Agglomeration And

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Agglomeration and productivity: Firm-level evidence from China'stextile industry

Hui-Lin LIN a,, Hsiao-Yun LI a, Chih-Hai YANG b

a Department of Economics, National Taiwan University, Taipei, Taiwan, ROCb Department of Economics, National Central University, Jhongli, Taiwan, ROC

a r t i c l e i n f o a b s t r a c t

Article history:

Received 8 January 2010

Received in revised form 17 March 2011

Accepted 28 March 2011

Available online 9 April 2011

Is the spatial concentration of manufacturing activity able to enhance firm-level productivity?

This question is particularly relevant to production in China, which has a huge territory and

population, but a skewed distribution in terms of urbanrural development. This paper aims to

examine the dynamics of industrial agglomeration and the impact of agglomeration on firm-

level productivity in China's textile industry by using a firm-level panel dataset from 2000 to

2005. First, the average value of the EllisonGlaeser (EG) index (city level) is found to beapproximately 0.00019. Moreover, the calculated city EG index of spatial concentrationfor each

year exhibits a decreasing trend of spatial agglomeration for garments and other fiber products,

but an increasing trend for the textile industries' agglomeration in China. The above findings

are similar to the findings of Lu and Tao (2009). Secondly and importantly, this study finds

an inverted U-shape relationship between agglomeration and productivity. It suggests that

while industrial agglomeration enhances firms' productivity, agglomeration diseconomies may

appear if the degree of agglomeration is too high. 2011 Elsevier Inc. All rights reserved.

JEL:

L67

L14

R39

C33

O53

Keywords:

Textile industry

Agglomeration

Productivity

China

1. Introduction

The tendency toward the spatial agglomeration of production activity was a widely observed phenomenon in past decades,especially for high-tech industries. The successful experience of industrial agglomeration, e.g., Silicon Valley in the U.S.A., hasattracted wide attention among economists and inspired a stream of research referred to as the New Economic Geography thathas been revitalized by Krugman (1991, 1998). While agglomeration can be discerned at various geographical levels, such as at the

city and provincial levels, the spatial concentration of manufacturing activity is thought to benefit from the advantages ofknowledge externalities, specialization, the existence of local public goods, and reduced transaction costs within industries, all ofwhich can positively contribute to the firms' productivity. Recently theoretical developments, e.g., Neary (2001), and Durantonand Puga (2004), have provided comprehensive explanations of the micro-foundation of agglomeration.

An important question worth examining is whether the spatial concentration of manufacturing activity really enhancesfirms' productivity. Furthermore, most of the literature relating industrial agglomeration to economic activity focuses on advancedcountries, providing the main inspiration for this study: Does the New Economic Geography theory based on Westernexperiences apply to China? This question is both interesting and important. On the one hand, it is particularly relevant to

China Economic Review 22 (2011) 313329

Corresponding author at: Department of Economics, National Taiwan University, No.21, Hsu-Chow Road., Taipei, 100, Taiwan, ROC. Tel.: + 886 2 23519641x377;

fax: +886 2 23225657.

E-mail addresses: [email protected] (H.-L. Lin), [email protected] (H.-Y. Li), [email protected] (C.-H. Yang).

1043-951X/$

see front matter 2011 Elsevier Inc. All rights reserved.doi:10.1016/j.chieco.2011.03.003

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production in China, in view of its huge geographic territory and population coupled with a high degree of regional inequality(Fleisher & Chen, 1997; Kankur & Zhang, 1999). On the other hand, China's economic reforms that have transformed the economyfrom a centrally-planned to a market-based economy further complicate the applicability of this argument.

The textile industry has traditionally been an important industry and has played a critical role in terms of output, exports andemployees in China's manufacturing sector. It remained well developed for a long time since the early 1950s during the centralplanning period.1 Even though China has aggressively developed its high-tech industries since the late 1990s, and has aimed totransform its primarily labor-intensive industries into technology-intensive industries in its manufacturing sector to meetthe challenges of the current era of a knowledge economy, the textile industry has remained one of its key industries. In 2007,the output of the textile industry amounted to RMB 263.33 billion, accounting for 6.5% of total industrial output of RMB4051.77 billion. The textile industry's exports amounted to US$ 16.580 billion (approximately RMB 128 billion), implying thatapproximately50% of its products were being exported to othercountries. Indeed, China is the largest producer as well as exporterof textile and clothing products in the world.

Unlike the newly-developed high-tech industries that are concentrated in Beijing, the Yangtze River Delta, and the Pearl RiverDelta due to the advantages of reduced transaction costs under the industrial structure of vertical disintegration, with rawmaterials being spread over a wide area,2 lower product prices, and high transportation costs, it does not seem to be necessary fortextile firms to benefit from agglomeration advantages. Specifically, in the early 1950s, the government asked the textile industryto begin deploying its resources in a raw material-oriented, market-oriented, and labor-oriented fashion, in order to shift textile

firms toward the areas from which the raw materials originated, as well as areas with a concentration of workers, and consumermarkets. In this way, the textile industry could be developed in the hinterland and in areas where minority ethnic groups wereclustered. Along with the open-door policy implemented in the early 1980s, the spatial concentrations of textile firms, however,

appear to have gradually changed. Liu and Ren (2004) showed that 80% of textilefi

rms in terms offi

rm numbers wereconcentrated in the southeastern coastal regions in 2002. The location quotient values of the textile industry in Zhejiang andJiangsu provinces increased correspondingly to more than 2,3 suggesting a higher degree of spatial concentration. Thus, it might beasked whether there really was an increasing degree of spatial agglomeration among textile firms along with China's accession tothe WTO in 2001 that forced textile firms to enter the large markets in the coastal regions to meet the demand there and also toenable them to export to international markets more conveniently. While some Chinese scholars have discussed this possibledevelopment of concentrated clusters in the manufacturing sector (Shiung, 2005; Wang & Wang, 2005; Zhang, To, & Cao, 2004),they have not used microdata to conduct in-depth examinations of the degree of agglomeration. One of the main purposes of thisstudy is therefore to explore the dynamics of agglomeration in China's textile firms using the EllisonGlaeser (EG) index of spatialconcentration.

More importantly, if there is an increased trend of agglomeration in the textile industry that is caused by the firms' self-selection, it needs to be asked whether the industrial agglomeration positively contributes to firms' productivity, as is the case inthe developed countries. In fact, the relationship between agglomeration and productivity has been widely examined empirically

and has tended to exhibit a signifi

cantly positive relationship, e.g., Ciccone (2002) and Lall, Shalizi, and Diechmann (2004). Whilethe issue of productivity is of much concern and is claimed to serve as one of the major driving forces of sustainable economicgrowth, e.g., Young (2003) and Yang (2009), no studies have systematically examined the relationship between agglomerationand firm-level productivity in China. This paper therefore aims to fill this gap in the empirical literature regarding the applicationof the New Economic Geography theory to a transition economy, China.

This paper analyzes the dynamics of spatial agglomeration and the impact of agglomeration on firm-level productivity inChina's textile industry by using a panel dataset constructed from the Chinese National Bureau of Statistics. The dataset covers22,152 textilefirms from 2000 to 2005, yielding 83,801 observations. We first adopt the spatial agglomeration index developed byEllison and Glaeser (1997), the so-called EG index, to measure the degree of spatial agglomeration and dispersion within thetextile industry. The resultsshow that market-oriented agglomeration occurs in the textile industry to the extent that there was anincreasingly spatial concentration toward the southeastern coastal provinces during the 20002005 period. The agglomerationarises from the benefit of close proximity to large markets and convenience in exporting. The regression analysis that seeks toquantify the relationship between agglomeration and productivity leads to results that are consistent with previous findings in

that the degree of spatial concentration does have a signifi

cantly positive impact on enhancing productivity in China's textileindustry. Moreover, some interesting findings regarding the influences of productivity are also discussed in this study.The remainder of this paper is organized as follows. Section 2 begins by discussing the nature of agglomeration and

productivity and then provides a summary review of the empirical literature on the impact of agglomeration on firm-levelproductivity, with a particular focus on studies related to Chinesefirms. Section 3 examines the dynamics of spatial agglomerationin China's textile industry. Section 4 introduces the data and demonstrates the descriptive statistics. The empirical specificationsand estimation results are explained and discussed in Section 5. The final section presents the conclusions and policy implications.

1 For the development and corresponding industrial policy of the textile industry in China, please refer to Li et al. (2009).2 The main production areas for cotton and linen include the coastal regions of Jiangsu and Shandong, as well as the inland regions of Anhui, Hubei, Jiangxi,

Shanxi, and Sichuan.3 The location quotient index refers to the ratio of the net output of an industry to the whole industrial net output in a region, and to the industrial net output

to the country's net output for all industries. It can be expressed in the form of the following formula: Z= (g1/g2)/(Q1/Q2). Z1 shows that the industry is not thebasic industry in this area. ZN1 indicates that the industry is a specialized sector in this area.

