20 283 170 712Maharashtra, Nanded (Nanded) 84 4 110 208 1 595.80 197 23 1 101 076 730 52Punjab, Bathinda (Bathinda) 165 4 527 837 389.79 418 198 17 0 0Rajasthan, Abu-Road (Sirohi) 153 7 202 101 914.00 297 53 27 2 283 626 300Rajasthan, Khara (Bikaner) 153 2 656 520 720.00 461 260 75 2 164 878 680Rajasthan, Dholpur (Dholpur) 141 2 929 208 332.22 211 104 53 3 425 440 240Rajasthan, Jhalawar (Jhalawar) 149 1 799 680 438.00 238 118 78 2 055 264 450Tamilnadu, Erode (Periyar) 149 20 878 260 2440.19 95 72 22 12 073 213 1 048Uttar Pradesh, Jamaur (Shahjahanpur) 142 2 055 264 302.00 51 41 4 10 276 319 560Uttar Pradesh, Pakbara (Moradabad) 142 8 298 698 419.34 159 45 2 0 0Uttar Pradesh, Satharia (Jaunpur) 142 2 299 406 508.45 465 337 86 9 030 601 1 402Uttar Pradesh, Shajanwa (Gorakhpur) 142 5 830 715 525.27 1 328 1 024 89 11 567 824 1 941West Bengal, Malda (Malda) 94 876 913 164.00 5 4 4 9 399 977 52

Average 143.15 6 620 816.13 1 025.96 278 155 37.73 86 848 463.82 1 272.12

Notes: Duration of GC is in months since the day of official approval, and as of December 2004. Monetary data in INR are converted to US$ using the exchange rate reported by the Reserve

Bank of India (see notes to Table 1).

Sources: Data obtained from the Department of Industrial Policy and Promotion, Ministry of Commerce and Industry, Government of India.

22

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enables us to look at the local employmentimpact of the GC. The rationale for includingall the exogenous variables that explainchanges in the unemployment rate, alongwith the GC, is explained. Before that, aneconometric problem that arises due tosimultaneous determination of the GC andunemployment rate, termed the treatmenteffects problem, is described.

The Treatment Effects Problem

The estimation of (the change in) unemploy-ment rate is made taking into account thetreatment effects problem caused by the endo-geneity of the GC dummy. The treatmenteffects problem refers to a sample selectivityproblem. The problem refers to one in whichGCs could be designated because of highunemployment, reversing the causality in aregression of the unemployment rate as a func-tion of GC status.4 The problem is one of simul-taneity of GC designation in a regression ofchanges in the unemployment rate as a functionof GC designation and other variables. The sim-ultaneous determination of the programmedummy and the outcome (here, unemploymentrate) is consistent with past literature on thetreatment effects problem (Korenman andNeumark, 1991; Sridhar, 2000). As standardtextbooks show, if we were to ignore the simul-taneity problem and estimate the equation byordinary least squares (OLS), we wouldobtain inconsistent estimates of the effect ofGCs on unemployment.

Based on various criteria specified by thegovernment, the characteristics that determineGC designation are land area, population,degree of urbanisation, the extent of industrialbackwardness (endogenous) and growthpotential of the area. To be consistent withpolicy, the change in unemployment rate(over 1991–2001) of district i is used as ameasure of industrial backwardness. Alongwith land area of the district, changes in itspopulation and urbanisation, measures arechosen to represent its growth potential. Thecompounded annual growth rate (CAGR)over 1991–2001, of outstanding bank creditfor commercial and industrial purposes per

capita, and utilised in the district, is used asa measure of an area’s growth potential. Thegrowth of outstanding bank credit per capitaindicates the area’s ability to attract andretain businesses and/or entrepreneurs.Specifically, the higher the growth of out-standing banking credit for commercial/industrial purposes per capita, the higherwould be the growth potential of the area.Discussions with officials of the ReserveBank of India (RBI) (from where I obtainedthe data; see section on data sources) con-firmed that outstanding bank credit in a dis-trict is a good indicator of credit actuallydisbursed, since the two are positively corre-lated. Areas with low commercial and/orindustrial activity are also those with lowamounts of credit outstanding.

The treatment effects problem is alleviatedthrough a two-step estimation process. Sincethe GC dummy is a discrete, binary variable,probit estimation is first performed. In thefirst-stage probit, the GC status of a districti is estimated as dependent on change inits unemployment rate, change in the degreeof its urbanisation, CAGR of outstandingbanking credit to industry per capita, percentagechange in its population, all over 1991–2001,and its geographical area. These are broadlyconsistent with criteria specified by the govern-ment for GC designation. The first-stage probitequation is then specified as follows

GC status (0 or 1)i

¼ a0 þ a1Change in unemployment

ratei þ a2Change in urbanisationi

þ a3CAGR of per capita outstanding

crediti þ a4Land areai

þ a5Change in populationi þ ui (1)

The Second-stage Estimation

In the second stage, the saved results from theprobit estimation in equation (1) are used toestimate a two-stage least squares (2SLS)model of the change in unemployment rate,taking into account the endogeneity of theGC dummy. This procedure ensures that




estimates we obtain of the effect of the GC onunemployment are consistent.

The second-stage model estimating thechange in unemployment rate of the ithdistrict, Ui, is summarised in equation (2)

Ui ¼ b0 þ b1Dummy for GCi þ b2

Duration of GCi þ b3Duration of GC

squaredi þ b4Change in proportion

urbanisedi þ b5Change in

manufacturing employmenti

þ b6Change in service employmenti

þ b7Change in proportion SC/STi

þ b8Change in proportion malei

þ b9Change in literacy ratei

þ b10Change in mean agei

þ b11Change in populationi

þ b12Sample selection correction

factori þ ei ð2Þ

Note that in equation (2), a sample selectioncorrection factor, obtained from the probitmodel of the first stage, is included, whichaccounts for endogeneity of the GC dummy.

In the second stage of the two-step pro-cedure specified by equation (2), in additionto the effect of the GC, the duration of GC isincluded to capture the effect of programmelength on the change in unemployment rate.This is consistent with past literature on thetreatment effects problem (see note 25).Duration of the GC is measured in monthssince the time the GC was officially approved(duration, in months, as of December 2004).5

One can imagine that GCs reduce the unem-ployment rate of an area, but there could besome optimum period for which it isdesirable,6 after which it is preferable thatthe area abates offering (infrastructure) incen-tives. In terms of policy action, this impliesthat areas that have been GCs beyond acertain period might need to be decertified.

In addition to the duration variable, in theestimation, its squared term is included. Thisis to check for any non-linearity in the impactof duration of GC on the change in unemploy-ment rate.7 As an example, one may expect that

the GC would initially be highly effective inreducing unemployment, but its effect couldgradually taper off later, either becausebureaucracies make way into the institutionalstructure, or simply because business/govern-mental interest in the programme wanes.

