FANGFANG CHENG*,**, LUC BOERBOOM**, STAN GEERTMAN* &PIETER HOOIMEIJER*
*Department of Human Geography and Planning, Faculty of Geosciences, Utrecht University, PO Box80115, 3508 TC Utrecht, the Netherlands. E-mails: [email protected]; [email protected];[email protected]**Department of Urban and Regional Planning and Geo-Information Management, Faculty ofGeo-Information Science and Earth Observation (ITC), University of Twente, PO Box 6, 7500 AAEnschede, the Netherlands. E-mail: [email protected]
Received: January 2012; accepted July 2012
ABSTRACTUsing county-level employment data, we analyse how the spatial concentration of jobs has changedin China’s Pearl River Delta (PRD) between 1990 and 2005. Despite unique Chinese policies thatexhibit strong influence on the economic landscape, we detect key parallels with the patternsfound in classic theories and empirical studies in Western contexts. Total employment has becomeincreasingly concentrated. This aggregate picture hides important sectoral variations though:manufacturing employment has spread out to suburban areas; producer service jobs have increas-ingly concentrated in metropolitan centres; and consumer and public services have clustered inareas with high aggregate population. We argue that the major forces that are shaping theeconomic landscape in PRD are the market institutions and development path-dependency.Under the circumstances of an increasingly liberalised market and decentralised government,policy now may function as a dynamic tool to magnify local spatial-economic and historicaladvantages and to balance uneven regional development.
Key words: economic geography, spatial concentration, sectoral employment, policy, Pearl RiverDelta, China
INTRODUCTION
For over a century, economic activities havebeen concentrated, often in large metropolitanareas, to take advantage of well-establishedinfrastructure, skilled labour pools, proximityto market, and the exchange of goods andinformation (Marshall 1890; Weber 1929).Uneven distribution of economic activities pro-motes growth in areas with high concentra-tions, and causes stagnation and decline inless concentrated areas, leading to increasing
regional inequality. In China, one of the coun-tries that advocates social equity, high concen-tration has been detected in coastal regions andurban areas, enlarging inter- as well as intra-regional gaps (He et al. 2008; Wei and Ye 2009).Understanding the spatial distribution ofeconomic activities is therefore a subject ofsignificant relevance to both policy-makers andacademic researchers.
While the literature on spatial concentrationof economic activities in China has been accu-mulating, the attention has been focused on
large geographical scale such as provinces (e.g.Ying 2003; He et al. 2008; Groenewold et al.2010; Villaverde et al. 2010). Démurger et al.(2002) demonstrate concentration of econ-omic activities in coastal regions in the 1990s,which has been further reinforced by marketforces and agglomeration externalities inrecent years. They argue that the initial concen-trations of economic activities in coastal areasare primarily due to policy preferences, giventhe geographical advantages of those regions.With temporal data between 1978 and 1998,Ying (2003) reveals a process of economicpolarisation at the provincial level as the mar-ketisation progresses. A variety of spillovereffects are detected, which are attributed to thefactor mobility, transfer payments and techno-logical diffusion in the marketisation process.He et al. (2008) found recent evidences thatindustrial activities have become increasinglyconcentrated at provincial level between 1980and 2003. They further argue that the spatialconcentrations of industrial activities are due toa complex combination of conditions ratherthan simple response to policy or marketforces. They found that globalisation, inter-province competition, and local comparativeadvantages play important roles in the spatialconcentration of industries. However, theanalysis at regional or provincial levels masksimportant spatial differentiations given thatprovinces in China are larger and more diversethan many countries in the world.
Some scholars have considered the effect ofspatial scale in the study of geographical con-centration of economic activities. Using disag-gregated spatial and sectoral data, He et al.(2007) examine the spatial concentration ofmanufacturing activities covering the whole ofChina at both county and provincial levels.Some sectors are found to be highly concen-trated at county level while showing dispersedpatterns at provincial level. With county-levelcensus data in 1990 and 2000, Hanink et al.(2011) expand the county-level spatial analysisby including service activities. They identifypersistent spatial concentrations of economicactivities in coastal and large metropolitanareas. Moreover, they detect sectoral differ-ences between the manufacturing and servicesectors: manufacturing activities have becomeincreasingly concentrated whereas the service
sectors are found to be more uniformly distrib-uted. Again, the geographical scale may chal-lenge the validity of these findings. Forinstance, are service activities spatially concen-trated or dispersed in Shanghai? Although pro-vided with county-level data and analysis, thosestudies with a national scope fail to providedetails and discussions at intra-regional level onthe spatial and sectoral variations that aremasked by the overall trends. Furthermore, it isimportant to realise that those variations areclosely related to regional or local conditionsrather than the international or even nationalconditions.