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2. Agglomeration and productivity

2.1. The nature of agglomeration and its potential impact on productivity

Following Krugman's work, there have been numerous theoretical developments on advancing the theory of agglomeration.4

According to Porter's (1998) definition, industrial agglomeration refers to the geographical clustering of a group of companiesand institutions which are related in terms of specific production and/or economic activities. Why do firms choose to concentratethemselves geographically? Krugman (1991) provided three possible reasons: agglomeration provides a labor market pooling;a higher degree of industrial agglomeration can support non-trade specialized inputs and improve the level of industrialspecialization; and information spillover in spatially concentrated regions can induce a positive externality on the firms'productivity.

An important point concerning the above-mentioned causes of agglomeration is that they are classed as positive externalities.In fact, the concept of agglomeration economies was first initiated by Marshall (1920) when he claimed that external economiescan be achieved by industrial regionalization. When industrial output is large enough to support the specialized production of manyfirms and enables firms to enjoy economies of scale, the average cost of production of a firm will decrease considerably. In otherwords, the advantage of industrial regionalization (agglomeration) lies in its promoting the division of specialized producersof intermediate goods in a specific region and then generating information spillovers. Consequently, many studies have providedexplanations as to the causes of agglomeration and have categorized the agglomeration effects into localization economiesand urbanization economies.5 The localization economies indicate that the clustering offirms within an industry can improveefficiency while the urbanization economies imply that an efficiency-boosting effect is brought about by the agglomeration of

various kinds of activity in a given region. Recently, scholars have often referred the agglomeration economies to, respectively,MarshallArrowRomer (MAR) externalities and Jacobs externalities (e.g., Beardsell & Henderson, 1999). In summarizing thepossible positive externalities and their effect in terms of fostering productivity, Fan and Scott (2003) have further broken downthe types of externalities intofive main issues: the clustering of proximatefirms can reduce the transaction costs incurred per unitof distance; dense local labor markets represent improved opportunities for job seekers and job vacancies; transactional relationsalso involve flows of certain kinds of business information or knowledge spillovers; the clustering of variousproducers is helpful infostering the formation of beneficial business alliances and organizations to augment local competitiveness; and the formation ofpublic goods can achieve significant economies of scale due to the consumption of infrastructures being spread over manyindividuals in any one place.6

Due to their benefiting from the aforementioned positive externalities, firms located within a relatively concentrated areaare thought to have higher productivity. However, it is also worth noting that agglomeration diseconomies may appear asregions grow in size due to diminishing returns. A high degree of urbanization may result in disadvantageous environments fordense firms, such as congestion, heightened competition, higher costs of land and other factor inputs, and intense competition

in output markets. These increased costs of trade brought about byagglomeration diseconomies

can negatively impactfi

rm-level productivity, thereby inducing lower productivity for firms located in spatially concentrated regions. Ultimately, whetherthe advantages of agglomeration economies or the disadvantages of agglomeration diseconomies dominate is an empiricalissue.

2.2. Literature review

There have been plenty of empirical studies that have analyzed the influence of spatial agglomeration on industrial or regionalproductivity and most conclude that a positive relationship exists.7 However, the estimation of agglomeration effects usingaggregated measures offirm performance is attributed to the limited availability of firm-level data required to obtain directestimates of productivity (Rosenthal & Strange, 2004). Economic analyses directly relating agglomeration to firm-level pro-ductivity have, however, emerged recently.

One line of research focuses on examining the potential impacts of agglomeration externalities on firms' labor productivity.Ciccone and Hall (1996) first related agglomeration to labor productivity and found that doubling employment density in a U.S.county increases average labor productivity by 6%. Similarly, Henderson (2003) used firm-level panel data for the machinery andhigh-tech industries to estimate production functions, aiming to examine the influences of various externalities brought about byagglomeration on firm production. He found that a ten-fold increase in the number of local plants in a high-tech industry,representing several local information spillover sources, increased labor productivity by over 20%. Moreover, he found littleevidence of economies of scale based on the diversity or scale of local economic activity outside the own industry. The NewZealand case conducted by Mar and Timmins (2006) confirmed that labor productivity is higher for firms in geographically-concentrated industries (localization), for firms in more industrially-diversified labor markets (urbanization), and for firms

4 See Fujita and Thisse (2002) for a comprehensive up-to-date discussion.5 For example, Hill and Brennan (2000), Rosenthal and Strange (2001), Iammarino and McCann (2006), and Ellison et al. (2009) .6 Some recent studies have emphasized the importance of demand-side externalities, such as the network of strongly interdependent firms, knowledge

producing agents and (demanding) customers, linked to one another in a value-adding production chain. See Drejer et al. (1999), Fingleton et al. (2005), and

McCann and Folta (2008).7 There are a number of excellent up-to-date surveys of the empirical literature, such as Graham (2006) and Broersma and van Dijk (2008).

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operating in larger labor markets. By controlling for heterogeneity among industries, locations and firms, they found somesupport for a positive productivity effect of changes in both localization and urbanization, although not all estimated effectswere statistically significant. Lee, Jang, and Hong (2010) revisited agglomeration economies by estimating the effects oflocalization, urbanization, and local competition on labor productivity using establishment-level data in Korean manufacturingindustries. By controlling for issues such as self-selection bias, omitted variable bias and the fixed effects of establishments/locations and industries, their estimates showed that, when an establishment locates in a more localized/specialized, moreurbanized/diversified, and more competitive area, its workers become more productive, due to the external benefits fromagglomeration.

Some studies examine the agglomeration-firm productivity nexus by starting from different perspectives. Lall et al. (2004)examined the extent to which agglomeration economies contribute to economic productivity in India by employing firm leveldata for manufacturing and using an input demand framework in the empirical analysis which allows agglomeration economiesto be factor augmenting. The empirical results indicate that access to markets through improvements in inter-regionalinfrastructure is an important determinant offirm level productivity, whereas the benefits from locating in dense urban areas(measured by the index of the location quotient (LQ)) do not appear to offset associated costs. Yamamura and Shin (2007) usedcensus data from Japanese assembly industries during the period 19602000 to investigate how innovation and imitation lead tothe exploitation of long-term subcontract networks and agglomeration economies, thus having an effect on an improvementin productivity. By adopting the data envelopment analysis (DEA) approach to decompose productivity into innovation andimitation, their empirical findings indicated that the level of efficiency readily improves in areas where the division of labor isadvanced by relatively small establishments, providing evidence of a positive impact of the agglomeration externality onproductivity. Similarly, Cainlli (2008) analyzed the impact of innovative activities and agglomeration effects connected with afirm's membership in a Marshallian industrial district, as well as the possible effects arising from the interaction between thesetwo factors, on firm productivity in Italy. Based on the estimate of an augmented CobbDouglas production function to accountfor the impact of innovative activity and agglomeration effects on firm productivity, the empirical results showed that the firms'membership in industrial districts, product innovation, and the interaction effects between these two factors are key factors inexplaining the firms' productivity.

While the above-mentioned studies have lent support to the importance of agglomeration to firm-level productivity, studiesrelating spatial agglomeration to productivity in China remain few, especially for firm-level studies.

Batises (2002) investigated the relationship between the local economic structure (local sectoral specialization, diversity,and competition) and the value-added growth of Chinese provinces during 19881994. The econometric analysis showed thatwhile diversity and competition have a positive influence on local growth, specialization has a negative impact. In addition tothe impact that competition has on local growth, Tian, Wang, and Chen (2010) used Solow growth theory to study 313 Chineseprefectures over the period 19912007, and found there to be a competition effect in terms of the growth of capitalaccumulation and urbanization among neighboring regions. That is, the urbanization growth rates for the prefectures were

found to have a positive impact on growth, but the competitive effect was found to have a negative infl

uence on physicalcapital accumulation in the neighboring areas. For this reason, the eastern coastal, central, and western regions exhibiteddifferent spatial patterns. The eastern coastal region was characterized by a substantive form of spatial diffusion, while thecentral and western regions were characterized by the nuisance form of spatial dependence. Kuo and Yang (2008) utilizedpanel data for 31 provinces over the period from 1996 to 2004, and found that R&D capital and technology imports had positiveimpacts on regional economic growth. Therefore, they recommended that the government encourage firms to engage in R&Dspending or import advanced technologies through industrial policies. Chen (2009) used data for 30 provinces covering theperiod from 1993 to 2006 to calculate different measures of agglomeration, and discovered that urbanization and foreign-specific agglomeration had a significant and positive impact on FDI location and industrial FDI. His results suggest that thereexist cross-regional agglomeration effects, and that local provinces need to coordinate their agglomeration-related activitieswith their neighboring provinces. Based on 30 manufacturing sectors in 2000, Fan and Scott (2003) first calculated the H-indexfor each industry in Chinese provinces. They also showed that many kinds of manufacturing sectors are characterized by astrong positive relationship between spatial agglomeration and productivity. Au and Henderson (2006a, 2006b) modeled and

estimated net urban agglomeration economies for cities in China. Theoretical models of cities postulated an inverted U-shapeof real income per worker in relation to city employment. Econometric estimates found that urban agglomeration bene fits inthe form of high real incomes per worker rise sharply with increases in city size from a low level, thus supporting thetheoretical prediction. The authors also found that a large proportion of cities in China are undersized due to strong, nationally-imposed migration restrictions, resulting in large income losses. Based on panel data for China's provincial electronicinformation manufacturing industry since 2002, Lei and Zhao (2009) discussed the relative importance of Marshallian and

Jacobs externalities on productivity. Their empirical results indicated that, while there are strong scale economies and Jacobsexternalities, there are, however, Marshallian externalities and related industries' convergence externalities which should bestrong in high technology industries. That is to say, there are no economic advantages in terms of the agglomeration of China'selectronic information manufacturing industry on a provincial level.