The proportion SC/ST refers to the pro-portion of scheduled castes/scheduled tribesin the district. This variable is included asSC/STs are groups of minorities that havebeen traditionally repressed socially in India.If true, their presence would have a positiveeffect on changes in the unemployment of dis-tricts that have higher populations of SC/ST.The proportion male is included to controlfor its effect on changes in the area’s unem-ployment rate since men tend to be theactive labour force participants in mostinstances. Literacy rate is included as acontrol for the skills of the area’s labourforce. Age is included to control for itseffect on unemployment. The proportion ofpopulation in the districts urbanised isincluded as an explanatory variable sincethere are a number of advantages offered byurban areas, such as agglomeration economiesthat attract firms and create jobs, reducing theunemployment level.

The percentage change in population isincluded to indicate the supply of labour, asinput into firms’ production processes, andhence can affect an area’s unemployment.While changes in population are included asa measure of labour supply, a growing popu-lation, not necessarily the supply of highlyskilled labour, fuels economic growth. Eco-nomic growth is fuelled by a variety of activi-ties including housing, construction, realestate, construction of equipment, all ofwhich do not always require highly skilledlabour. Further, population, not labour force,is a good measure of the potential laboursupply. This is because, at high enoughwages (as with jobs in call centres and othertechnology-enabled services), even those outof the labour force might be willing to work(see note 12).

In addition to controlling for these socio-demographic characteristics at the districtlevel (based on data for all Indian states),




the district’s economic base measured as theproportion of workers in manufacturing andservice employment is controlled for. Specifi-cally, the change in the economic base ofthe district is used to assess its impact on thechanges in the district’s unemployment rate.The change in proportion of workers employedin manufacturing and in services, 1991–2000is used to operationalise the economic base.Manufacturing and service occupations couldhave varying impacts on an area’s unemploy-ment due to different demand and supplyconditions.8 These measures reflect theimportance of these sectors in the area’s econ-omic base and it is necessary to control forthem, as shown in equation (2).

Data, Sources and Variable Definitions

As must be clear from the earlier discussion,the dummy for GC status is 0 if there is noGC and 1 if there is a functioning GC inplace in the district. These data are fromDIPP. As explained, in the first-stage probitmodel, the binary GC status is estimated as afunction of various characteristics that deter-mine GC designation. The policy states popu-lation, urbanisation, land area, industrialbackwardness and the area’s growth potentialas criteria for GC designation. Given that theGCs have been in place for more than adecade (Table 1), changes in the explanatoryindicators over 1991–2001 have been usedto determine an area’s potential eligibility toreceive a GC. Data on all explanatory indi-cators, with the exception of that for thearea’s growth potential, are taken from theCensuses of India 1991 and 2001. Further,note that, in the second stage, the appropriatedependent variable is the change in unemploy-ment rate, so changes in indicators have beenused in both stages of the estimation. The useof explanatory indicators over 1991–2001also has the advantage of ensuring thatenough time has elapsed for a meaningfulassessment of the GC policy.

The outstanding bank credit (disbursed forcommercial/industrial purposes) data for1991 and 2001 are taken from the centralbank of the country, the Reserve Bank of

India (RBI) (http:///www.rbi.org.in).9 Thetotal credit for commercial/industrial pur-poses outstanding is divided into the totalpopulation of the districts for 1991 and 2001to arrive at outstanding credit per capita. TheCAGR of per capita outstanding credit over1991–2001 in the ith district is calculated asfollows

(Per capita bankingcredit outstanding, 2001)

Per capita bankingcredit outstanding, 1991)

0BB@

1CCA

110

� 1

26664

37775

(3)

The CAGR shows the growth of the totalamount of credit outstanding and utilised forvarious purposes including industry, transport,trade and professional services in the district,sanctioned through various bank branches.

Given its importance, the unemploymentrate is used as a measure of industrial back-wardness of an area. Data on unemploymentrate were not available readily. Data on popu-lation, main, marginal and non-workers wereavailable by district from the 1991 and 2001Censuses of India. Main workers are thosewho had worked for the major part of theyear preceding the enumeration.10 Marginalworkers are those who worked for sometimein the year preceding the enumeration butdid not work for the major part of the year.11

If an individual had not worked at all duringthe past year, he or she is treated as a non-worker by the Census. Non-workers include:those attending to household duties at home;students; dependents; retired persons orrenters; beggars; inmates of institutions; and,other non-workers. To be consistent withthe Census definition of non-workers, here,non-workers have been treated as thoseoutside the labour force.12 Marginal workershave been treated as those that were willingto work, but have not found full-time work.The unemployment rate is the ratio of thesemarginal workers to those in the labour force(main plus marginal workers). The change inunemployment rate is calculated as the absol-ute change in unemployment rate over 1991–2001. It is computed as unemployment rate in




2001, minus unemployment rate in 1991, forevery district.

Other variables are calculated in a straight-forward manner from the 2001 and 1991Censuses of India. The proportion urbanisedis calculated as the proportion of populationin each district that was urban in 2001 and1991.13 Changes in the proportion urban arecalculated as absolute changes in urbanisedpopulation in the districts over the 1991–2001 period. Geographical areas of districtsare in square kilometres and populationfor 2001 and 1991 in actual numbers. Thechange in population of a district is rep-resented as percentage change over 1991–2001. Literacy rate is the total number of lit-erates divided by population older than 6years for each district. The change in literacyrate over 1991–2001 is calculated as absolutechange—the literacy rate for 2001 minus theliteracy rate for 1991 for all districts. The pro-portion male is the male population older than6 years computed as a ratio of populationolder than 6 years, for 1991 and 2001. Thechange in proportion male over 1991–2001is calculated as absolute change (that is, pro-portion male in 2001 minus proportion malein 1991). The average age, each in 1991 and2001, is computed as the weighted averageof the population in every age-group (theweights) and the ages.14 The change inaverage age over 1991–2001 is calculated aspercentage change in average age of districti over 1991–2001.

The proportion employed in manufacturing(manufacturing and processing in householdindustry, and other than household industryworkers) and those in services (this includesworkers in trade and commerce, those intransport, storage and communications, andin other services) are calculated as ratios oftotal workers.15 District-level data regardingworkers in manufacturing and services for1991 are obtained from the Census of India.

Since the 2001 Census of India has notyet released the data on the proportion ofworkers in manufacturing and services for2001, I use instead data from the NationalSample Survey Organisation (NSSO), 55thround (Employment and unemployment

situation in India).16 NSSO data are not avail-able at the district level since the samplenumbers at the district level are quite small.Because of this, the state-level proportion ofworkers in manufacturing and services wascomputed for 2000, using NSSO data.17 Thiswas done in two steps. First, the proportionof workers in the total population of everystate was calculated from the Census data.Then, using the ratios of workers in variousindustry categories from the NSSO, theworkers in each state were assigned tovarious industry categories. The change inthe economic base for districts was thus calcu-lated as absolute changes in the proportion ofworkers in manufacturing/services in 2000minus that in 1991, for states in which the dis-tricts are located.18

Comparison of GCs and Non-GCs

Table 4 shows relevant data for districts inIndian states for which all data were avail-able.19 Table 5 disaggregates these data andpresents them separately for districts contain-ing GCs and those without them, for purposesof comparison.

On average, the change in unemploymentrate is around 15 percentage points, meaningthat the unemployment rate increased in alldistricts during the period 1991–2001(Table 4). Few districts, in fact, only 3 of the560 districts, experienced a reduction intheir unemployment rate over the period.20

As one would expect, the workforce partici-pation rate of the population increasedduring 1991–2001.

