Geographic Proximity and Location Choice of Foreign Direct Investment in China

Original Article

Geographic proximity and location choice of

foreign direct investment in ChinaReceived 13 May 2011; revised 10 August 2012; accepted 31 August 2012

Sui-Hua Yua,* and Chen Feng ShenbaDepartment of Accounting, National Chung Hsing University, 250, Kuo Kuang Road, Taichung

402, Taiwan, Republic of China.bDepartment of Accounting and Information Technology, National Chung Cheng University,

168 University Road, Min-Hsiung, Chia-Yi 62102, Taiwan, Republic of China.

*Corresponding author.

Abstract Our research examines the concept of geographic proximity, particu-larly proximity to knowledge, market and labor resources in China, to understandhow the embedded spatial context of a region influences the preferences of enter-prises investing in China. Using a sample of Taiwanese enterprises investing inChina from 2007–2011, we find that proximity to knowledge and labor resourceshave a significant negative influence on the location choice of Taiwanese enterprises,whereas proximity to market has a significant positive influence. Furthermore, wefind that the influence of proximity on location choice changes with differentindustry characteristics.Asian Business & Management (2013) 12, 351–380. doi:10.1057/abm.2013.4;published online 20 February 2013

Keywords: foreign direct investment; location choice; geographic proximity

Introduction

In 1979, China implemented economic reforms and an open-door policy,allowing its economy to develop rapidly. The expanding internal demand andadvantages of low-cost labor and land have been attracting an increasing inflowof foreign direct investment (FDI). For example, in 2003, FDI inflows intoChina constituted 10 per cent of FDI worldwide and 30 per cent of FDI indeveloping countries (UNCTAD, 2005). During the period 1991–2007, FDI inChina increased tenfold, and such foreign investment and commercial activitybecame a main factor stimulating Chinese economic growth (Das, 2007).Therefore, management of FDI in China is an important and widely discussed

r 2013 Macmillan Publishers Ltd. 1472-4782 Asian Business & Management Vol. 12, 3, 351–380www.palgrave-journals.com/abm/

topic in academic circles, particularly with regard to how foreign enterprisesinvesting in China make location decisions at regional level.

China has a vast land area, with widespread regional heterogeneity in terms ofaverage income, average wages and resources; these regional differences have astrong influence on investment decisions by foreign investors (Chan et al, 2010).Much research has been conducted on the issue of location in China (Kang andLee, 2007), including descriptive studies (Beamish andWang, 1989) and empiricalstudies (Chadee et al, 2003). Recently, several authors have analyzed factorsinfluencing foreign enterprises in China faced with choice of location. Forexample, Du et al (2008) analyze how differences in economic system influencelocation choice by US enterprises in China. Zhou et al (2002) discuss locationchoices by Japanese enterprises, particularly the establishment of the specialeconomic zones and opening of coastal cities. Cheng and Stough (2006) report onthe influence of factors such as market size, energy costs and industrialagglomeration on Japanese investment. Finally, Kang and Lee (2007) discusslocation choice by Korean enterprises. Although the above studies have yieldedvaluable insights, such research focuses on analyzing the intrinsic characteristicsof locations, such as labor cost, market potential, business environment and gov-ernment policies, while ignoring the environment surrounding a given location.

According to network theory, a network is a set of nodes connected by aspecific type of relationship (Laumann, 1978), where nodes refer to individuals,events or organizations. The position of a node within a network determinesits accessibility to resources embedded in the network (Lin, 1999). Moreover,both the characteristics of a node and the network position of the nodeinfluence the outcome (Borgatti and Everett, 1992). Incorporating conceptsfrom network theory, economic geographers suggest that space is constructedwithin networks and the world is both built and stratified by stable sets ofrelationships (Murdoch, 2000). Position in a space influences the level ofintegration in world systems (Blainey, 2001). Applying this idea to differentgeographic units of analysis, such as cities and countries, several studies indicatethat the desirability of a geographic location is determined partly by its positionin a global system (for example, Smith and Timberlake, 2001; Nachum et al,2008). Therefore, to understand the advantage of a given region, one not onlyneeds to analyze the intrinsic characteristics of that region, but also the region’sposition within the geographic environment. In addition, recent managementresearch suggests that FDI be analyzed in a multilateral context (Strom andYoshino, 2009). Nachum et al (2008) show that no country is a stand-aloneentity; the positional context of a country influences how much multinationalenterprises (MNEs) are attracted to that country. Similarly, the position of aregion within a country may determine its value to investors and influenceenterprises’ willingness to invest. However, this factor has not received atten-tion in previous research.