Recently, studies analyzing the effect of spatial agglomeration on Chinesefirms' productivity have emerged. Lu and Tao (2009)investigated trends and determinants of the geographic concentration of manufacturing industries using large firm-level data forthe period from 1998 to 2005. They found that the extent of industrial agglomeration, measured by the EG index, has increased

steadily throughout the sample period. Moreover, they found that industrial agglomeration is lower in industries with highershares of state-owned enterprises (SOEs) in employment, suggesting a role of local protectionism among China's various regions



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in obstructing the process of geographic concentration in manufacturing industries. By studying a cashmere sweater cluster inChina, Ruan and Zhang (2009) highlighted one key advantage which is that clusters bring about a finer division of labor. This inturn lowers the capital entry barriers, attracting more entrepreneurs with limited capital who would otherwise have difficultyentering the business. Moreover, Fleisher, Hu, McGuire, and Zhang (2010) examined a cluster of children's garment firms in Zhilitownship in Hangchou province in 2000 and 2008 to investigate the evolution of industrial clusters, and found that specializationand outsourcing had resulted in a large increase in the number offirms in the township, so that TFP had tended to rise over time.However, because of the low entry barriers, rising wage costs and the stricter imposition of safety regulations, the firms saw theirprofits decline.

3. Developments in industrial agglomeration in China's textile industry

There are three models of industrial agglomeration which have been formed due to the policy restrictions that haveaccompanied the economic development in China (Chen & Tang, 2003). The first is the planning-based aggregation model whichgathers together mainly large and medium-sized state-owned enterprises of traditional industries as the core members to forma traditional industry cluster. The second is the government-supported aggregation model, which comprises industrial agglom-erations that are mainly high-tech industrial development zones planned and established by the government to attract high-tech firms through the provision of beneficial policy measures, e.g., the famous high-tech park Zhongguancun in Beijing. Thefinal kind of model comprises the market-oriented aggregation model which is naturally formed by private enterprises throughthe market mechanism and is mostly concentrated in the coastal regions, such as Wenzhou in Zhejiang. This model of spatialagglomeration is similar to the industrial agglomerations in the US, Europe, and other developed countries.

As for the development of industrial agglomeration within the textile industry, the government enacted an industrial policythat was raw material-oriented, market-oriented, and labor-oriented to direct the geographical distribution of the textile firmsin the 1950s. This strategy was implemented under the period of the centrally-planned economy, with a view to balancing theprovision of job vacancies across regions and achieving a balanced regional development, because the textile industry was themost critical industry in that decade. It therefore led to textile firms being dispersed across different regions in China.

Along with the open-door policy proposed in the early 1980s, the government implemented a series of export-expansionpolicies, with a preference for the coastal regions. In 1979, Special Economic Zones (SEZs) were established to attract FDI tospecifically designated export-oriented areas in Shenzhen and Guangzhou. This export-oriented economic policy relied heavilyon the exports of labor-intensive manufacturing. To further link China's economy to the world, the Open Port Cities policy waslaunched in 1984 which led to the establishment of Economic and Trade Development Zones (ETDZs) in 1985. As China enteredthe 1990s, a new policy of Free Trade Zones (FTZs) was proposed by removing tariffs on numerous exports and imports for

firms locating themselves in FTZs in 1992. To be specific, after China's accession to the WTO in 2001, Chinese textile firms facedtough competition in international markets, which forced them to improve their productivity to compete with foreign firms.

As a series of market-opening policies were implemented, the geographical layout of textilefi

rms gradually changed frombeing dispersed over wide areas to the formation of industrial agglomeration in the coastal regions. It is attributed to boththe movement of textile firms affected by the market mechanism and the establishment of foreign-owned textile firmsconcentrated in the coastal regions, because the textile industry is a labor-intensive as well as export-intensive industry. Forexample, Zhejiang province contains several industrial agglomerations of textile sub-industries, e.g., chemical fibers andchemical fiber fabrics in Shaoxing and Xiaoshan, the garment industry in Ningbo and Wenzhou, ladies' clothing in Hangzhou,the tie manufacturers in Shengzhou, the warp knitting industry in Haining, and the socks manufacturers in Zhuji. It is clearthat these industrial agglomerations have been formed through the market mechanism, belonging to the market-orientedaggregation model.

Figs. 1 and 2 depict the spatial distribution of textile firms in China in 2000 and 2005, respectively. A province with a biggercircle indicates that there are more textilefirms located in that province, as specified at the bottomof the figures. The geographicaldistributions of textile firms were quite similar in 2005 and 2000 in that they were mainly concentrated in the coastal provinces,with Zhejiang, Jiangsu, Guangdong, Shandong and Shanghai being ranked the top five in terms of the number offirms and having

more than 1000fi

rms each. To be specifi

c, there were 3217fi

rms (about 22%) located in Zhejiang province alone in 2005.

8

By contrast, there were relatively few firms, usually less than 15 firms, which chose to establish plants in Tibet, Qinghai, Hainan,Yunnan, Ningxia, and Guizhou. In terms of the inland regions, Hubei province possessed the most textile firms due to theabundance of cotton.

The numbers offirms at the provincial level are calculated and displayed at the bottom of Figs. 1 and 2. From Figs. 1 and 2, wecan observe the numbers offirms that are clustered in Shanghai, Jiangsu, Zhejiang, Shandong and Guangdong and elsewhere. Inaddition, the number offirms increased rapidly in the above regions from 2000 to 2005. The most remarkable increase in thenumber offirms occurred in Fujian province (the southeastern coastal province) where the number of firms grew from 515 firms(about 5.5%) in 2000 to 969 firms (about 6.5%) in 2005.

The abovefigures suggest that the textile industry appears to exhibit a geographical concentration toward the southeast coastalregions. In fact, these regions were the main textile manufacturing areas before 1950 when the new Chinese government formed

8

In fact, the statistical data encompasses only large and medium-sized enterprises (with sales in excess of RMB 5 million), implying that the actual number offirms is much larger than the statistics suggest.

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Legend

below 50

50 - 300

300 - 450

450 - 600

above 600

Fig. 2. The distribution of the Chinese textile industry in 2005.

Legend

below 50

50 - 300

3001 - 450

450 - 600

above 600

Fig. 1. The distribution of the Chinese textile industry in 2000.



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by the Communist Party implemented its centrally-planned economic policy with its emphasis on balanced regional de-velopment.9 While this policy led to a lack of regional specialization, the subsequent transformation from a centrally-plannedeconomy to a market-oriented economy caused the distribution of textile firms to change from one of geographical dispersionto one of geographical concentration due to considerations of transportation costs, exports, vertical disintegration and so on. Ithas provided the basis for the principal question with which we are concerned, which is whether spatial aggregation can increase

firm-level productivity in China or not.

4. Data and measurement of the spread of the textile industry

4.1. Data sources

Due to its long-standing importance in China's manufacturing sector which has undergone a transformation from acentrally-planned to a market-oriented economy, the textile industry provides an interesting as well as excellent case for us toexamine the relationship between industrial agglomeration and firm-level productivity. The dataset of textile firms used in thisstudy is based on the National Bureau of Statistics (NBS) Enterprise Data Set covering the period from 2000 to 2005. The annualsurvey conducted by the NBS of China contains almost all state enterprises and large and medium-sized non-state enterprises(with sales of above 5 million RMB). Information regarding financial statements and some non-financial information, suchas the entry date, district code, industry code, and main products of the enterprises are surveyed in the questionnaire. Thetextile industry selected in this study encompasses two two-digit manufacturing industries coded 17 textiles industry and 18

manufacturer of garments and other fibers, containing a total of nine three-digit industries. The nine three-digit industries

include: the cotton, chemicalfi

ber textile and printing/dyeingfi

nishing industry (171), wool textile and dyeingfi

nishingindustry (172), hemp textile industry (173), silk textile and finishing linen textile industry (174), textile product industry(175), knitwear/woven goods and their products manufacturing industry (176), textile and garment manufacturing industry(181), textile and fabric shoe manufacturing industry (182), and hat manufacturing industry (183). One point worth notingis that this survey is conducted based on a minimum sales threshold of RMB 5 million, implying that the surveyed firms mayvary from year to year, especially for medium-sized enterprises. After cleaning the dataset by deleting observations withunreasonable variables, which prevented us from calculating the productivity measure, such as negative or zero values forvariables including sales, capital, wages and so on, we finally obtained an unbalanced panel dataset of 22,152 firms, yielding atotal of 83,801 observations for the 20002005 period.