It may be noted from Table 4 that districts,on average, have young population and thatthe average age has declined since 1991.21

Since we expect young people in their mid30s to be actively involved in the labourforce (seeking or changing jobs), it is import-ant to study GCs, to assess their effects on theunemployment rate.

On average, the literacy rate has increasedsignificantly during 1991–2001 from only50 per cent to 64 per cent for all districts, con-sistent with similar data for the country(Table 4). On average, a little more than half




Table 4. Description of data for all districts

Variable MeanStandarddeviation Minimum Maximum Observations

Unemployment rate2001 0.24 0.09 0.05 0.49 5451991 0.10 0.06 0.00 0.33 518

Work forceparticipation2001 0.41 0.07 0.24 0.64 5451991 0.39 0.07 0.22 0.65 518

Literacy rate2001 0.64 0.13 0.30 0.97 5451991 0.50 0.15 0.19 0.96 534

Proportion male2001 0.52 0.02 0.47 0.57 5471991 0.52 0.02 0.44 0.57 534GC status, 2001 0.05 0.22 0.00 1.00 547

Duration of GCDuration in months 7.16 31.27 0.00 165.00 547Duration squared 1 027.53 4 618.03 0.00 27 225.00 547

Average age2001 33.63 0.80 30.68 36.46 5331991 34.07 0.78 31.56 36.46 533

Proportion SCST2001 0.32 0.22 0.00 0.98 5341991 0.31 0.21 0.00 0.98 533

Proportionmanufacturing2000 0.10 0.04 0.01 0.26 5341991 0.08 0.06 0.00 0.37 533

Proportion services2000 0.23 0.07 0.14 0.50 5341991 0.19 0.10 0.06 0.72 533

Proportionurbanised2001 0.22 0.17 0.00 1.00 5341991 0.22 0.16 0.00 1.00 533

Population2001 1 726 590 1 122 800 31 362 5 863 720 5331991 1 989 210 1 218 460 20 297 9 925 890 534

Land area (in 1000 sq km) 5 193.83 3 626.36 9.00 19 130.00 533

CAGR, bankingcredit percapita utilisedin district1991–2001 0.08 0.08 20.27 0.93 5342001 2 868.45 15 030.50 141.07 335 135.00 5341991 920.10 1 421.45 0.36 17 919.30 534


India, National Sample Survey Organisation, Reserve Bank of India and the Census of India.




Table 5. Comparison of data for areas with and without GCs

Non-GC districtsDistricts with functioning

GCs (N ¼ 28)a

Variable MeanStandarddeviation Cases Mean Standard deviation

Unemploymentrate2001 0.24 0.09 517 0.23 0.091991 0.10 0.06 490 0.09 0.05Work forceparticipation2001 0.41 0.07 517 0.40 0.081991 0.39 0.07 490 0.37 0.08Literacy rate2001 0.64 0.13 517 0.68 0.141991 0.50 0.15 506 0.54 0.20Proportion male2001 0.52 0.01 519 0.51 0.021991 0.52 0.02 506 0.51 0.02GC status, 2001 0 0 519 1 0Duration of GCDuration in months 0 0 519 139.786 23.522Duration squared 0.00 0.00 519 20 073.60 5 897.50Average age2001 33.62 0.79 505 33.82 0.851991 34.06 0.78 505 34.22 0.66Proportion SCST2001 0.32 0.22 506 0.22 0.121991 0.32 0.22 505 0.22 0.12Proportionmanufacturing2000 0.09 0.04 506 0.10 0.041991 0.08 0.06 505 0.09 0.05Proportionservices2000 0.23 0.06 506 0.26 0.101991 0.19 0.10 505 0.22 0.10Proportionurbanised2001 0.22 0.17 506 0.26 0.141991 0.22 0.17 505 0.23 0.11Population2001 1 699 560 1 122 740 505 2 214 010 1 024 5401991 1 983 400 1 229 330 506 2 094 150 1 014 900Land area (in 1000 sq km) 5 175.21 3 618.38 505 5 529.66 3 820.14CAGR, banking creditper capita utilisedin district1991–2001 0.08 0.08 506 0.11 0.042001 2 892.29 15 435.00 506 2 437.58 1 897.481991 929.25 1 455.22 506 754.70 498.42

aRecall from Tables 1–2 that there are 26 GCs in 28 districts.


India, Centre for Monitoring Indian Economy and the Census of India.




of population in the districts is male and thisgender composition has remained unchangedbetween 1991 and 2001. On average, aboutone-third of the population is SC or ST inthe districts and their proportion remainedstable during 1991–2001.

On average, the proportion of workersdependent on services is more than twicethat dependent on manufacturing (in bothyears)—which illustrates the serviceeconomy India has become.

Five per cent of the districts contain function-ing GCs.22 On average, less than one-quarter ofthe population in the districts is urbanised,although metropolitan districts such asKolkata, Mumbai, Chennai and Hyderabadare completely urban. The extent of urbanis-ation has remained the same over 1991–2001.Surprisingly, on average, there is a decline inthe absolute population of the districts con-sidered. On average, the CAGR of outstandingbanking credit for commercial and industrialpurposes, utilised per capita, registered an 8per cent increase over 1991–2001, from INR920.10 (US$21) in 1991 to 2868.45 (US$66)per capita in 2001. This shows the generalincrease in entrepreneurship and commercialactivity in the post-1991 period.

Table 5 compares these data for districtswith GCs and those without them. Districtscontaining GCs are larger in terms of landarea and population, consistent with criteriaused for their designation. Further, note thaton average the population of the GC districtsincreased over 1991–2001, whereas averagepopulation in the non-GC districts actuallydeclined over the period. Thus, taking all dis-tricts, on average, we observe a decline in theabsolute number of people over 1991–2001.

Note from Table 5 that, while the amount ofoutstanding bank credit per capita (for 1991and 2001) is higher in non-GC districts, thegrowth of this credit over 1991–2001 hasbeen much higher in districts with GCs thanin those without, showing their highergrowth potential. On average, the degree ofurbanisation is higher in the GC districtsthan without them. Note that this urbanisednature of districts with GCs, is itself not acontra-indicator of the original designation

criteria for GCs that require lack of urbanis-ation (see section on description of GCs pro-gramme). Also, note that the district is alarger geographical area than the GC.Further, GCs are not, in reality, designatedon the basis of the stated criteria. Moreover,some criteria could be in conflict with oneanother (for instance, see criteria (1) and (2)for GC designation).