To bridge the research gap, this paper aimsto add county-level evidences to the literatureon spatial concentration of economic activitiesin China at an intra-regional scale. Usingcensus data in 1990 and 2005 in the Pearl RiverDelta (PRD), one of the fastest growing regionsin China, we identify changes and patterns inthe spatial distribution of employment. Weattempt to link spatial concentration of econ-omic activities to contextual factors, includingpolicy intervention, indigenous developmentpath and market institutions. With the findingspresented later in this paper, we argue thatpolicy in the Chinese context is highly dynamicand adjustable to economic conditions andchanges. It functions as a complementary toolin allocating resources through reinforcingexisting spatial-economic advantages.
ECONOMIC GROWTH AND SPATIALRESTRUCTURING IN THE PRD
The PRD is located in Guangdong Province inSouth China, bordering Hong Kong andMacau. It consists of nine municipalities (boldfonts in Figure 1). Prior to the economicreforms in the 1980s, excepting Guangzhou,the PRD was generally an agrarian economyand had not received particular attention fromthe Central Government. Since the 1980s,industrial development has been encouragedand accelerated by a series of economicreforms which aimed to promote export-oriented manufacturing. The past threedecades have witnessed profound industrialgrowth, transforming the PRD from an agricul-tural region to China’s leading manufacturingcentre, a gateway to China for international
trade (Sit and Yang 1997; Tuan and Ng 2004;Chen 2007) and one of the fastest growingregions in the world (Enright et al. 2005). Twoof the nine municipalities have been theeconomic centres in the PRD – Guangzhou the
provincial capital and Shenzhen the firstspecial economic zone (SEZ) in China.
Studies attuned to the PRD reveal complexand changing patterns of spatial concentrationof economic activities. Historically, economic
activities are concentrated in Guangzhou. Forcenturies, the concentration of internationaltrade and business activities has served as amagnet in stimulating economic activitiesacross this large area (Xu & Yeh 2003).During the Tang Dynasty, approximately 1,400years ago, Guangzhou was the largest port cityin China, and about 30 per cent of Guang-zhou’s residents were foreigners doing businessand trade (Yuan 1985). During wartime in the1930s and 1940s, Guangzhou became the onlyport for foreign trade in China since othermajor ports including Shanghai and HongKong were invaded (Zhang & Ye 1992).Moreover, being the cultural and politicalcentre in south China for centuries, Guang-zhou has a long history of government bodiesand schools. Today Guangzhou hosts approxi-mately 60 per cent of the higher educationinstitutions in Guangdong Province, and ranksthird among Chinese cities in terms of thenumber of universities (Guangzhou StatisticBureau 2010).
Since the economic reforms, the regionalorder in the PRD has been altered, largelyattributed to the regional development policiesand extensive production decentralisationfrom Hong Kong (Leung 1996; Enright et al.2005). In late 1970s, manufacturing firms inHong Kong were facing tremendous labourshortages and soaring production costs (Luiand Chiu 1994). Meanwhile, the Central Gov-ernment of China initiated economic reforms,and designated the PRD to be a special econ-omic region to experiment a market economyin the socialist country. The first two SEZs –Shenzhen and Zhuhai – were established in thePRD in 1980. State-prescribed financial incen-tives and tremendous infrastructure invest-ments were provided. With those policypreferences, the production costs in the PRDwere considerably lower than other regions(Ng 2003; Zhu 1996). The PRD thus providedan ideal place for Hong Kong investors to trans-fer their manufacturing activities (Sit 1998;Leung 1996). Subcontracting activities fromHong Kong were widely distributed in the PRD,stimulating unprecedented economic growth(Leung 1993; Tuan & Ng 2004).
Population growth goes hand in hand witheconomic development: between 1990 and2005, the population of the PRD had increased
by 95 per cent, from 23.5 million to 45.9million. This overwhelming population growth,however, was concentrated in a limited numberof cities and areas. Shenzhen and Dongguan inparticular have experienced high populationgrowth (Figure 2). This population growth ledto a high demand of basic service activities suchas healthcare and education and a variety ofother service activities. Although studies onservice activities in the PRD are limited, evi-dence is found that concentrations of serviceactivities have re-emerged in the old citycentres (Wu & Yeh 1999; Yang 2004; Lin2004). This concentration in the old city centreis consistent with the findings found in othertertiarising metropolitans in China (Zhou &Ma 2000; Li & Wu 2006; Han & Qin 2009).On a city-wide scale, Yi et al. (2011) suggest thatthe distribution pattern of producer services inGuangzhou has gradually changed from dis-persed to centripetal development towards thecentral business district (CBD). The authorsargue that the process is distinct from that ofWestern cities. We should also recognise thatthe resultant concentration in CBDs showssimilar patterns to the ones found in otherlarge metropolitans in China and in Westerncountries.