4.2. Measures of industrial agglomeration

How should industrial agglomeration be measured? Following Krugman's (1991) work, there has been a renewed interest

in location patterns of economic activity, with two broad measures of industrial agglomeration, namely, geographical proximityand geographical concentration, mostly being used. Classical measures such as the Gini index have been criticized for havingseveral drawbacks in the measurement of spatial inequality. Several recent contributions argue that only when the share ofan industry's employment in manufacturing employment varies significantly across regions, can we refer to the existence ofindustrial agglomeration within a region. To be specific, Ellison and Glaeser (1997) simultaneously considered the share of anindustry's employment within a region, the share of aggregate manufacturing employment within a region, and the marketconcentration of an industry in developing an agglomeration index. This is the well-known EG index in the economic geographyliterature and we adopt the index to measure the degree of agglomeration in this study. This index is based on a comparisonbetween the observed geographical distributions offirms of a random distribution which is defined as the expected distribution inthe absence of agglomeration. It is specified as follows

j =

kSjkXk

2 1

kX

2k

iZ

2ij

1k

X2k

1i

Z2ij =

Gj 1k

X2k

Hj

1k

X2k

1Hj 1

The term j represents the degree of thejth industry's agglomeration at the city level. The EG index, however, does not considerthe geographical proximity and distance of industrial agglomeration. The different distances impact the external economies,knowledge spillovers, and competition and cooperation effects that are produced by agglomeration differently. Therefore, even ifthe concentrations are the same, they have different agglomeration effects because of the differences in distances. An alternativeapproach is to use Busch and Reinhardt's (1999) method to calculate the degree of agglomeration which is measured based on thedistance between firms. However, we need the latitude and longitude offirms to determine their location, if we are to use thelatitude and longitude to calculate both the distance and the agglomeration index. Because we lack the data on the firms' latitudeand longitude, we are unable to calculate the index. If we could collect such data in the future, we would then be able to estimatethe agglomeration index. Nevertheless, the EG index is still one of the most popular and commonly used agglomeration indicators.

9 The People's Republic of China was established in 1949 to replace the Republic of China.

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In addition, the definition of a cluster at the city level is better than that at the provincial level, with the result that we calculatethe EG indexat the city level. In this way, we can reduce the impact of distance. The logic underlying the construction of this indexis as follows. The first part of the numerator is the traditional space Gini coefficient Gj =

kSjkXk 2

, representing a raw

geographical concentration. The term Sjk denotes the share of industryj's employment in the city k andXk is the share of aggregate

manufacturing employment in the city k. Inthe secondpart 1k

X2k

iZ2ij , the term

iZ2ij is the HerfindahlHirschman Index

of industry j (Hj) which is measured as the sum of squares offirm i's sales to industry j's share (Zij). Assuming that there are no

agglomeration economies and that each province is equally attractive, the raw geographical concentration Gj of industry j shouldbe proportional to its market concentration Hj. Ellison and Glaeser (1997) showed that

Gj = 1k

X2k

Hj + j 1Hj

h i2

From Eq. (2), we can obtain an estimate of the excess-concentration , referred to as the agglomeration index, as shown inEq. (1).

4.3. Results for the EllisonGlaeser index

Table 1 displays the results for the EG index for China's textile industries at the city level during the period 20002005. From

observing thefi

gures for the EG index in Table 1, several points are worth mentioning.First, from Table 1, the calculated city EG index of spatial concentration for each year exhibits an increasing trend in thetextile industries, having increased from 0.0007 in 2000 to 0.00014 in 2005. On the contrary, the EG index for garments and other

fiber products exhibits a decreasing trend as the findings of Lu and Tao (2009) indicate. Overall, the dynamics of industrialagglomeration for both textile and garments appear to have experienced a slightly decreasing trend from relatively highconcentration in 2000 to relatively low concentration in 2005. While there are some studies measuring the EG index in variouscountries, it is hard to compare the degree of agglomeration directly due to the differences in the measured unit of industry. Forexample, Mar (2005) finds that the estimated mean EG ranges from a small negative value (0.006) to a large positive value(0.179) for the two-digit industry classification in New Zealand in 2002. The study on Belgium conducted by Bertinelli and Decrop(2005) shows that the value of the EG index ranged from 0.045 to 0.69 for four-digit industries in 2000. Leahy, Palangkarayaa,and Yong (2010) estimated the geographic agglomeration of establishments in the Australian manufacturing industries andfound that the EG index ranged from 0.029 to 0.148 for four-digit industries in 1997.

Second, the EG index varies significantly among sub-industries. For instance, in 2000, the industry with the highest degree of

industrial agglomeration was the textile and fabric shoe manufacturing industry (182), which reached a value of 0.00415. Bycontrast, the textile product industry experienced the lowest degree of agglomeration of0.00008.An interesting issue worth examining is the change in ranking. The textile and fabric shoe manufacturing industry (182) was

often ranked among the top two in each year, whereas the textile product industry (175) tended to be ranked the lowest in everyyear, suggesting that the geographical distribution of textile product firms is much dispersed. The reasons why there are differentgeographic concentrations in the silk textile and finishing linen textile industry (174) and the textile product industry (175) arethat there are more firms in the industry, the sizes of the firms are more or less the same and there are not many large firms.For these reasons, the degrees of agglomeration for SIC 174 and 175 are lower than those in other industries. More interestingly,in the garments and other fiber products, the textile and garment manufacturing industry (181) and the textile and fabric shoemanufacturing industry (182) the corresponding EG indices decreased sharply. On the other hand, the hat manufacturing

Table 1

The calculated EG Index of the 3-digit textile industries at city level, 2000

2005.

SIC Industry EG, 2000 EG, 2001 EG, 2002 EG, 2003 EG, 2004 EG, 2005

171 Cotton, chemical fiber textile and printing/dyeing fini shing i ndus try 0.00 006 0.00 00 9 0 .0001 0 0.00 011 0 .0000 6 0 .0001 1

172 Wool textile and dyeing finishing industry 0.00005 0.00021 0.00038 0.00020 0.00022 0.00023

173 Hemp textile industry 0.00074 0.00047 0.00089 0.00130 0.00134 0.00159

174 Silk textile and finishing linen textile industry 0.00020 0.00033 0.00033 0.00033 0.00019 0.00022

175 Textile product industry 0.00008 0.00007 0.00009 0.00007 0.00010 0.00002

176 Knitwear/woven goods and their products manufacturing industry 0.00010 0.00007 0.00009 0.00009 0.00015 0.00012

Weighted means of Textile Industry 0.00007 0.00011 0.00013 0.00013 0.00013 0.00014

181 Textile and garment manufacturing industry 0.00024 0.00017 0.00015 0.00015 0 .00017 0.00014

182 Textile and fabric shoe manufacturing Industry 0.00415 0.00210 0.00193 0.00187 0.00285 0.00202

183 Hat manufacturing industry 0.00047 0.00116 0.00152 0.00120 0.00151 0.00147

Weighted means of garments and other fiber products 0.00134 0.00032 0.00029 0.00027 0.00030 0.00024

Weighted means 0.00022 0.00019 0.00020 0.00018 0.00019 0.00017

Note: The city level refers to municipal districts, prefecture cities and county level cities such as the Beijing municipal districts, Wenzhou, Wuhan, Chengdu andZhuji.



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industry (183) experienced an increase in industrial agglomeration so that the EG index increased from 0.00047 in 2000 to0.00147 in 2005.

To sum up, the distribution of industrial agglomeration within the textile industry appears to have exhibited a twin-peakeddistribution in that few industries have an EG index that reflects a moderate degree of agglomeration, while more industriesexhibit either high or low industrial agglomeration. Moreover, along with the accession to the WTO, the degree of industrialagglomeration within the textile industry has tended to decrease. After China's accession to the WTO, the exports of the textileindustry increased because the quotas were abolished. While many textile firms were set up, they were not necessarily located inplaces where textile firms were clustered together, thus giving rise to the decline in the agglomeration index. In addition, it may bebecause the Chinese government adopted the Western Development 10 in 2000, and many firms set up factories in the West,with the result thatfirms became more decentralized and the agglomeration index was affected. Furthermore, the Rise of CentralChina11 policy was adopted in 2004. This policy encouraged firms to set themselves up in the middle of China, so that theagglomeration level also decreased in 2004.

5. The link between agglomeration and productivity

5.1. Measure of productivity

Has industrial agglomeration led to higher firm-level productivity in China's textile industry? To answer this question, we firstneed to provide an appropriate measure of productivity. The concept of productivity is defined as the relationship between output(produced goods) and inputs (consumed resources, such as labor, capital, and energy), and generally refers to the unobserved

firm-specific effect that can be recovered from an estimated production function as the difference between actual and predictedvalue added. It is perhaps one of the most important variables governing economic production activities and affecting amanufacturing firm's competitiveness (Singh, Motwani, & Kumar, 2000). There are two widely-adopted measures of productivityin the literature, including labor productivity and total factor productivity (TFP). Labor productivity is a partial measure of TFP thatfocuses on evaluating the productivity of labor inputs, whereas TFP considers the production efficiency of utilizing all inputs. Thisstudy adopts labor productivity and describes its measurements as follows.