On average, districts with GCs are moreliterate, contain fewer minorities such asSC/ST and have more service employmentthan those without them, not consistent withideas of distress. In fact, GC districts have alower unemployment rate than their non-GCcounterparts. On average, GCs have been inplace for 140 months (a little less than 12years) (for districts with functioning GCsonly, Table 5).23

Results from Probit and Estimation ofUnemployment Rate

Table 6 shows the results from the probitestimation of GC status, specified by equation(2), as dependent on certain characteristics.The table shows that the percentage change ofpopulation in the district over 1991–2001, andits growth potential reflected in the growth ofoutstanding bank credit per capita for commer-cial/industrial purposes, significantly influenceGC designation. Specifically, the greater thepercentage change in population of a district,the higher is its probability of being a GC in2001, consistent with our expectations, and asspecified by policy criteria. This entails theassumption that areas inhabited by continuallyincreasing populations are in need of moreresources and special incentives to attract indus-try. Further, the probit model in Table 6 showsthat the higher the growth potential of thedistrict, as seen in the growth of bankingcredit for commercial and industrial purposes,the higher is its probability of securing a GC.This is consistent with our expectations sincebanking credit is based on the creditworthinessof firms and enterprising individuals in thearea and hence reflects the growth potentialof the district in attracting such firms and resi-dents. Hence, areas with growth potential are




designated as GCs. While the results from theprobit have mixed statistical significance, thesefindings are discussed further in the next twosections, together with the results from theestimations and their policy significance.

Table 7 shows the second step 2SLSestimation of the change in unemploymentrate, when the endogeneity of GC status istaken into account, as specified by equation(2). The estimation in Table 7 shows thatdemographic factors such as the percentage

change in population, changes in averageage and proportion of male population, andsocioeconomic factors such as the manufac-turing base and degree of urbanisation, arestatistically significant in explaining changesin a district’s unemployment rate.

The coefficient on the percentage change inage is negative. This implies that an increasein the average age of an area’s population,implying higher work experience and employ-ability, reduces its unemployment rate.

Table 6. Results from probit estimation (dependent variable: GC status)

CoefficientStandard

error T-valueVariable

mean

Intercept 22.06�� 0.32 26.42Change in urbanisation,

1991–20012.06 1.34 1.54 0.0042

Change in unemployment rate,1991–2001

0.85 1.39 0.61 0.1452

CAGR, banking credit utilisedper capita, 1991–2001

2.38�� 1.06 2.24 0.0784

Percentage change in population,1991–2001

0.61�� 0.29 2.06 20.0370

Land area (1000 sq km) 0.01 0.03 0.26 5.2008

Notes: N ¼ 532. ��Statistically significant at the 5 per cent level; ��Statistically significant at the 1 per cent level.

Table 7. Results from 2SLS estimation (dependent variable: change in unemployment rate)

Variable Coefficient Standard error T-value

Constant 0.12�� 0.01 14.75GC status, 2001 0.30 0.28 1.08Duration of GC (in months) 0.00 0.00 0.50Duration squared 0.00 0.00 20.63Percentage change in age 21.32�� 0.25 25.27Change in percentage SCST 0.00 0.07 20.04Change in percentage manufacturing 0.13� 0.07 1.86Change in percentage services 0.04 0.04 0.95Change in percentage urbanisation 20.13�� 0.04 23.19Percentage change in population 20.03�� 0.01 23.03Change in percentage male 22.26�� 0.51 24.48Change in literacy rate 20.03 0.05 20.63Lambda 20.18�� 0.09 22.10Adjusted R 2 0.16F 9.47

Notes: N ¼ 530. �Statistically significant at the 10 per cent level; ��Statistically significant at the 5 per cent level;��Statistically significant at the 1 per cent level.




Specifically, for every one percentage pointincrease in the average age of the district’spopulation, there is a 1.32 percentage pointdecrease in the unemployment rate of thearea. The change in the proportion in manu-facturing has a positive and significantimpact on the unemployment rate, showingthat, as the proportion dependent on manufac-turing increases, unemployment is likely toincrease. This certainly points to the weaknessof India’s manufacturing as an engine ofemployment growth, albeit with increasedproductivity.24 Further, India’s manufacturingbase is not as large as that in some othercountries—China’s, for example—becauseof its generally poor infrastructure, relativeto the requirements of manufacturing firms.For instance, Sridhar (2006), based on asurvey by the World Bank (2002) of theinvestment climate in many countries, findsthat only 2 per cent of the sales of Chinesefirms, in contrast to 9 per cent of the sales ofIndian firms, were lost due to power outages.

Urbanisation has the expected negativeimpact on unemployment rate. This is reason-able to expect, as urban areas offer manyagglomeration economies for industry tolocate and create jobs. Based on the estimatesin Table 7, for every percentage point increasein the district’s urbanisation, there is a 0.13 percent point decrease in its unemployment rate.

Changes in the district’s population affectthe unemployment rate. Specifically, decreasesin population increase the unemployment rateand vice versa. This is an interesting resultbecause it emphasises the supply of labour asa constraint and implies that labour (measuredby population), which firms require, might bemissing in certain districts, which is thecause of their high unemployment. Alterna-tively, it could be the case that populationinflows occur into an area as a result ofdecreases in the local unemployment rate, inwhich case the change in population isendogenous to the model. However, in thecontext of developing countries such asIndia, a number of information imperfectionsexist and labour mobility is restricted toskilled workers. Since a majority of theworkers are unskilled and information

imperfections exist regarding job availability,labour migration in response to informationregarding job availability and changes inunemployment rate is minimal. For instance,for a janitor in Etawah, Uttar Pradesh (aneastern Indian large state), to find out aboutemployment opportunities in Bangalore (inthe south Indian state of Karnataka), wouldbe like ‘looking for the proverbial needle in ahaystack’ (Ehrenberg and Smith, 1994).While such information imperfections existeven in developed countries, it is certainlyreasonable to assume in the context of devel-oping countries such as India that populationchanges determine changes in the unemploy-ment rate but not vice versa. Supporting this,in fact, Sridhar (2004) finds that the ratio ofthe unemployment rate in the central city tothat in the suburbs did not have a significantimpact on population suburbanisation inIndia’s urban agglomerations.

Further, as one would expect, districts whichhave greater proportions of male populationhave lower increases in their unemploymentrate, primarily because men tend to be theprimary work force participants in most, ifnot, all households. This is corroborated bythe fact that, in 2001, male workforce partici-pation rates for Indian states were on average52 per cent compared with only a 30 per centworkforce participation rate for women (com-puted from data for all districts from theCensus of India for 2001). Further, male partici-pants in the workforce are likely to have moreskills that enable their better employability.As an instance, male literacy rate was 75 percent compared with only 54 per cent forwomen, as of the 2001 Census.

Finally, the selection correction factor, l,obtained from the probit model of the firststage, is statistically significant. This showsthat the endogeneity of the GC dummy, whichwas a statistically significant problem, hasbeen corrected for, in the second stage, withinclusion of the sample selection correctionfactor l. Thus the marginal effect of the GCon changes in unemployment has two parts.One effect is due to the influence of variousfactors (such as changes in a district’s popu-lation and its growth potential) on the




probability of getting GC designation for a dis-trict, captured in the probit estimation. Theother effect is due to the GC’s direct influenceon the unemployment rate, which the 2SLSestimation attempts to capture, after accountingfor its endogeneity (see Greene, 1993).

The OLS explains only 16 per cent of thevariation in unemployment rate. While this islow, note that the R 2 is only a descriptive stat-istic. In cross-sectional data, a lower R 2 mightoccur even if the model is a satisfactory one,because of the large variation across individualunits of observation (see Pindyck and Rubin-feld, 1991). This suggests that the R 2 alonemay not be a suitable measure of the explana-tory power of a model. I discuss this furtherin the context of the wider findings of thepaper, in the section on policy implications.