Since the late 1990s, knowledge-basedeconomy has emerged as the new engine ofgrowth worldwide (Audretsch 1998). TheChinese government has been leading ratherthan following the economic transition towardsa technology- and knowledge-based economy(Zhou & Leydesdorff 2006). In the PRD,inventory of technology- and knowledge-intensive sectors are developed and the centraland local governments provide direct fundingprogrammes to support the research and devel-opment of specific sectors. Moreover, the gov-ernments spend substantial efforts to promotegeographical clusters of technology- andknowledge-intensive activities. High-tech parks,industrial development zones, and universitytowns have been established in major cities anddevelopment zones designated for knowledge-based activities, forming new concentrations ofthe emerging sectors (Shen et al. 2006; Lu &Wei 2007).
The importance of policy in the PRD’sregional development has been extensivelyemphasised (Shen et al. 2002; Chen 2007). We
should also acknowledge that, in the process ofeconomic reform, the market forces started tooperate and to play an increasingly importantrole. Since the early 1980s, deregulation of thecentral government has allowed Chinese peas-ants to diversify agricultural production and toindustrialise the rural economy. Sit and Yang(1997) and Lin (2001) argue that the deregu-lation has given rise to vast rural industrialisa-tion at the grassroots level, which had led to adispersed distribution of manufacturing activi-ties in the PRD. This contradicts the marketinstitution that manufacturing activities tendto concentrate in a few locations to exploitincreasing returns of scale (Krugman 1996). Asthe development of the market progressed, theagglomeration effect started to operate and thedispersed distribution started to change. Sub-contracting manufacturing activities fromHong Kong were found to be concentrated inShenzhen, Dongguan and Guangzhou in thelate 1980s, forming three major manufacturing
centres in the PRD (Leung 1993). Similar con-centration patterns were found for a widerrange of economic activities in the triangulararea between Guangzhou, Hong Kong andMacao (Lin 2001).
More importantly, in China’s transitiontowards a market economy, policies wouldrequire certain market conditions to be success-ful. It can be illustrated with the example of theSEZs policy, which has frequently been cited asa key to Chinese economic success (Jones et al.2003; Zeng 2010). Comparing the two SEZs inthe PRD, Shenzhen and Zhuhai, both hadreceived similar preferential policies andachieved substantial economic growth in the1980s (Lin 2001). In recent years, however,Shenzhen has become much more successfulthan Zhuhai in terms of attracting investmentand economic growth (Zeng 2010). Whileacknowledging that policy has contributed sub-stantially to Shenzhen’s success, we should alsobe aware of the fact that policy was made to
Note : Sorted by figures in 1990 from left to right.Source : Guangdong Statistic Bureau (1991, 2006).
some extent to accommodate market circum-stances. Under the national strategy of ‘puttingeconomic development first’, the central andlocal governments function as efficiency- orprofit-oriented entrepreneurs. Policies areimplemented in such a way that policy-makersare willing to consider and adapt to the marketconditions. Shenzhen possesses geographicaladvantages over Zhuhai given its proximity toHong Kong. In addition, Shenzhen SEZ has aland area of 327 km2 (expanded to 1953 km2 in2010), far exceeds that of Zhuhai SEZ (6.8 km2
in 1980, and then expanded to 15 km2 in 1983and again to 121 km2 in 1988). It is thereforeunderstandable that investors favoured Shen-zhen more than Zhuhai. Policy-makers quicklyresponded to the market changes in strategicplan and resource allocation. For example, adouble-track Guangzhou-Shenzhen railway wasapproved in 1984 and completed in 1987,shortly after the establishment of ShenzhenSEZ. However, until 1993, a similar railwayproject connecting Zhuhai and Guangzhou wasapproved by the Central Government but soonsuspended in 1997 due to lack of funds andincentives (Guangdong News 2007).
In the post-reform era, as the market maturesand as the service- and knowledge-based indus-tries develop, the spatial distribution of econ-omic activities are expected to exhibit parallelpatterns between the essence of market institu-tion and what has taken place in urban China.Formanufacturingactivities,giventhehistoricallegacy of rural industrialisation and concentra-tions of manufacturing in large metropolitanareas, we hypothesise that manufacturing activi-ties become increasingly concentrated at placessurrounding large metropolitans to avoidagglomeration diseconomies yet not too far toexploit agglomeration economies. For serviceactivities, based on the findings of Han and Qin(2009) and Yi et al. (2011), producer service isexpected to be increasingly concentrated incities of large metropolitans. Consumer serviceon the other hand, is expected to have similarconcentrationpatternswiththeaggregatepopu-lation. These sectors share common featuresthat provide service to people in general andwhich can only be consumed at the point ofproduction. Therefore their spatial distributiontends to follow the patterns of the consumers,namely, the population. Public sectors such as
education, healthcare and welfare are expectedto have similar patterns with consumer services,since these are the sectors that are supposed toprovide basic public services for everyone andtherefore should also follow the distribution ofthe population.