To reduce the influence offluctuations in output prices on the estimation of productivity, the study adopts the price-adjustedmeasure of labor productivity developed in Aw and Lee (2008) as follows:

ln LPijt = ln VAijtln VAjt

ln Lijtln Ljt

3

VA refers to the value added which is measured by price-adjusted sales minus intermediate inputs. L is employment. Thesubscripts i, j, and t denote firm, 3-digit industry, and time, respectively. ln VAjt and ln Ljt are respectively the average of valueadded and employment in the jth textile industry. This labor productivity index denotes the relative productivity of each firmwithin an industry. This index is perhaps suitable for measuring firm-level productivity in this study, because the quality of theChinese micro data is sometimes criticized for not being sufficiently reliable.

We describe the relationship between the EG index and productivity in Table 2a. From Table 2a, we can see that therelationship between labor productivity and industrial agglomeration for the textile industry as a whole from 2000 to 2005exhibits no clear positive relationship. However, for the textile industry, as the degree of agglomeration increases, the trend oflabor productivity also increases. Moreover, in the case of the garment industry, the relationship between labor productivity andindustrial agglomeration is not too obvious. This is because there are many factors that affect a firm's labor productivity. If we canfurther control other factors such as firm size and the types of enterprises that affect labor productivity, we can truly verify therelationship between agglomeration and labor productivity.

Table 2b displays the classification of the EG index (High EG, Middle EG and Low EG), the type of enterprise, firm size and labor

productivity in the textile and garment industry. From Table 2b, we find several points of interest.First of all, in our sample, a higher EG seems to have higher labor productivity. Secondly, foreign-owned enterprises account for

about 35%, state-owned enterprises (SOEs) for about 4%, and private enterprises for 48% of all enterprises. In addition, for foreign-owned and private enterprises, the labor productivity is positive. SOEs are found to have negative values of labor productivity. Insum, the labor productivity of foreign-owned enterprises is higher than that of other types of enterprises, and state-ownedenterprises have lower labor productivity values. This phenomenon is consistent with thefindings of some scholars (Jefferson, Hu,Guan, & Yu, 2003). Furthermore, since many large firms are SOEs, small firms are more likely to have higher labor productivitythan large firms.

10 China Western Development is a policy adopted by the State Council of China to boost its less developed western regions in January 2000. The policy covers 6

provinces (Gansu, Guizhou, Qinghai, Shaanxi, Sichuan, and Yunnan), 5 autonomous regions (Guangxi, Inner Mongolia, Ningxia, Tibet, and Xinjiang), and 1

municipality (Chongqing).11

The Rise of Central China is a policy of the central government. It was advanced by the Chinese Premier Wen in March, 2004. The Chinese government wantsto raise economic zone in central China which are Henan, Hubei, Hunan, Jiangxi, Anhui and Shanxi provinces.

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5.2. Empirical specification

To examine whether industrial agglomeration contributes to firm-level productivity, we specify the empirical model as shown

below:

Yijkt = 0 + 1EGjt + 2EG2jt + 3EGjt lnLijkt + 4lnLCITYjkt + 5AGEijt + 6SIZEijt + 7lnKLijt + 8FCijt + 9SOEijt

+ 10PRIVATEijt + 11WBijt 12lnPGDPkt + 13lnRDkt + 14lnFDIkt + DINDj + DYEARt + ui + ijkt4

The dependent variable is either the relative labor productivity (lnLP) calculated in the previous subsection, whereas theindustrial agglomeration index and other controlling factors are the independent variables. The subscripts i, j, k, and tdenote firm,industry, province, and time, respectively.

Among the explanatory variables, EG is the EllisonGlaeser indexof industrial agglomeration at the city level which is the mainvariable of concern in this study. If the externalities of industrial agglomeration do contribute to enhancing productivity asdiscussed in the existing literature, the EG variable will be associated with a significantly positive coefficient. The squared term of

the EG index is included in the equation to test the nonlinear relationship between industrial agglomeration and productivity.Since agglomeration diseconomies may arise if the industrial agglomeration is too high, we therefore expect a negative sign forthe squared term of the EG index. Moreover, if there is a positive contribution brought about by agglomeration to firm-levelproductivity, we would like to examine whetherthis impact varies between large and small firms. To explore this interesting issue,the interaction term between the EG index and firm size (in terms of employment) is also included.

In order to consider variation in agglomeration offirms in a sub-industry across different location in China, we use lnLCITY,the logarithm of the total employment offirms in a sub-industry located in the city, to measure agglomeration in city. With thisvariable we are able to measure time and geographic variations in city agglomeration.

Two-dimensional factors to differentiate productivity are also considered, namely, firm-specific and province-specific char-acteristics. The firm-specific characteristics include firm age (AGE), large firm (SIZE), the capital-to-labor ratio (lnKL), foreigncapital (FC), state-owned enterprises (SOE), private enterprises (PRIVATE) and the worker benefits-to-wage ratio (WB). Kortumand Lerner (1998) proposed the regulatory-capture hypothesis, claiming that incumbent firms have more management andadministrative advantages than the new entrants and can adapt to the changing environment. Therefore, the impact of firm age

(AGE) on productivity should be positive. However, some scholars, e.g., Liu, Tsou, and Hammitt (1999) and Bernard and Jensen(1999, 2004) found a negative impact offirm age on productivity in Taiwan and Germany. Therefore, there is no expected sign

Table 2

Agglomeration and labor productivity in the textile industry, 20002005.

a

Year All Textile industry Garments and other fiber products

Firm

number

Weighted

EG mean

lnLP

mean

Firm

number

Weighted

EG mean

lnLP

mean

Firm

number

Weighted

EG mean

lnLP

mean

2000 8695 0.00022 0.027 4894 0.00007 0.048 3801 0.00134 0.001

2001 12,367 0.00019 0.018 6920 0.00011 0.031 5447 0.00032 0.002

2002 14,892 0.0002 0.162 8411 0.00013 0.029 6481 0.00029 0.411

2003 17,504 0.00018 0.019 10,135 0.00013 0.029 7369 0.00027 0.006

2004 15,476 0.00019 0.011 9810 0.00013 0.018 5666 0.0003 0.001

2005 14,867 0.00017 0.007 9386 0.00014 0.009 5481 0.00024 0.004

b

Firm number

(ratio%)

lnLP

Mean S.D. Max Min

Level of EG EGH 27,973(34) 0.1353 0.9219 5.4892 6.2879

EGM 27,917(33) 0.0430 1.0637 5.6348 9.6573

EGL 27,911(33) 0.0432 1.0654 5.1865 7.6450

Type of enterprise Foreign-owned 29,065(35) 0.1935 0.9657 5.1865 7.6450

State-owned 3162(4) 0.8941 1.4226 4.1616 9.6573

Private 40,526(48) 0.0006 0.9591 5.4892 9.1079other 11,048(13) 0.1327 1.0876 5.6348 5.8190

Firm size Large 42,665(51) 0.1981 0.9819 5.6348 9.6573

Small 41,136(49) 0.2383 1.0167 5.4892 7.6450

All 83,801 0.0161 1.0226 5.6348 9.6573

Note: Based on actual capital received, foreign-owned enterprises include foreign joint ventures, foreign cooperatives, foreign wholly-owned enterprises and

foreign companies with limited shareholdings. State-owned enterprises include state-owned enterprises, state-owned jointly operated enterprises and wholly

state-owned companies. Private enterprises include private wholly-owned enterprises, private-cooperative enterprises, private limited liability companies and

private shareholding companies.



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for the firm age variable a priori. The large firm (SIZE) is a size dummy variable. If a firm's employment is larger than medianemployment, then the value equals one. Biesebroeck (2005) used a sample of manufacturingfirms from nine sub-Saharan Africancountries, and large firms are found to achieve higher productivity levels and to be more likely to survive. However, Cabral (1995)proposed a theory to explain the empirically observed negative relationship between firm size and firm growth, although wewould not expect such a sign a priori. The term KL is the capital intensity which is measured by the ratio of capital to labor and isexpected to have a positive impact on productivity. FCis an ownership dummy variable which equals one if a firm has a positivevalue of foreign capital. The issue of ownership in differentiating productivity in China has been widely examined in the existingliterature, e.g., Jefferson, Rawski, Wang, and Zheng (2000) and Li, Hu, and Chi (2007). Most studies have obtained consistentresults whereby foreign-owned enterprises (FOEs) tend to have higher productivity relative to domestic firms, because FOEsusually possess more advanced technologies and management knowledge. To further examine the question of whether there isa difference in the productivity effect between various sources of foreign capital, we further divide foreign capital into foreigncapital from Hong Kong, Macau and Taiwan (HMTFC) and other foreign capital (OFC). The logic of this division arises from thefact that more than half of the FDI inflow into China comes from Hong Kong, Macau and Taiwan. In addition, we also include

SOE and PRIVATE variables to see the effects of different types of enterprises. Jefferson et al. (2003) demonstrate that SOEswith higher concentrations of state assets perform at the lower end, whereas these with lower concentrations of state assetsperform at the higher end. As for the PRIVATE variable, Chen and Feng (2000) proved that private and semi-private enterpriseslead to an increase in economic growth in China. From that, we expect the sign of SOEto be negative and that ofPRIVATE to bepositive. Finally, the worker benefit variable (WB) is measured by the ratioof expenditures on insurance and pensions to the wage.From the perspective of the employees, this expenditure is one type of working compensation in China that induces higherproductivity in China's electronics industry (Yang, Lin, & Ma, 2010).12 Thus, a better worker benefit can be accompanied by higher

labor productivity, because the benefi

t is helpful to consolidating the loyalty of employees and can then induce higher laborproductivity.As for the province-specific variables, PGDPktis the percapita GDP in province k in year tthat is used to capture the income level

in the local market. Moreover, the logarithm of a province's R&D expenditure ( RD) and the amount of foreign direct investment(FDI) are also considered in this study. We use them to capture a province's technological capability and the possible technologicalspillovers of advanced technological and management knowledge from capital inflows. In addition to firm and provincialcharacteristics, we include two sets of dummies, that is, eight three-digit industry dummies and five year dummies. The terms uiand ijkt denote the unobserved firm-specific effect and pure white noise, respectively.