Further, the correlation matrix of all inde-pendent variables was examined and no corre-lations were alarming enough to suggestcollinearity, with the exception of thedummy for GC and its duration (and durationsquared), which are highly correlated. Thecorrelation matrix is reported in Table 8.Despite their high correlation, I include theGC’s duration and duration squared in themodel as discussed earlier. This is because itis quite common in the literature dealingwith treatment effects of the nature dealtwith in this study, to examine not only howa treatment effect (here the GC programme),but also how the effect of its durationimpacts the outcome variable (change inunemployment rate).25

Discussion: What Is the Impact of GCs andWhat Reduces an Area’s UnemploymentRate?

Based on findings from the probit estimation,areas that have growth potential and thosethat have experienced increases in populationgrowth are designated as GCs. This impliesthat distressed areas are not necessarily desig-nated as GCs (except if they experience popu-lation increases, which is unlikely). Sincesuch distressed areas are unable to affordspecial incentives to attract industry, theprocess of the actual setting up of GCs in

the country has the effect of accentuatingregional disparities.

Further, note that changes in the populationof a district are a statistically important factorin determining their GC status and also effec-tive in reducing their unemployment. Thisdoes indicate that the population criterion isindeed a good one for alleviating unemploy-ment and should be retained for purposes ofGC designation.

The GC dummy does not have a statisti-cally significant impact in reducing theunemployment rate of districts that containthem.26 Several reasons could explain thisresult. First, it is possible that institutionalrigidities are far too strong in these backwardregions and any positive effect of the GCs inreducing unemployment is not strong enoughto counter the institutional problems orinherent disadvantages in the regions.Further, as Table 2 shows, GC jobs as a pro-portion of district jobs are still too small tomake much of a difference. If we were toobtain data on unemployment rate at a moredisaggregate level, such as census tracts,one might find GCs to have significantimpacts. Alternatively, GCs have to bescaled up in a much bigger way than is cur-rently the case. For instance, in the HindupurGC (Andhra Pradesh), the land acquired aspart of the GC is less than 4 per cent of thetotal area of the district. When largeramounts of land are acquired and developed,the size of the programme itself will expandconsiderably.

Further, the results from the 2SLSregression imply that areas should attractand retain population with skills rather thantry to attract firms directly. Sridhar (2004)finds that, in fact, firms suburbanise basedupon the skills of the population as measuredby the literacy rate. Thus, by attracting resi-dents with skills, areas can reduce their unem-ployment rate. As Tiebout (1956) showed,local governments are able to attract residentswith the offer of good quality public services.However, states and local governments inIndia are not competing with each other yetin terms of public services, as found bySridhar (2004).




Table 8. Correlation matrix

GC2001 GCDURN DURNSQ CHGAGE CHGSCST CHGMFG CHGSERV CHGURBN CHGPOP CHGMALE CHGLIT

GC2001 1.0000GCDURN 0.9859 1.0000DURNSQ 0.9588 0.9926 1.0000CHGAGE 0.0144 0.0114 0.0089 1.0000CHGSCST 20.0385 20.0361 20.0339 0.0384 1.0000CHGMFG 20.0023 20.0016 20.0007 20.0890 0.0247 1.0000CHGSERV 0.0027 20.0022 20.0042 20.0730 20.0038 0.4884 1.0000CHGURBN 0.0812 0.0868 0.0899 20.0964 20.2516 0.0202 0.1032 1.0000CHGPOP 0.0845 0.0781 0.0721 20.1145 20.1618 20.0662 0.0117 0.1550 1.0000CHGMALE 0.0451 0.0446 0.0451 20.0136 20.1807 20.0882 0.0204 0.1917 20.0118 1.0000CHGLIT 0.0152 0.0258 0.0358 20.3393 20.2062 0.0880 0.1122 0.1917 0.0286 20.0112 1.0000

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Further, states and local governments arebetter off in ‘urbanising’ their rural areas,thus encouraging industrial agglomerationand reducing the area’s unemployment. It isreasonable to expect that firms are attractedto areas where other firms exist because ofthe benefits from agglomeration and accessto common services.27 The benefits fromagglomeration economies, generated by highdensities of firms, are supported by empiricalevidence (Ciccone and Hall, 1996; Maniet al., 1996; Lall and Chakravorty, 2005).

Note that, while this finding with respect tourbanisation reflects the uneven nature ofeconomic development which growth policiessuch as GCs are trying to reverse, the resultregarding population surely indicates thatareas with an increasing supply of labour arein a better position to reduce their unemploy-ment rate. It is indeed a novel finding that, inthe context of a large, populous, developingcountry like India, it is not demand forlabour, but the supply of labour, that is theconstraint in reducing the unemployment rate.

In the next section, the counterfactual tothe GC is examined and a qualitative assess-ment is provided of its impact on localeconomies.

Qualitative Assessment of the Impactof GCs

A key question of the study has been to inves-tigate the impact of infrastructure on localeconomies and how it eventually feeds intoemployment. The econometric work showsthat GCs do not have a statistically significantimpact on changes in the local unemploymentrate through their jobs. Given this finding, Iattempted to get qualitative information fromfield visits to several GCs, by answering thequestion: would the GC jobs have beencreated anyway in the area without the GC?This, while being different from the onedealing with the effect of GCs on the localunemployment rate, answers the concern ofwhether the jobs created in the area wereattributable to the GC. If yes, the programmemight still be worthwhile, even if it does notaffect the local unemployment rate. It should

be remembered that GCs, even in theabsence of significant impacts on the localunemployment rate, create jobs that increasethe employability of the local labour forceand create hysteresis effects.28 This meansthat a one-time job growth because of theGC can have lasting impacts on the locallabour force’s long-run employability, basedon the skills they learn from the jobs.

However, if the answer to the above ques-tion is no, then the programme is expensiveto continue.

Field Visits

The author visited several GCs located invarious parts of India between May 2001and July 2002 to assess their qualitativeimpacts. While it would have been ideal tovisit all GCs, the choice of the field visitswas determined by funding constraints, asthere was no allowance for long-distancetravel from the seed money project thatfunded the study. In order to ensure spatialrepresentativeness of the sample of GCs, theauthor visited four GCs geographicallyspread across the country, despite thefunding constraints. One in the south(Hassan in Karnataka, a progressive high-income state), one in the north (Bawal inHaryana) and both the GCs in the north-eastern state of Uttar Pradesh (U.P.) that arefully functional and where firms havelocated.29

There are several reasons to believe that thesample of GCs visited is a representative one.First, while only the western part of the coun-try’s GCs was unrepresented, the location ofGCs actually visited is spatially dispersed,covering the northern, southern and easternparts of the country. Next, the states contain-ing these GCs are of varying sizes, withKarnataka and U.P. representing largerstates, whereas Haryana is a relativelysmaller state (in terms of geographical area).Finally, Haryana and Karnataka representstates with high urban incomes, whereasU.P. is a state with a low urban income (theincome dummy in Table 2 applies to ruralincome). Thus, visits to GCs spatially




dispersed in various parts of the country andlocated in states of varying sizes and urbanincomes in the country, enabled a fair assess-ment of the functioning of representative GCs.