DATA AND METHODS
Data – Many empirical studies in this field arebased on employment data (e.g. Krugman1991; Polese & Shearmur 2006), given theadvantages of being easy to access, relativelystable over time, and simple to calculate. Thishowever may lead to biases towards labour-intensive industries. Alternative indicators,such as output, value-added and number offirms are often used in combination withemployment data to avoid such biases (e.g.Viladecans-Marsal 2004). In the case of thePRD, statistics of those indicators are only avail-able from statistic yearbooks. However, the sta-tistical coverage on those indicators hasencountered major alterations in the year of1994 and 2004 (Guangdong Statistic Bureau1995, 2006), and thus the figures are not com-parable over time. Therefore, we chose to useemployment data which are collected in censussurveys on a household basis.
County-level sectoral employment data arederived from two surveys: Guangdong CensusSurvey 1990 (Census Office of Guangdong Prov-ince 1992) and 1% population survey 2005(Census Office of Guangdong Province 2007).1
The coverage of employment data in these twostudy years varies. In 1990, the employment datain the census covers all the resident population,which is defined as the persons with a minimumstay of 12 months at the survey place. The resi-dent population-based survey data helps elimi-nate a common problem of excluding residentpopulation without local hukou (household reg-istration) in many published statistics in China.Similarly, the 2005 data is also residentpopulation-based.Theonlydifferenceis that theminimum stay requirement has been reduced to6 months, meaning the 2005 data has a largercoverage than 1990. Given that population flowswere more dynamic in 2005 than in 1990, weconsiderthereductionofminimumstayrequire-ments reasonable and the consequent biases inthe statistics acceptable.
Although complete information of employ-ment data is provided, there are two importantalterations related to the statistics in the studyperiod. One is the merger or split-up of admin-istrative counties, which leads to changingnumbers of counties.2 To maintain a consis-tent list of counties, we thus combined thosecounties experienced administrative changes,resulting in 43 counties as an analysis unit. Theother is the Chinese industrial classification ofsectors, which has been altered repeatedly tobe compatible with the International StandardIndustrial Classification of Economic Activi-ties. The latest industrial classification enactedin 2002 was used to reclassify the 1990 datainto 19 sectors, making it compatible with the2005 data.
Analyses methods – Prior to the spatial analysis,absolute and relative growth for all 19 sectorswas tabulated, so as to provide information onthe magnitude of employment growth in eachsector. To examine the patterns and changes ofthe spatial concentration of employment, weaim at answering four questions: what are thesectors that were highly concentrated in 1990and 2005? What are the sectors that havebecome more concentrated over time? Whatare the locational patterns of the spatial con-centration of employment? Have the locationsof concentration changed over time? Theformer two questions are concerned withoverall concentration of each sector in PRD,thus global measures of concentration areneeded. The latter two require local measuresof concentration since locational patterns andchanges of concentrations are central to answerthose two questions.
One frequently used global measure ofspatial concentration is the locational Ginicoefficient (e.g. Krugman 1991). It is definedas:
Gn x
x xis
si sjji
= −∑∑12 2
where n is the number of counties; x is theemployment share of county in a sector s; thesubscript of i and j denotes county i and jrespectively. The Gini coefficient rangesbetween 0 when employment is absolutely
evenly distributed in all counties and 1 when allemployment is concentrated in a single county.The higher the value, the more concentrated asector is. This coefficient in fact measures thelevel of localisation, or localised concentrationof one sector in a region.
A major drawback of the locational Gini coef-ficient is that it does not distinguish a randomor non-random spatial unit. It cannot provideinformation on the geographical distributionpattern of economic activities. A high value tellsus a high concentration in the study area but itdoes not tell if it is spatially clustered or ran-domly distributed. It is also the case with itsmain alternative, the Hirschman-Herfindahlindex (for a review, see Kim et al. 2000).Another drawback of Hirschman-Herfindahlindex is that it ignores the size of the geographi-cal areas within which economic activitiesoperate.
A relatively new measure, the Ellison-Glaeserindex, presents a major improvement of earlierconcentration measures. Using a dartboardapproach, it considers the spatial concentra-tion of employment that may be the result ofa random distribution (Ellison & Glaeser1997). However, the Ellison-Glaeser index isnot suitable for this study because it requiresplant level employment data which are notavailable. Therefore, we chose to use locationalGini coefficient to measure concentration anda complementary measure, the Moran’s Istatistic, to detect spatial association or spatialclustering.