To obtain robust estimates of the potential impact of industrial agglomeration on firm-level productivity, this study appliesanother strategy to include the EG index. Ellison and Glaeser (1997) suggested that the degree of geographical concentration canbe classified into highly concentrated (N0.05), relatively concentrated (0.020.05), and not very concentrated (b0.02).However, the city EG indices are relatively small. We therefore classified EG into highly concentrated (N0.0007), relativelyconcentrated (0.0000367bb0.0007), and not very concentrated (b0.0000367).13 Using the above classification, we replaced

the EG variable in Eq. (4) by two dummies for the EG indices in each province:EGH

andEGM

. They represent highly concentratedand relatively concentrated degrees of geographical concentration, respectively. Eq. (4) can be re-specified as:

Yijkt = 0 + 1EGHjt + 2EGMjt + 3EGHjt ln Lijkt + 4EGMjt ln Lijkt + 5ln LCITYjkt + 6AGEijt + 7SIZEijt

+ 8ln KLijt + 9FCijt + 10SOEijt + 11PRIVATEijt + 12WBijt + 13ln PGDPkt + 14ln RDkt + 15ln FDIkt

+ DINDj + DYEARt + ui + ijkt

5

To deal with the unobservable firm-specific effect ui in a panel data model, we used a within panel estimator, the fixed effect(FE) or the random effect (RE) technique, to eliminate the firm characteristic as a standard estimation method. The fixed effectsmodel can eliminate the biases in the estimated coefficients due to time-persistent un-observation including important deter-minants offirm productivity such as the entrepreneurial skill of owners, and can cater for the self-selection of firms that areclustered together. By contrast, the random effects model controls the unobserved firm characteristics and follows a normal

distribution. However, these firm-specific components in the error term may be plausibly correlated with the firm's productivecharacteristics, implying that the RE estimators are inconsistent when the assumption of zero correlation between the error termand right-hand side regressions is violated. We use both the random effects and fixed effects models along with the Hausman testto judge which model is more accurate. The definitions and basic statistics of the variables are summarized in Table 3.

5.3. Empirical results and discussion

A series of estimates obtained from regressing Eq. (4) are shown in Table 4. Columns 2 and 3 are estimates obtained byregressing on all variables, whereas columns 4 and 5 are estimates obtained by regressing on all variables except EG^2. Theestimates do not include SIZEin columns 6 and 7,and in columns 8 and 9,we separateHong Kong, Macau and Taiwan(HMTFC) andother foreign capital (OFC) from foreign capital.

12 Before the Labor Contract Law was enacted in 2008, it was not compulsory for Chinese firms to pay the insurance and pensions on behalf of their

employees. Therefore, the expenditures on employee insurance and on pensions by employers can be treated as a part of job compensation.13 When we arrange the city EG index in order and divide it into three portions, the cutting points are 0.0000367 and 0.0007.

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We first focus on the main question with which this study is concerned: does a concentrated industrial agglomeration reallycontribute to enhancing firm-level productivity due to the positive agglomeration externalities? It is obvious that the coefficientfor the EG index is significantly positive at the 1% statistical level, and that the EG square is significantly negative at the 10%statistical level.14 The EG coefficient is 134.120 and the coefficient of the squared term of the EG index is660.480. That meansthat when the EG index increasesby 0.0001, the growth rate of labor productivity will increase by 1.33% (the mean of the EG indexis 0.0005). If the EG index changes by a unit standard deviation, the growth rate of labor productivity will increase by 20.02%

(the standard deviation of the EG index is 0.0015). In addition, if we look at EG's quadratic model, from the calculations we willsee that when the EG index is over 0.1015, there will be agglomeration diseconomies and labor productivity will decline.However, because the maximum value of our EG index is 0.0126, there will be over agglomeration and so it not very likely thatagglomeration diseconomies will occur. This result suggests that industrial agglomeration does have a positive impact onfirm-levellabor productivity in China's textile industry, while this productivity-enhancing effect decreases as the degree of industrialagglomeration increases. That is, firms belonging to sub-industry with higher EG index may enjoy the advantages brought aboutby positive agglomeration externalities, such as improved opportunities for labor markets and pooling, the reduced transactioncosts of intermediate goods, as well as technological and knowledge spillovers and so on. However, such close proximity mayresult in agglomeration diseconomies if the spatial concentration is too high. This finding supports the theoretical predictionsregarding the agglomeration-productivity nexus, validating the application of agglomeration economies in a developing andtransitional economy. It is also consistent with those findings using firm-level panel datasets in developed countries, e.g., the US(Ciccone & Hall, 1996; Henderson, 2003) and New Zealand (Mar & Timmins, 2006). More importantly, the argument regardingthe existence of agglomeration economies in China is not only supported by provincial-level data (Fan & Scott, 2003) and city-level

data (Au & Henderson, 2006a, 2006b), but is also supported byfi

rm-level cases in this study. Combes, Duranton, Gobillon, Puga,and Roux (2009) used French firm-level data and arrived at similar conclusions to those in our study. However, their paper wasbased on the use of a city's size for measuring agglomeration. They believed that if a worker is able to interact to a signi ficantdegree with other workers, his productivity will be higher. Therefore, workers in a large city will tend to have higher productivityand a higher level of agglomeration, compared to workers in a small city. The authors measure the level of agglomeration byranking the cities according to size, but their use of such a calculation method does not appear to be sufficiently precise.

Interestingly, the interaction term between the EG index and firm size is associated with a significantly negative coefficient,implying that as the degree of industrial agglomeration remains constant, smallfirms benefit from a larger productivity-enhancingeffect from the agglomeration externalities than their larger counterparts. Small firms usually bear a higher unit cost of searchingfor qualified workers and intermediate inputs transactions. The advantages derived through the spatial benefits of economicactivities can largely reduce the unit cost of transactions involving labor and specialized intermediate input markets for smallfirms. Therefore, spatial concentration contributes more to small firms than large firms in terms of enhancing their productivity.

14 We have tried to run separate regression for each sub-industry. The empirical results indicate that the signs of the EG and EG squared terms are not affected.

Table 3

Variable definitions and basic statistics.

Variable

names

Definition Mean (S.D.) Maximum (Minimum)

LP Labor productivity: measured by Eq. (3) 0. 0168 (1 .0243 ) 6.4 374 (9.6573)

EG EG index: calculated according to Eq. (1) at city level. 0.0005 (0.0015) 0.0126 (0.0093)

EG^2 The square of the EG index at city level. 0.000002 (0.00001) 0.0002 (0.000000)

EGH High EG dummy: equals 1 if the EG index at city is larger than 0.0007. 0.3338 (0.4716) 1 (0)

EGM Middle EG dummy: equals 1 if the EG index at city is between 0.0000367 and 0.0007 0.3331 (0.4713) 1 (0)

EG*lnL The interaction term between the EG index and employment. 0.0027 (0.0079) 0.1024 (0.0854)

EGH*l nL The interaction term bet ween the High EG du mmy and emp loyment. 1 .7751 (2 .5701 ) 10 .2885 (0)

EGM*l nL The interaction term bet ween the Midd le EG du mmy and emp loyment. 1 .8013 (2 .6223 ) 10 .4980 (0)

LCITY The city employment by fir ms in a sub -ind ustr y (thousa nd peop le) 153.1 734 (1 25.00 65) 609.1 930 (0. 0270)

AGE Firm age: measured by the sample year minus the established year. 9.5408 (10.2210) 146 (0)

SIZE Size dummy: equals 1 if the firm's employment larger than median employment. 0.505 (0.500) 1 (0)

K/L Capital intensity: measured by the ratio offixed capital to employment.

(RMB ten thousand/person)

45.0949 (69.7938) 811.6129 (0)

FC Foreign ownership dummy: equals 1 if the firm has positive foreign capital. 0.3468 (0.4760) 1 (0)

OFC Foreign ownership dummy: equals 1 if the firm is a foreign-owned enterprise but not

a Hong Kong, Macau, or Taiwan-owned enterprise.

0.1511 (0.3581) 1 (0)

HMTFC Foreign ownership dummy: equals 1 if afirm is a Hong Kong, Macau, or Taiwan-owned enterprise. 0.1997 (0.3998) 1 (0)

SOE SOEs ownership dummy: equals 1 if the firm has positive SOEs' capital. 0.0378 (0.1906) 1 (0)

PRIVATE Private ownership dummy: equals 1 if the firm has positive private capital. 0.4836 (0.4997) 1 (0)

WB Worker benefit variable: measured by the ratio of expenditures on insurance and

pensions to the wage.