While the GC is in place in only 5 per centof all districts, the secondary data from thecensus and the states, and primary data col-lected from the field visits, show that severalof them would have generated jobs withoutthe GC. See Figure 1 for a map of the districtsof the largest (in terms of land area and popu-lation) state in India, Uttar Pradesh (U.P.).The author’s field visits suggested that Gor-akhpur (where the Shajanwa GC is located,see Tables 2 and 3) has been historically anindustrial area, independent of its GC status.To corroborate this, the GC in Shajanwa wasdesignated in 1993, whereas, even as of1991, the district’s service base (22.29 per

cent employment in services) was in factgreater than the average for the state (17.22per cent)! (Sridhar, 2005).

Table 9 shows secondary data on the totalnumber of jobs (including those created bysmall, medium and large units) by districtfor the south Indian state of Karnataka. Thedistricts in Table 9 are arranged in ascendingorder of their share of total employmentcreated in the state during 2000. This tableshows that there are many industriallybackward and high unemployment districtssuch as Kodagu and Koppal in the state, thathave quite low shares of employment andindustry. These districts cannot attractemployment without the incentives impliedby the GC, whereas districts in the lowerhalf of the table can attract firms withoutspecial incentives, due to their natural

Figure 1. Districts of Uttar Pradesh, India. Source: Census of India (http://www.censusindia.net/).




advantages such as agglomeration economies.With the exception of the Hassan GC, theother two GCs in this state, however, are indistricts in the second half of Table 9, whichmeans that they can attract jobs withoutGC incentives. Further, while the Hassan(Karnataka) and Satharia (Jaunpur, U.P.)GCs are accessible to/from state highways,Shajanwa in U.P. (Gorakhpur district) andBawal (in Haryana) are accessible directlyby national highways.

All these data together imply that certainlysome of these areas, with their pre-existingeconomic base and access to transport infra-structure, could generate jobs without thespecial incentives implied by a GC. Theseare areas with the infrastructure already inplace, and not necessarily as a result of theGC, that is needed for firms to grow.

Secondary and primary data for GCslocated in areas with no pre-existing manufac-turing base were also examined. Satharia (inJaunpur district, U.P., see Figure 1), is anexample. The Census of India shows thatJaunpur had a below-average concentrationof its workforce in manufacturing and services(7 and 15 per cent respectively) comparedwith that for the state of U.P. in 1991 (respect-ively 8 and 17 per cent) (Sridhar, 2005,Table 7.2), before its designation as a GC in1993.

While accessible from a state highway, theSatharia GC is in a remote location, about47 km from the (Jaunpur) district headquar-ters, with road access being poor (windingroads amidst thick vegetation), which indus-tries find disadvantageous in terms oftransport costs, time and logistics. Based on

Table 9. Industrial units in Karnataka

Number District Total employment Percentage of total

1 Haveri 752 0.142 Koppal 769 0.143 Kodagu 1 187 0.224 Davanagere 1 299 0.245 Gadag 1 337 0.256 Bagalkote 1 430 0.277 Chamarajanagar 1 478 0.288 Mandya 2 501 0.479 Udupi 3 200 0.60

10 Hassan 4 568 0.8611 Chickmagalur 4 627 0.8712 Bidar 6 854 1.2913 Uttara Kannada 7 264 1.3714 Bijapur 8 539 1.6115 Dakshina Kannada 9 907 1.8616 Tumkur 11 257 2.1217 Raichur 12 707 2.3918 Chitradurga 12 978 2.4419 Shimoga 13 260 2.5020 Bellary 17 341 3.2621 Gulbarga 19 400 3.6522 Bangalore (rural) 21 511 4.0523 Belgaum 27 769 5.2324 Dharwad 30 493 5.7425 Kolar 30 603 5.7626 Mysore 38 436 7.2327 Bangalore (urban) 239 899 45.15

Total 531 366 100.00

Source: District Industries Centre, Karnataka, and author’s calculations.




this and on discussions with firms that havelocated there, it is only because of theestablishment of the GC and provision ofinfrastructure (power, telecoms, paved roads)that this area has been able to attract industry(see Sridhar, 2003, Table 3). It may be usefulto note that, in an otherwise disadvantageouslocation like Satharia, nearly 86 firms havelocated, creating close to 1400 jobs as of2004 (Table 3). This must be a direct impactof the GC infrastructure being in place there.

It should be noted that Bhind (located in adifferent state, Madhya Pradesh), is anotherGC in which one of the largest number ofjobs has been created (7296 in 2004, seeTable 3). Based on census data for 1991,prior to its GC designation, this GC also hada much lower share of employment in manu-facturing (3 per cent) relative to the averagefor the state (Madhya Pradesh) as a whole (6per cent).

These anecdotal examples reinforce thenotion that competition can encourage jobgrowth primarily in backward areas. Onereason why competition encourages jobgrowth in distressed areas is the lower reser-vation wages of the workforce in such areas,since they are likely to accept jobs at lowerwages. If development is not targetedtowards the most distressed areas, such pro-grammes cannot significantly affect thearea’s unemployment rate, as found here.

The next section summarises policy impli-cations arising out of the work based on theeconometric work and qualitative assessmentof the counterfactual based on the field visits.

Policy Implications: GC Designation andReduction of Unemployment

Note that, while the primary objective of thisstudy has been to examine the impact ofGCs on unemployment, in the process of cor-recting for the treatment effects problem in theeconometric work, an understanding of theGC designation process has been gained.This understanding of designation of GCshas been facilitated with secondary data(used in the econometric work) and infor-mation from the field visits (used to

corroborate the counterfactual). The econo-metric results remain statistically poor, theprobit has mixed statistical significance andthe OLS only explains 16 per cent of the vari-ation in its dependent variable. Nevertheless,even the insignificance of (distress) factorssuch as the unemployment rate in the probit,and the insignificance of GC status or its dur-ation on changes in the unemployment rate,have important policy lessons. Based onthese, certain policy implications arise forGC designation, as well as for areas seekingto reduce their unemployment. And, as werecognise, the two are quite closely related.

While on paper, criteria for establishmentof GCs relate to lack of urbanisation and/orpresence of infrastructure, the results fromthe probit model estimation show that thearea’s growth potential and changes in itspopulation are the most important factorsthat determine their actual GC status. As dis-cussed earlier, the population criterion forGC designation should certainly be retainedsince that is the most effective in influencingactual GC designation and reduces unemploy-ment by increasing the supply of labour forfirms.

The findings from the probit also imply thatdistress factors (such as changes in unemploy-ment rate) are not important in the actualsetting up of GCs. This has the followingimplications

(1) Regional disparities in the country willbecome accentuated, if only areashaving higher growth potential are pro-vided incentives to attract firms.

(2) Many areas that could attract jobs withoutspecial incentives, get GC status. Cur-rently, distress criteria such as industrialbackwardness (a measure of which hasbeen used in the empirical work) areused on paper, but that does not seem tobe a determining factor in the actualsetting up of GCs. There can be severalproblems with the way in which GCs areactually set up.