The Moran’s I statistic is calculated as:
InS
w e e e e
e e
ij i jji
ii
=−( ) −( )
−( )
∑∑∑0
2
where n is the number of counties; ei is theemployment of county i; wij are the elements ofthe weight matrix where wij = 1 if the county iand county j are neighbours and wij = 0otherwise; and S0 is the sum of the elements ofthe weight matrix: S wij
ji0 = ∑∑ . If I values are
larger than the expected value E(I) = -1/(n -1), it indicates positive spatial autocorrelation;whereas smaller values represent negativespatial autocorrelation.
This method assesses if counties with similarlocations have shared similar (or distinctive)employment. We used contiguity as the basisfor spatial relationships and constructed aspatial weight matrix. Inference was based ona permutation approach, with 9,999 permuta-tions, and the significance level is set to 5 percent. The value of Moran’s I ranges between–1 and 1. A positive Moran’s I indicates thatsimilar values tend to occupy neighbouringcounties and thus the existence of positivespatial autocorrelation, whereas a negativeMoran’s I implies that high values tend to beadjacent to low ones, representing negativespatial autocorrelation. If the spatial distribu-tion is completely random, the expected valueof Moran’s I approximates 0, indicating theabsence of spatial autocorrelation (Anselin1999).
Since the Moran’s I is a global measure anddoes not indicate the locations or the types ofspatial cluster, we further use the local indica-tors of spatial association (LISA) developed byAnselin (1995) to identify local clusters of posi-tive or negative spatial autocorrelation. It iscomputed as:
I z w zi i ij jj
= ∑
where zi is the standardised form of ei; and thewij is the same elements of the weight matrix asdefined in the global Moran index. The sum ofall the local Moran’s indices is equal to theglobal Moran’s index.
The LISA statistics measure whether a coun-ty’s standardised sectoral employment is closerto the values of its neighbours or to the PRDaverage. By plotting the spatial lag against thestandardised value of employment, LISA allowsvisualising four categories (allocated in fourquadrants) of local spatial association: high-high (HH) (top right quadrant) indicates acounty with an above-average value is sur-rounded by neighbours whose values areabove-average; high-low (HL) means an above-average county is adjacent to below-averageneighbours; and vice versa for low-low (LL) andlow-high (LH) quadrants. The significancelevel was set at 5 per cent and 9,999 permuta-tions were used to identify counties with signifi-cant spatial autocorrelations. LISA cluster maps
were subsequently produced to show the distri-bution of spatial clusters.
Sectoral growth and transformation – Theeconomic growth in the PRD has been stagger-ing since the economic reforms in the 1980s.This has been translated into extensive employ-ment growth as well. Between 1990 and 2005,the total employment has increased from 13million to 29 million (Table 1). This is largelydue to the growth of non-agriculture sectors.The industrial and service sectors’ sub-totalemployment have increase by 11 million and 6million jobs respectively, whereas the employ-ment in agriculture has decreased by 2 million.Half of the jobs were concentrated in the indus-trial sector (54.3%) by 2005. The industrialsector has replaced agriculture (43.7% in 1990)to be the largest among the three broad sectors.It is not surprising to see the marked shift ofemployment moving away from agriculturetowards industrial and service sectors, whichreflects rapid industrialisation and urbanisa-tion in the delta.
Among all the sectors, manufacturing is nodoubt the most dominant one, in both absoluteand relative terms. This sector has had an abso-lute employment growth of more than 10million between 1990 and 2005, contributingtwo thirds of the total employment growth. In2005, half of the jobs, namely, more than 14million, were concentrated in manufacturing.Being a gigantic sector, manufacturing has asurprisingly fast growth rate of 9.4 per cent,almost twice as much as the overall growth rateof 5.2 per cent. Most of the jobs were created byinvestments in labour-intensive export produc-tions, which were attracted by cheap land andlabour in the delta (Enright et al. 2005).
The highest growth rates are found inservice sectors. The top three fastest growingsectors include renting and business service(24.2%), real estate (19.7%), and informationand communication (13.7%), which are, if incombination with the sector of finance andinsurance (8.3%), considered as producer ser-vices. Similarly, the consumer service sectorssuch as accommodation and catering (12.6%),
wholesale and retail (8.5%) and householdservice (7.2%) have also achieved high growthrates. With a growth rate of 12.2%, the sectorof culture, sports and media stands out.
Spatial patterns of concentration and disper-sion: an overview – For each of the 19 sectors,global measures of concentration, the loca-tional Gini coefficient and global Moran’s I, arecomputed to measure the level of localised andclustered concentration in 1990 and 2005(Table 2). The Gini coefficient indicates thatthe most concentrated sectors in 1990 areservice activities. These include research andtechnical service (0.785), renting and businessservice (0.593), real estate (0.534), public facil-ity management (0.528) and culture, sportsand media (0.505). From 1990 to 2005, thoseservice sectors have maintained high concen-tration except public facility management.