2.2629 (12.6519) 288.75 (3.2103)

PGDP Per capita GDP of a province 18.5502 (8.9949) 53.2070 (2.7958)

RD R&D expenditure of a province (RMB billion) 11.6551 (7.5658) 39.2084 (0.0200)

FDI Foreign direct investment of a province (RMB billion) 939.0373 (731.9609) 2506.8254 (2.7624)

Note: The means and standard errors are calculated by pooling data for the 20002005 period.

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Furthermore, we found the lnLCITYhas a statistically significant positive impact on the labor productivity. It denotes that the firmslocated in cities with higher agglomeration again enjoy the advantage brought by positive agglomeration externalities and theirlabor productivity will increase.

As for the influences offirm characteristics, firm age is negatively associated with productivity, suggesting that younger firmshave higher productivity on average. The possible explanation is that younger firms usually adopt more advanced technologies,enabling them to perform better in productivity terms. The largefirm (SIZE) has a negative effect on labor productivity, and meansthat small firms are more productive in the Chinese textile industry. This result is consistent with Cabral's (1995) research and ourstatistics in Table 2b. The capital intensity has a significantly positive impact on labor productivity. A higher capital-to-labor ratiowill lead to higher labor productivity because the increase in per capita capital can enhance production efficiency to promoteproductivity. The share of worker benefit expenditure in salary (WB) is found to be associated with a significantly positivecoefficient after controlling for other variables, suggesting that firms with a higher intensity of employee benefits are associatedwith higher relative labor productivity. This result is consistent with the findings in the case of Chinese electronics firms obtainedby Yang et al. (2010).

Moreover, we find that ownership matters to firm-level productivity, as suggested in previous studies. The coefficient of theforeign capital (FC) dummy is positive and significant at the 1% statistical level, indicating that FOEs have higher labor productivitythan their domestic counterparts. We further examine the potential differences in the productivity-enhancing effects of variousforeign ownerships. The estimates in Tables4 and 5 show that the estimated coefficient ofHMTFCis slightly lower than that ofOFC.This implies that textile firms with capital inflows from Hong Kong, Macau and Taiwan do not experience higherproductivity thanother FOEs. In fact, Motohashi and Yuan (2009) also found that Hong Kong-, Macau- and Taiwan-owned manufacturing firmsexperienced slightly lower productivity than other foreign-owned enterprises during the 19952003 period. In addition, SOEexhibits a negative coefficient at the 1% statistical level and the coefficient of private enterprises is positive. This result is consistent

Table 4

Estimates of industrial agglomeration at the city level and firm-level labor productivity.

Dep. variable lnLP lnLP lnLP lnLP

RE FE RE FE RE FE RE FE

Constant 1.319*** 1.692*** 1.324*** 1.690*** 1.617*** 1.763*** 1.324*** 1.690***

(0.077) (0.274) (0.076) (0.274) (0.078) (0.275) (0.077) (0.274)

EG 137.304*** 134.120*** 134.818*** 130.671*** 204.206*** 160.623*** 137.207*** 133.883***

(11.863) (13.755) (11.609) (13.590) (11.846) (13.785) (11.861) (13.754)

EG^2 348.448 660.480* 629.425* 836.181** 356.202 660.773*

(342.017) (407.357) (345.080) (409.388) (341.965) (407.317)

EG*lnL 28.101*** 27.513*** 27.787*** 26.978*** 40.482*** 32.582*** 28.007*** 27.464***

(2.155) (2.500) (2.133) (2.478) (2.151) (2.504) (2.154) (2.500)lnLCITY 0.063*** 0.004 0.063*** 0.004 0.062*** 0.0012 0.062*** 0.005

(0.006) (0.016) (0.006) (0.016) (0.006) (0.016) (0.006) (0.016)

AGE 0.008*** 0.002 0.008*** 0.002 0.010*** 0.002 0.008*** 0.002

(0.0005) (0.001) (0.0005) (0.001) (0.0005) (0.001) (0.0005) (0.001)

SIZE 0.303*** 0.251*** 0.303*** 0.251*** 0.304*** 0.251***

(0.008) (0.010) (0.008) (0.010) (0.008) (0.010)

lnKL 0.230*** 0.235*** 0.230 0.235*** 0.247*** 0.258*** 0.229*** 0.235***

(0.003) (0.004) (0.003) (0.004) (0.003) (0.004) (0.003) (0.004)

FC 0.154*** 0.078*** 0.154*** 0.078*** 0.119*** 0.068***

(0.013) (0.020) (0.013) (0.020) (0.013) (0.021)

OFC 0.182*** 0.104***

(0.014) (0.021)

HMTFC 0.133*** 0.064***

(0.014) (0.020)

SOE 0.468*** 0.090*** 0.468** 0.090*** 0.470*** 0.092*** 0.467*** 0.089***

(0.022) (0.031) (0.022) (0.031) (0.023) (0.031) (0.022) (0.031)PRIVATE 0.040*** 0.027** 0.039*** 0.027** 0.050*** 0.028** 0.041*** 0.029**

(0.011) (0.013) (0.011) (0.013) (0.011) (0.013) (0.011) (0.013)

WB 0.003*** 0.002*** 0.003*** 0.002*** 0.003*** 0.002*** 0.003*** 0.002***

(0.0002) (0.0002) (0.0002) (0.0002) (0.0002) (0.0002) (0.0002) (0.0002)

lnPGDP 0.076*** 0.557*** 0.076*** 0.553*** 0.110*** 0.555*** 0.072*** 0.556***

(0.019) (0.077) (0.020) (0.078) (0.019) (0.078) (0.019) (0.077)

lnRD 0.005 0.039 0.005 0.037 0.011 0.046 0.001 0.038

(0.013) (0.037) (0.013) (0.037) (0.014) (0.037) (0.013) (0.037)

lnFDI 0.060*** 0 .076*** 0 .060*** 0. 078*** 0. 071*** 0.0 81*** 0.0 65*** 0.076***

(0.011) (0.029) (0.011) (0.029) (0.011) (0.029) (0.011) (0.029)

Industry Year yes yes yes yes yes yes yes yes

yes yes yes yes yes yes yes yes

Adj. R2 0.195 0.146 0.195 0.146 0.172 0.116 0.196 0.146

Hausman test 45.16*** 620.28*** 73.50*** 45.98***

# of obs. 83,801 83,801 83,801 83,801 83,801 83,801 83,801 83,801

Note: Figures in parentheses are standard deviations. ***, **, *, and+ represent significance at the 1%, 5%, 10% and 15% statistical levels.



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with thefindings ofJefferson et al. (2003) in the case of Chinese manufacturing firms. Therefore, the sign of the private enterprisesis significantly positive. It means that private enterprises can improve their labor productivity, which will then lead to an increasein economic growth in China (Chen & Feng, 2000). To sum up, the most productive type of enterprise is the foreign-fundedenterprise, followed by the domestic private enterprise, and the productivity of state-owned enterprises is at its lowest in theChinese textile industry.

Based on the impact of thefi

rm-specifi

c components in thefi

xed-effects model, the coeffi

cients of the province-specifi

cvariables do not appear to be either significant or ideal. However, from the random-effects model, we can clearly see that thecoefficients of these variables are in line with our expectations. The estimated coefficients of provincial characteristic variables,including per capita GDP and foreign direct investment, are significantly positive in the random-effects model estimates. This

finding suggests that textile firms located in provinces with higher per capita GDP and FDI tend to have higher productivity, ceterisparibus.

The above analyses indicate that industrial agglomeration has a non-linear positive impact on firm-level labor productivity.To obtain more robust estimates and examine the differences in the productivity-enhancing effect among various degrees ofindustrial agglomeration, we replace the EG variable by two dummies of the EG indices: EGHand EGM, and then estimate Eq. (5).Table 5 displays the results of the estimation.

By comparing the estimates in Table 4, the results are quite similar and we therefore pay attention to discussing the resultsregarding the EG indices. It is apparent that both variables for highly concentrated (EGH) areas and relatively highly concentrated(EGM) areas are associated with a significantly positive coefficient, revealing that textile firms located in highly or relatively

spatially concentrated areas have higher productivity than their counterparts located in areas with less industrial agglomeration,ceteris paribus. As explained earlier, this result arises from the positive externalities brought about by industrial agglomeration.

Table 5

The degree of industrial agglomeration at the city level and firm-level labor productivity.