(a) The first problem is that areas thatcould attract industries without GCsare given funds at the expense of the




state and central exchequer. The pro-gramme does cost the exchequerdearly (for example, see expenditureper job, Table 1). Given that GCsoffer firms a number of advantages,a more judicious use is required ofresources spent on the programmethrough careful designation of GCs.The use of geographical targetingand geographical informationsystems to improve poverty mappingin developing countries is supportedby other literature (see Bigman andFofack, 2000).

(b) Secondly, lack of clear criteria makethe designation process arbitrary, ashas been the case in many states.For instance, according to existingcriteria, if an area is located at a dis-tance from an urban area as specifiedby the policy, it can either have theinfrastructure or not have it, but inboth cases it could be designated asa GC! Note criterion (3) for GCdesignation.

Because of their cost implications, criteria fordesignation of GCs should be made clearerand have to be made distress-based. If dis-tress-based criteria were to be adopted in theactual setting up of GCs, then it is possiblethat targeted programmes such as GCs willhave significant local impacts on employment,unlike what is found here.

Further, currently, the size of employmentcreated in the GCs is too small (see Table 2,last column) to have significant effects onthe overall unemployment rate of the districtscontaining them. GCs (infrastructure) shouldbe made much larger in area, being targetedat the distressed areas, so that they maybecome the vehicle of infrastructure develop-ment and an engine of employment growth forlocal economies.

The theory that underlies greater netbenefits from jobs, if they were created in dis-tressed areas, has been discussed at the begin-ning of this paper. It should be emphasisedthat the econometric work in this study doesnot throw light on these hypotheses (although

the anecdotal data do). The data required totest these hypotheses systematically—as forexample, those on individuals’ reservationwages—are not collected by the Census ofIndia or by other organisations such as theNational Sample Survey Organisation(NSSO) that publish micro data at the house-hold level.30 Attempts should be made tocollect such data, which shed more light onregional labour market dynamics than hasbeen the case so far.

Concluding Remarks

The findings from the empirical work pre-sented here are that changes in populationaffect the GC status of an area, along withits growth potential. This has the effect ofaccentuating regional disparities in thecountry. The population criterion for GC des-ignation should be retained. We may notexpect targeted, place-oriented programmessuch as GCs to have favourable employmentimpacts, especially so if they are untargeted.The evidence from the theory, anecdotal sec-ondary data and the field visits suggests thatGCs have to be set up in distressed areas ifsuch programmes have to impact the locallabour force, justify state/local expenditureon the programme and narrow regionaldisparity.

Alternatively, apart from the issue of GCs,if the objective is rather to decrease the unem-ployment of an area, greater urbanisation of,or attraction of population to the area, mightserve to achieve this. Some reasons why thismight happen are because firms reap benefitsfrom agglomeration economies in urbanareas and are likely to locate there andcreate jobs. Further, a novel finding is thatpopulation with skills is a critical input forfirms. Hence, the attraction of residents to anarea with the offer of good-quality public ser-vices might be part of the solution, rather thanattempting to attract firms by means offinancial incentives.

Finally, common sense suggests thatincentives and geographical targeting cannotcontinue indefinitely, even in distressedareas. Recalling that such targeted




programmes originally started as experimen-tal initiatives, once an area’s conditions indi-cate that it is no longer distressed (howeverdefined), its special status (whether GC orSEZ) must be revoked. Thus the time limitfor which the areas would be designated asspecial incentive areas should be specified.In the absence of a time limit—and clear,and distress-based, criteria—all areas andindustries have incentives to lobby foracross-the-board continuation of the specialstatus and incentives indefinitely, havingserious cost implications for the government.

The implications of this work for geo-graphically targeted programmes that aim atconvergence are for a performance-basedprogramme during the period that it is in exist-ence. It should be made performance-basedfor the state or local government administer-ing the programme, for designation criteriathey use, because of their cost implications,and should be performance-based for thefirms locating there for the jobs and relatedeffects they create. Only then can geographi-cally targeted programmes have some favour-able employment impacts on the areasadopting them.

Acknowledgements

The work for this paper was funded by a seedmoney project from the Indian Institute ofManagement (IIM), Lucknow, India. Theauthor wishes to thank IIM–Lucknow forproviding the opportunity to use seedmoney for the research. Thanks are due toofficials in the Census of India in the Officeof the Registrar General of India, who pro-vided access to district-level data. Theauthor also wishes to thank Mr. P. C.Mohanan, of the National Sample SurveyOrganisation (NSSO), for answering ques-tions pertaining to the 55th round, for2000, and also officials in the Departmentof Industrial Policy and Promotion (DIPP),Union Ministry of Commerce, Governmentof India, for providing secondary data onGCs. Thanks are also due to officials inUPSIDC (Uttar Pradesh Industrial Develop-ment Corporation), for devoting time to

answering questions and to officials ofKIADB (Karnataka Industrial Areas Devel-opment Board), HSIDC (Haryana StateIndustrial Development Corporation), GIDA(Gorakhpur Industrial Development Auth-ority) and SIDA (Satharia Industrial Devel-opment Authority), in the various states, foranswering questions, and for their time intalking to various firms. The author thanksfirms that answered several questions, toenable an objective assessment. Manythanks are due to the Reserve Bank ofIndia, Department of Statistical Analysisand Computer Services, specifically, MrMaria, for supplying data on outstandingbank credit and answering many questionspertaining to them. Thanks are due toGovinda Rao, Indira Rajaraman, the threeanonymous referees and the Editors ofUrban Studies for their comments on earlierversions. The author thanks Charlie Beckerfor his suggestions and support and NIPFPfor the time to work on this paper. Anyerrors remain the authors.

Notes

1. This is reasonable to assume for the follow-ing reasons: wages (adjusted for occupation)across areas are usually cost-of-livingadjusted; and, although rural–urban wagedifferentials exist, in the long run, they areequalised because of migration, as inTodaro’s model.

2. The Bawal (Rewari) GC in Haryana recentlyadvertised itself in the Economic Times, aleading business newspaper, that it is thebest global destination for businesses toinvest!

3. Data on total employment (total main (full-time) workers) in the districts were obtainedfrom the Census of India, 2001. The employ-ment created in GCs was calculated as a pro-portion of total full-time employment in thedistricts, as a means of assessing the GCs’contribution to total economic activityoccurring in the area.

4. This implies that the change in unemploy-ment features as an X (independent variable)in the probit and as a Y (dependent variable)in the 2SLS regression, whereas the GCdummy is first a Y and then an X. Becauseof such endogeneity, specification problemsarise regarding the double use of X and Y




variables across the 2SLS. This is the reasonwhy the treatment effects model has beenused. The next section of the paper givesdetails of the estimation in the presence ofendogeneity.

5. The GC dummy is defined according to itsstatus in 2004. For all GCs, I have definedthe duration variable from the month of itsapproval until December 2004. Also, notethat whether or not a GC is completelyoperational on the day it is certified or desig-nated is not relevant. It is only the idea ofmarketing the area as a good place to dobusiness, if not the actual incentives thatcould make a difference to prospectivefirms. This idea supports the constructionof the duration of GC variable since theday of its certification.