Additionally, the sectors whose locations aredriven by natural advantages are also found
being concentrated, including mining (0.687)and agriculture (0.547). The least concen-trated sectors in both years are found mostlyin public service sectors (utility, education,healthcare and social welfare, government andsocial organisation) and consumer services(wholesale and retail, accommodation andcatering).
The values of Moran’s I are low for all sectorsin 1990s, ranging from 0.036 (agriculture) to0.276 (education), showing little evidence ofspatially clustered concentration. The Moran’sI for overall employment is insignificant andextremely low (0.001), suggesting no sign ofspatial clustering of aggregate employment atthat time. By contrast, it becomes significant in2005, and the value goes up to 0.186, signallinga trend of increasing spatial clustering of aggre-gate employment. Similarly, most individualsectors show statistically significant spatial clus-tering in 2005. The values of Moran’s I for eachsector have also increased considerably. High
Table 1. Sectoral employment in the PRD, 1990 and 2005 (thousand persons).
concentrations are found in service sectors,including real estate, household service,renting and business service, finance and insur-ance and wholesale and retail.
One change worth noticing is manufactur-ing’s decrease in values as well as ranks in bothindices. This suggests that manufacturing hasundergone considerable dispersion in thestudy period. In the next section, we are goingto analyse the locational patterns of thosechanges.
Location pattern of spatial changes – For eachof the 19 sectors, we computed LISA statisticsand produced cluster maps, showing the coun-ties with significant LISA statistics and indicat-ing the quadrants which these counties belongsto. Among the 19 sectors, we identified similarlocation patterns for sectors with similar loca-tion requirements. For instance, householdservice as well as accommodation and catering
are consumer services which follow the popula-tion. Both sectors show high concentrations inGuangzhou in 1990 and by 2005 the concentra-tion shifted to Shenzhen, since Shenzhen over-took Guangzhou to be the most populated cityin the PRD during the study period (Guang-dong Statistic Bureau 2006).
Considering the limited space in this paper,we aggregated the sectors with similar locationrequirements into sub-groups: manufacturing(single sector, because this is the largestsector), high-order service (or producerservice, including information and communi-cation, finance and insurance, real estate,renting and business service, and research andtechnical service), consumer service (whole-sale and retail, accommodation and catering,and household service), and public service(public facility management, education,welfare, culture, sports and media, and govern-ment and social organisation). We left the
Table 2. Localized concentration (locational Gini) and clustered concentration (Moran’s I) index of 19 sectors in PRD.Top five sectors in both indices and both years are shaded with a tint.
sector of transport and storage aside, as it is amixed sector of producer, consumer serviceand public services and thus would cause sub-stantial bias to the aggregated employmentdata. Results for individual sectors are avail-able upon request.
Manufacturing – Although the degree of spatialclustering of manufacturing has decreased asreported by global Moran’s I, the local patternsof spatial association seem to be reinforced. In1990,HHandLHareasare found inGuangzhouand Shenzhen (Figure 3a), indicating a spatialconcentrationofmanufacturing in thecitycoresand the surrounding areas of Guangzhou andShenzhen. Turning to the LISA map of 2005, itshows the clusters of manufacturing have shiftedto Dongguan and the outer districts of Shen-zhen. LH areas are found in the city core ofShenzhen and Huizhou, suggesting a high con-centration of manufacturing in the surroundingareas. Combining these results with thedecreased global measures, both Gini andMoran’s I, there is a trend that manufacturingactivities are shifting away from Guangzhou andShenzhenmetropolitancentres towards thesub-urban areas in Shenzhen and Dongguan. This isin line with our expectation that manufacturingactivities suburbanise to surrounding areas oflarge metropolitan centres.
What are the principal factors underlyingthe empirically observed concentration ofmanufacturing in the PRD? Both governmentpolicies and market mechanisms are two
general factors identified. Compared withWestern countries, China has a different historyof urban development, which has been stronglyinfluenced by the changing industrial policies.Prior to the economic reforms, industrial activi-ties were emphasised as the first priority ofurban development. Under the ‘productionfirst, living conditions later’ national strategy,urban land was allocated to state manufactur-ing enterprises free of charge for an infiniteperiod (Gar-on Yeh & Wu 1999; Zhu 1999; Ma2004). Moreover, industrial activities occupiedcentral locations given the absence of marketcompetition for optimum locations under theallocation system (Wei 1993). For housing andcommercial activities, on the other hand, theleast amount of land was granted (Zhu 2005).This resulted in a disproportionate distributionof economic activities in Chinese cities, and thisspatial structure did not change prior to theeconomic reforms.
China’s initial reforms started in the 1980s toexperiment with market mechanisms. In the1990s, the economic reforms accelerated andthe market economy has gradually consoli-dated. Emerging private firms began to partici-pate in the market economy and thesignificance of state enterprises has graduallydeclined (Jefferson & Rawski 1994). In 1992,official documents were issued which allowedinefficient, underperforming state enterprisesto change their ownership structure. The 1990switnessed a large number of laid-off workersand factory closures and an outward movement
Figure 3. LISA map of manufacturing in 1990 (a) and 2005 (b).
of production activities to the urban periphery(Zhou & Ma 2000; Ding 2003; Ma 2004).