Dep. variable lnLP lnLP lnLP

RE FE RE FE RE FE

Constant 1.283*** 1.634*** 1.422*** 1.667*** 1.287*** 1.631***

(0.077) (0.273) (0.076) (0.273) (0.076) (0.273)

EGH 1.049*** 1.224*** 1.347*** 1.397*** 1.050*** 1.223***

(0.037) (0.045) (0.034) (0.044) (0.034) (0.045)EGM 0.830*** 0.878*** 1.100*** 1.033*** 0.832*** 0.878***

(0.034) (0.041) (0.032) (0.040) (0.034) (0.041)

EGH*lnL 0.196*** 0.227*** 0.252*** 0.260*** 0.195*** 0.227***

(0.007) (0.008) (0.006) (0.008) (0.007) (0.008)

EGM*lnL 0.160*** 0.169*** 0.211*** 0.198*** 0.160*** 0.169***

(0.006) (0.007) (0.006) (0.007) (0.006) (0.007)

lnLCITY 0.067*** 0.027* 0.068*** 0.022 0.066*** 0.028*

(0.005) (0.016) (0.006) (0.016) (0.005) (0.016)

AGE 0.007*** 0.002 0.008*** 0.002 0.007*** 0.002

(0.0005) (0.001) (0.0005) (0.001) (0.0005) (0.001)

SIZE 0.189*** 0.178*** 0.190*** 0.178***

(0.008) (0.010) (0.008) (0.010)

lnKL 0.219*** 0.209*** 0.223*** 0.220*** 0.218*** 0.209***

(0.003) (0.005) (0.003) (0.005) (0.003) (0.005)

FC 0.170*** 0.087*** 0.158*** 0.082***

(0.013) (0.020) (0.013) (0.020)OFC 0.197*** 0.111***

(0.014) (0.021)

HMTFC 0.149*** 0.074***

(0.014) (0.020)

SOE 0.458*** 0.081*** 0.458*** 0.081*** 0.457*** 0.080***

(0.022) (0.031) (0.022) (0.031) (0.022) (0.030)

PRIVATE 0.032*** 0.023* 0.035*** 0.023* 0.032*** 0.024**

(0.011) (0.013) (0.011) (0.013) (0.011) (0.013)

WB 0.003*** 0.002*** 0.003*** 0.002*** 0.003*** 0.002***

(0.0002) (0.0002) (0.0002) (0.0002) (0.0002) (0.0002)

lnPGDP 0.047** 0.547*** 0.057*** 0.544*** 0.043** 0.546***

(0.019) (0.077) (0.019) (0.077) (0.019) (0.077)

lnRD 0.019 0.050 0.013 0.054 0.015 0.049

(0.013) (0.037) (0.013) (0.037) (0.013) (0.037)

lnFDI 0.045*** 0.113*** 0.049*** 0.123*** 0.050*** 0.114***

(0.011) (0.029) (0.011) (0.029) (0.011) (0.029)Industry Year yes yes yes yes yes yes

yes yes yes yes yes yes

Adj. R2 0.202 0.150 0.196 0.140 0.202 0.150

Hausman test 673.90*** 777.03*** 680.18***

# of obs. 83,801 83,801 83,801 83,801 83,801 83,801

Note: Figures in parentheses are standard deviations. ***, **, *, and + represent significance at the 1%, 5%, 10% and 15% statistical levels.



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From a closer look at the magnitude of the estimated coefficients, one more interesting finding arises. We can see that theestimated coefficient for EGHis higher than that ofEGM, implying that compared to relatively concentrated areas, highly spatiallyconcentrated areas appear to exercise a stronger positive externality effect, thus inducing stronger productivity-enhancing effectson firms located in those regions.

It is worth emphasizing here that estimates of the EG index variables in Table 4 reflect a positive but decreasing productivityeffect induced by industrial agglomeration. However, the above analysis shows that the productivity-enhancing effect for textilefirms in highly spatially agglomerated areas is much higher than that in relatively concentrated areas. By drawing from acombination of these analyses, it is suggested that the phenomenon ofagglomeration economies does exist among textile firms inChina. More importantly, the degreeof industrial agglomeration is not so high as to result in agglomeration diseconomies during thesample period.15

6. Conclusions and policy implications

While an abundance of urban economic theories have utilized the concept of agglomeration to explain the formation ofindustrial concentration and have predicted a positive impact of spatial concentration on productivity brought about by positiveexternalities, existing empirical studies using aggregate or micro datasets of developed countries have also identified evidence ofagglomeration economies for manufacturing industries. In this paper, we have sought to explore the interconnections betweenindustrial agglomeration and economic activity in China, paying special attention to observe the degree of spatial concentrationwithin the textile industry and to examine the role of industrial agglomeration as a conduit for firm-level productivity. China'stextile industry provides us with an excellent case study because the China economy experienced a series of open policies prior to

its accession to the WTO in 2001. Moreover, most of the previous studies focused on examining the advantages of agglomerationexternalities in the high-tech industries rather than traditional industries. If the New Economic Geography theory is robust, itshould be broadly applied to a variety of industries and countries.

By using an unbalanced panel dataset of Chinese textilefirms during the period 20002005, wefirst depicted and discussed thespatial distribution of the textile industry. Before the open policy was implemented in the 1980s, textile firms were forced tospread out across different provinces due to the restrictions imposed upon regional policy. However, it is obvious that there hasbeen a tendency toward industrial agglomeration among the southeastern coastal regions in the textile industry. This can beattributed to the open policy that induced textile firms to (re)locate plants in the coastal regions, with a view to serving the largelocal as well as export markets. By adopting the agglomeration index developed by Ellison and Glaeser (1997) (EG index),we found that the overall weighted city EG index among three-digit textile industries ranged from 0.00017 to 0.00022 duringthe 20002005 period. Overall there appears to have been a twin-peaked distribution in the city EG index during that period.However, the EG index of spatial concentration for each year exhibits an increasing trend for textile industries' agglomeration anda decreasing trend of spatial agglomeration for the garments and other fiber products in China, which is similar to the findings of

Lu and Tao (2009).Next, in this study we examined the relationship between industrial agglomeration and firm-level productivity in China'stextile industry. Estimates obtained from various specifications confirmed the commonfinding that industrial agglomeration has asignificantly positive impact on firm-level labor productivity, validating the application of the New Economic Geography theoryto a developing and transition economy such as China. Moreover, the positive relationship between industrial agglomeration andproductivity was found to be nonlinear in that highly-concentrated areas seemed to give rise to a stronger productivity-enhancingeffect through positive externalities.

Interestingly, the productivity-enhancing effect brought about by industrial agglomeration was observed to be stronger forsmall firms, ceteris paribus. This can be attributed to the positive externalities of industrial agglomeration that can largely reducethe transaction costs of labor and intermediate inputs for small firms so that they benefit much more from the advantages ofindustrial agglomeration as they promote their productivity. In addition, we find that ownership matters to productivity in thatforeign-owned enterprises experienced higher productivity, especially the non-Hong Kong, Macau, and Taiwan foreign-ownedenterprises. Finally, we also demonstrate that state-owned enterprises have lower productivity than other types of enterprises.

This may be caused by a heavy historical load and social burden in Chinese state-owned textile enterprises which always lackedeffective constraints and incentive mechanisms in regard to operational controls and ownership. The government should absorbthe foreign management experience and speed up the reform of state-owned enterprises to improve the SOEs' productivity.Moreover the smaller firms have higher productivity than the larger firms.

Several important policy implications are derived from the empirical analysis. First, due to the existence of positive ag-glomeration externalities that can enhance firm-level productivity, the government could continue to implement its sciencepark policy, in order to create agglomerations of high-tech firms with positive externalities that will further enhance the high-tech firms' productivity. Since agglomeration diseconomies may occur as the spatial concentration increases to a certain degree,the government should note this possibility. Second, small firms established in more clustered regions could obtain moreexternal economic benefits of agglomeration than large firms, for example, through information sharing, greater ease in findingprofessionals, the savings from lower transportation costs, and so on. Thus the government could set up an industrial zone for

15 (1) The sensitivity analysis we conducted seems to suggest that different ways of de flating variables do not result in any material differences. (2) As for the

endogenous relationship between agglomeration and productivity, we simply used the number offirms and employment to deal with this problem. The resultsshowed that agglomeration still has a positive effect on productivity.

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small firms to help them to improve their productivity and development. Third, foreign enterprises have the highest productivity,state-owned enterprises have the lowest productivity, and private enterprises are ranked second in terms of productivity, whichis increasing. Therefore, the government could encourage the establishment of private enterprises, and state-owned enterprisesshould carry out institutional reforms to strengthen their management and production efficiency.

Our results need to be qualified in light of the data limitations and measurement problems present in the sample of medium-sized and large firms. In particular, we do not know whether the results apply to small enterprises. This paper studies how theagglomeration impacts the labor productivity. As for the causal relationship that results from the high productivity among theclustered firms, the current literature in this area is limited. This is an area where more research could be conducted in the future.

References

Au, C. C., & Henderson, J. V. (2006a). Are Chinese cities too small? Review of Economic Studies, 73, 549576.Au, C. C., & Henderson, J. V. (2006b). How migration restrictions limit agglomeration and productivity in China? Journal of Development Economics, 80, 350388.Aw, B. Y., & Lee, Y. (2008). Firm heterogeneity and location choice of Taiwanese multinationals. Journal of International Economics, 75, 167179.Batises, C. (2002). Dynamic externalities and local growth: A panel data analysis applied to Chinese provinces. China Economic Review, 13, 231251.Beardsell, M., & Henderson, V. (1999). Spatial evolution of the computer industry in the USA. European Economic Review, 43, 431456.Bernard, A. B., & Jensen, J. B. (1999). Exceptional exporter performance: Cause, effect, or both. Journal of International Economics, 47, 125.Bernard, A. B., & Jensen, J. B. (2004).

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