6. Incidentally, when I visited the ShajanwaGC, this issue was discussed by GorakhpurIndustrial Development Authority (GIDA)officials in their observations about itseffectiveness.

7. The effect of the GC, its duration andits duration squared, on the change inunemployment rate, over a period of12 months, can be computed as follows:Coefficient on GC dummyþ (Coefficienton duration of GC � 12)þ Coefficient onduration squared � 122). If we wanted toknow the effect of the GC over a period of2 years, we substitute 24 (months) insteadof 12 as above, and so forth.

8. We may note here that, while the proportionof employment in manufacturing andservices determines changes in the unem-ployment rate of an area, there is noreverse causation from unemployment rateto the proportion in manufacturing andservice occupations. We would expect themanufacturing and service base to be deter-mined by exogenous factors such as naturalresources available, skills of the populationand the extent of integration with inter-national markets.

9. In the case of both the years 1991 and 2001, Iexcluded outstanding bank credit for per-sonal purposes such as loans for consumerdurables.

10. These workers are those who were engaged inany economically productive activity for 183days or 6 months or more during the year.

11. These workers include those who worked forless than 183 days or 6 months during the year.

12. This becomes tricky. If we agree that will-ingness to work itself depends on the wagerate, we have to accept that, at very highwages, even those working at home ordependents may be willing to work! So if

wages were to be high, taking into accountonly main workers at any given point intime could be an underestimation of thosein the labour force. I am thankful toGovinda Rao for pointing this out.

13. The Census of India defines settlementshaving the following characteristics asurban areas: a population of 5000 or more;a minimum density of 1000 people persquare kilometre; and, at least 75 per centof the work force outside agriculture.

14. I have calculated weighted average age afterexcluding persons below 15 and above 65,since we are concerned about the effect onthe unemployment rate, of only thoseeligible to be in the workforce.

15. Data on workers in manufacturing andservices in the 1991 Census of India weredisaggregated for categories—rural/urbanand male/female—so I aggregated thesecategories to obtain total employment inthe respective category.

16. Industry classifications from the NSSO areconsistent with those used by the Census,so I am able to compare them.

17. Computed in this way, the proportion ofworkers in manufacturing and services for2000 is the same for districts within a state.

18. I discussed with the NSSO and confirmedthat Census and NSSO data are compatiblefor such computation.

19. In all, these districts are in 27 (out of 35)Indian states/union territories that containGCs. I did not include in the estimation,states/union territories that do not containGCs (these being Chandigarh, Uttaranchal,Delhi, Sikkim, Daman and Diu, Dadra andNagar Haveli, Lakshadweep, Andaman andthe Nicobar Islands); districts in thesestates could systematically be differentfrom their counterparts in states containingGCs. The objective is to compare only dis-tricts that are equally likely to contain aGC, which would not be met if (districtsin) states with not a single GC were to beincluded.

The Census of 2001 was not held in Kutchdistrict, Gujarat, because of the earthquake,and so data were not available for this district.

During 1991–2001, several new districtswere created in the country, mostly carvedout of existing districts. There were alsothree new states (Jharkhand carved out ofBihar, Chhattisgarh carved out of MadhyaPradesh and Uttaranchal out of UttarPradesh) created during the decade. For newdistricts created and districts in these newstates, it is assumed that the data for theparent district (from which it was carved)




are valid. Given the fact that these districts andthose in the state have been formed only rela-tively recently (in 2000), the assumption iscertainly reasonable to make.

20. These three districts were Mahesana(Gujarat), Lahul and Spiti (HimachalPradesh) and Banswara (Rajasthan).

21. It is to be noted that the maximum averageage in the districts is 36 years (both in1991 and in 2001). This does not mean thatthere were no persons in any district duringthe two years that were above this age; thisis the maximum of the weighted averageage calculated for the districts. The weightedage is based on information regardingnumber of people in each of the age-groups(the weights) in the districts.

22. Functioning GCs are those in which at leastone firm has located. According to data Ireceived from DIPP, there were 68 GCs asof 2004, but only 26 of them were fully func-tional in this sense. The other GCs are atvarious stages of land acquisition, develop-ment (installation of infrastructure) andallotment to firms, with no firms located.

23. The meticulous reader should note that theduration data in Tables 1, 2 and 3 are consist-ent. The data in Tables 1, 2 and 3 are basedon the 26 functioning GCs and are presentedat the GC level. The data in Table 5 arebased on the 26 GCs in 28 districts (someGCs span more than one district) and arepresented at the district level. The data inTable 4 are presented for all districts (thosewith and without GCs).

24. For instance, Aiyar (2005) points out howIndia’s Bajaj Auto produced a millionvehicles in the mid 1990s with 24 000workers, whereas in 2004 it produced2.4 million vehicles with just 10 500workers!

25. Given here are a few examples from the lit-erature on treatment effects models withregard to how the duration of the treatmenthas been handled. Sridhar (2003) estimatesthe impact of tax incentives (the treatment)and their duration and duration squared onunemployment rate. Ham and LaLonde(1996) estimate an employment rate hazardmodel for participants in an employmenttraining programme. In the hazard modelestimation, the duration of employment(and squared) and unemployment spell(and squared) are used as independent vari-ables along with other control variables.Korenman and Neumark (1991) employ thetreatment effect dummy (which in theircase is marital status of individuals) as wellas the duration of the effect (number of

years of marriage) on earnings. Ashenfelter(1978) examines the effect of the treatmentby including explicit year dummies forseveral years before the treatment actuallycame into effect. This is one way of handlingduration and is a before-and-after effect ofthe treatment (which in this study referredto the effect of a post-schooling training pro-gramme on participants’ earnings). Thus theliterature on treatment effects treats the dur-ation of the treatment as an independent vari-able. Others treat this kind of model as ahazard model for which, however, a majorextension of such research is needed.

26. Although my interest is to explain only vari-ables that are statistically significant, I probeinto the statistically insignificant GC vari-able to explain it, as that is one of theprimary objectives of the study.

27. In the 2005 budget that was presented inMarch, India’s Finance Minister made a pro-posal to extend urban amenities to rural areas(PURA). As part of the Finance Minister’sproposal, pilot projects are to be taken upfor growth poles, applying PURA principles.

28. Hysteresis is a term borrowed from physicsto explain how the electromagnetic proper-ties of certain materials are completelychanged due to the temporary applicationof certain magnetic fields.

29. The Indian Institute of Management,Lucknow, that funded the study is locatedin U.P.; more than one field visit, onlywithin the state, was permitted. So visits toboth the U.P. GCs (Shajanwa (Gorakhpur)and Satharia (Jaunpur)) were financed bythe project funds. The author had to financepersonally part of the visit to the HassanGC, located in Karnataka, which is roughly2000 km south of U.P. The visit to theBawal GC (Haryana), about 500 km fromLucknow, U.P.’s state capital, and 90 kmfrom Delhi, was covered by the seedmoney fund.

30. Indeed, data on reservation wages are noteven collected by international organisationssuch as the International Labour Organis-ation (http://www.ilo.org).

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Local Employment Impact of Growth Centres: Evidence from India