Guangzhou has experienced this economicand spatial transformation. Privatised stateenterprises as well as private firms, especiallynew firms, have chosen suburban locations formanufacturing activities under market macha-nisms. Meanwhile, Shenzhen has also experi-enced an outward movement of manufacturingfirms from the inner SEZ to outer districts andeven neighbouring cities such as Dongguan,driven by rising land and labour costs as well asstrict planning control (Bruton et al. 2005). Inan report prepared by the municipal plan-ning bureau, local firms were asked to identifythe main reasons for deciding to move. Highcosts of land and labour, limited space, andstrict planning/environmental regulations areamong the top three on the list.
High-order serviceIn the 1990 LISA map (Figure 4a), all HH areasare found in the city of Guangzhou, indicatinga high concentration of high-order serviceemployment in Guangzhou city. In 2005, theHH areas are found in both Guangzhou andShenzhen city. However, the spatial extent ofHH areas in Guangzhou decreased to only twocentral districts and more HH areas are foundin the urban districts of Shenzhen. The chang-ing patterns of concentration suggest a persis-tent but contracted spatial concentration inGuangzhou city, and the emergence of a newconcentration in Shenzhen.
This is in line with the general spatial pat-terns found in the western literature wherehigh-order services are concentrated in centrallocations of large metropolitan areas (Marshallet al. 1987; Sassen 1990; Coffey & Shearmur2002). Under market conditions, the majorlocation factors for high order services areskilled labour pool, other service activities asforward business linkages, and backward link-ages including research institutes, universitiesand specialised consultants that providecomplementary services and inputs (Coffeyet al. 1996). In general, these elements are avail-able both in greater scope and quantity in largecities.
Guangzhou’s persistent concentration maybe attributed to its long-established role ofeconomic centre in south China, as well as adevoted local government to promote highorder service development (Yi et al. 2011). Theshift of employment concentration towardsShenzhen, however, is the result of mixedforces. The rise of Shenzhen as a new mega-cityhas definitely contributed to the growth of highorder service in this area. Other than that, gov-ernment policy may also be a major forcereshaping the geography of high order servicesectors in the PRD. The most influential poli-cies are the ones related to insitutional reforms.In the early stage of economic reforms, Shen-zhen’s local government established the firstland market in China. Earlier all land was allo-cated by the government. Furthermore, theestablishment of the Shenzhen Stock Exchange
Figure 4. LISA map of high order service in 1990 (a) and 2005 (b).
facilitated local firms’ ownership reform frompublicly owned towards joint-venture andshare-holding companies. These instituionalreforms permitted market mechanisms to playa role in allocating land and capital, and thuscreated a platform for real estate and financialdevelopment. In addition to the local govern-ment’s efforts, the central government also pro-vides policy incentives for Shenzhen’s highorder services, given Shenzhen’s congenital dis-advanteges in skilled labour and research facili-ties. For example, the tax rate for finance firmsis 15% in Shenzhen and Zhuhai SEZs, whereasin Guangzhou it was 33% (Ng 2003). The 1990sand 2000s have witnessed an outward move-ment of high order service firms from Guang-zhou to Shenzhen and Zhuhai. Lastly,complementary policies are provided by Shen-zhen municipality to attract skilled labour inhigh order service sectors. These includehousing subsidy, flexible Hukou policy and con-sistent efforts in improving the city’s amenities(Bruton et al. 2005). Those policies promoted arapid growth of the finance and real estatesectors and paved the way for Shenzhen to bean emerging centre for high order service inSouth China.
Comparing Shenzhen and Guangzhou, wecan conclude that both market and policyfactors play an important role in determiningthe spatial distribution of high order serviceemployment. Nevertheless, it is difficult toargue to what extent each factor contributes tothe resulting distribution. By taking market
constraints and advantages into consideration,complementary policies may be effective toovercome the congenital drawbacks and topromote sectoral employment growth.
Consumer service and public service – For thesectors of consumer service, all six HH areas in1990 are found in Guangzhou, including foururban and two suburban districts, showing alarge spatial extent of concentration centeringin the city (Figure 5a). One LH area is locatedin a suburban district (Huangpu) in Guang-zhou, as it is surrounded by high values ofneighbouring districts. In 2005, three of thefour HH areas are found in Shenzhen(Nanshan, Bao’an and Longgang); only one isleft in Guangzhou (Figure 5b). It suggests thatShenzhen has caught up during the studyperiod and formed a new centre for consumerservice employment. Also, three of the four HHareas are suburban districts of the two largecities (Panyu, Bao’an and Longgang). The twoLISA maps clearly demonstrate that the spatialconcentration of consumer service sectors haveshifted from Guangzhou to Shenzhen, andfrom the city towards the suburb.
The two LISA maps for public service(Figure 6a and 6b) show identical spatial con-centration patterns for consumer service. In1990, all five HH areas are found in Guang-zhou. Similarly, the concentration in 2005 hasshifted to the suburban districts in Guangzhouand Shenzhen (Panyu, Bao’an and Longgang).This result is in line with our expectations that
Figure 5. LISA map of consumer service in 1990 (a) and 2005 (b).
both consumer service and public service aredependent on proximity to the users, thereforetheir spatial distribution tends to follow thedistribution of aggregate population.
DISCUSSION AND CONCLUSION
The urban growth in the PRD have long beendriven by industrialisation powered mainly byexternal capital and markets (Enright et al.2005). In the post-reform era, as the overalleconomy become more dependent on theemerging service- and knowledge-basedsectors, new patterns are detected in the sec-toral and spatial distributions of economicactivities. By 2005, the economy in PRD hadbeen dominated by manufacturing. But thefastest growing sectors are found mostly inservice sectors. The results of the spatial analy-sis provide compelling evidence on dispersionof manufacturing employment in the region,and the shifting concentrations of manufactur-ing jobs from metropolitan centres towardssuburban areas. By contrast, most servicesectors, especially the knowledge-intensiveones, are found to be highly concentrated inthe cities of metropolitan areas, creating a core-periphery structure in the PRD. Those locationpatterns at an intra-regional scale are similar tothose found in the Western context (Eng 1997;Krugman 2011).
The spatial distribution of economic activitiesare configured by a complex mix of policy, path-dependency, and market forces. Policy has long
been considered to be a dominant factor inshaping the spatial distribution of economicactivities, especially at a national scale (Joneset al. 2003; Groenewold et al. 2010). At an intra-regional scale, special locales with preferentialpolicies prescribed by the central and local gov-ernmentshavegainedinitialadvantages toboostthe local economy. As the Central Governmentdecentralises, local governments become moreentrepreneurial and local policies are highlysensitive to market and pre-existing conditions.Therefore, path-dependency and market forcesbecome increasingly important in allocatingresources and (re-)shaping the economic land-scape in the PRD.
These findings have important implicationsfor policy-making. First, the growth of manufac-turing in the suburbs of large metropolitanareas and secondary cities can help generatejob opportunities and promote economicgrowth, and thus alleviating regional inequal-ity. Future changes in the spatial distribution ofmanufacturing will most likely reinforce theshifts towards suburb areas and secondarycities. Those areas with obvious locationaladvantages, such as the airport, harbor, high-way access, will consolidate their industrial- andservice-based sectors. In contrast, increasingnegative externalities such as high wage, short-age of land and congestion in the cities willfurther erode the dominant role of the metro-politan centres.
The high concentration of public servicesectors, such as education in large metropoli-
Figure 6. LISA map of public service in 1990 (a) and 2005 (b).
tan areas, may be reinforced in the years tocome. First, skilled labourers in these sectorsare likely to seek job opportunities in betterinstitutes in metropolitan areas. Second, with alarge skilled labour pool, large cities canprovide better public services, attracting peoplefrom smaller cities and rural areas. This createsa large market in those sectors in large cities. Asa consequence, private hospitals and schoolshave emerged and have been rapidly expand-ing. Profits have stimulated the public sectorsto partially commercialise.
There are a few important limitations of thisresearch. First, the spatial analysis is based onemployment data, which could lead to a biasedtowards labour-intensive sectors such as manu-facturing. The contribution of some lesslabour-intensive sectors, such as finance, maybe under-estimated. It would be an interestingattempt for future research to analyse thespatial concentration patterns using other indi-cators in combination with employment. Thesecond limitation is that we lack detailed exami-nation of some large sectors, such as manufac-turing. Particularly, the high-tech firms withinthe manufacturing sector may have some quitepatterns different from traditional manufactur-ing firms. It would be an interesting topic forfuture research.
Acknowledgements
The authors would like to thank Professor RichardKlosterman for his constructive suggestions on anearlier version of this paper. Dr. Pu Hao’s detailedcomments in improving this paper are also highlyappreciated. We are also indebted to the editor andtwo anonymous reviewers for their contributionwhich substantially improved the final version of thispaper.
Notes
1. China has conducted the National PopulationCensus in 1953, 1964, 1982, 1990 and 2000 respec-tively. Since 1982, there has been a 1% NationalPopulation Survey for updating population dataevery five years after the last census. ‘1%’ is just abenchmark percentage for sampling and citiescan have their own sample standards.
2. There were 40 counties in 1990 and 52 in 2005.
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