Yu and Shen

352 r 2013 Macmillan Publishers Ltd. 1472-4782 Asian Business & Management Vol. 12, 3, 351–380

To fill this void, this study looks into the concept of geographic proximity toanalyze how spatial location influences the location choice of foreign investorsin China. Geographic proximity generally refers to geographic distance amongregions (Attfield et al, 2000; Sohn, 2004), which is a factor crucial to a region’sability to access resources from other regions (Herander and Saavedra, 2005).In this study, we characterize each region by its proximity to knowledge, marketand labor resources distributed throughout the country, and examine howthese proximity measures affect the preferences of enterprises investing there.We use a sample of Taiwanese enterprises investing in China. According to theChina Foreign Economic Statistical Yearbook (1979–2000), Taiwan is one ofChina’s top three investors. Owing to similarities in language, culture andtradition, China has always been an important region for Taiwanese foreigninvestment, particularly after 1990, when the Taiwanese government publishedits ‘Administrative Rules for Indirect Investment or Technological Cooperationin China’, drastically altering the foreign investment environment for Taiwaneseenterprises. Taiwanese investment in China entered a phase of rapid growth,and according to the Evaluative Report on Influences to Cross-Strait Trade andInvestment (Ministry of Economic Affairs of Taiwan, 2007), investments byTaiwanese enterprises in China from 1991 to 2006 totaled US$54.898 billion.Meanwhile, cross-strait trade as a percentage of total Taiwanese foreign traderose from 14.58 per cent in 1995 to 38.43 per cent in 2006. This has provided uswith abundant data.

From the analysis of the 5416 Taiwanese enterprises investing in China from2001–2007, proximity to knowledge and labor resources have a significantnegative impact on the likelihood that Taiwanese enterprises choose a givenregion, suggesting that the closer a region is to knowledge and labor resources,the more likely Taiwanese enterprises are to invest there. Meanwhile, proximityto market has a significant positive influence, indicating that Taiwanese enter-prises investing in China prefer regions farther from markets. As this outcomemay be related to the fact that most Taiwanese enterprises investing in Chinaare manufacturers, the data is further analyzed by industry. For food enter-prises, proximity to market is an important factor in choosing a location forinvestment; however, manufacturers tend to choose locations with greaterproximity to knowledge and labor resources.

This study makes several major contributions to the literature. First, scholarshave only recently begun to incorporate geographical factors into the analysisof FDI location choice (Blonigen et al, 2007; Jensen and Pedersen, 2011);however, their studies are limited to country-level analysis. This study contri-butes to this stream of research by offering a framework for how the spatialcontext of particular regions influences foreign investors’ location choice.Second, sub-national location decisions are particularly relevant for investorsin large and decentralized emerging markets (for example, China and Vietnam),

Geographic proximity and foreign direct investment

353r 2013 Macmillan Publishers Ltd. 1472-4782 Asian Business & Management Vol. 12, 3, 351–380

as policies, institutions and resource endowments vary at regional level in thesemarkets (Meyer and Nguyen, 2005). However, they have received relatively lessattention from international business scholars (Makino et al, 2002). This studyprovides new evidence on FDI location decisions in a country. Third, moststudies examining regional investments in a country have been dominated byreferences to the relative abundance of resources endowed in particular loca-tions (for example, Chung and Song, 2004). This study proposes that the attrac-tiveness of a particular region to foreign investors depends on the abundance ofresources inherent in a region as well as the amount of resources that can beaccessed from surrounding regions. Fourth, prior studies focus on analyzingdifferences among regions in China in terms of specific conditions or character-istics (for example, Cheng and Stough, 2006). To the best of our knowledge,ours is the first empirical study on the impact of spatial location on regionalinvestments in China. Finally, this study demonstrates that the concept pro-posed by Nachum et al (2008) to measure geographic proximity amongcountries may also be used to analyze smaller geographic units such asprovinces, showing that their proposed principle has wider applicability.

Following this introduction, the second section discusses the literature oflocation choice and develops research hypotheses. The third section describesresearch methods, including a description of the empirical model and variablesas well as this study’s sample selection and analysis techniques. The fourthsection reports the empirical results and analysis. Finally, the last sectiondiscusses the findings and presents suggestions for further study.

Literature Review and Hypothesis Development

As globalization increases, many enterprises invest overseas, particularly indeveloping countries, making FDI an important factor in economic develop-ment and a hot topic in management literature. Many researchers in this streamdiscuss location choice by enterprises engaging in FDI. Early research, such asthat by Kojima (1973), proposes that changes in economic factors in the homecountry lead enterprises to invest in other countries with a comparativeadvantage. Modern research analyzes two aspects of FDI separately whendiscussing location choice by MNEs (Tsang and Yip, 2007). In general,enterprises have two purposes for FDI: to better exploit existing assets and toseek new assets in the host country (Makino et al, 2002).

According to the asset-exploitation perspective, enterprises engage in FDI totransfer their own proprietary resources overseas; hence, enterprises use firm-specific advantages to enter a foreign market or find low-cost natural resources.According to the asset-seeking perspective, FDI enables enterprises to receivestrategic assets from the host country, such as technology, management

Yu and Shen


expertise or marketing expertise (Tsang and Yip, 2007). Strategic assets aredistributed in different countries, indicating that enterprises must seek strategicassets overseas to maintain long-term advantage (Dunning, 2000). However,regardless of perspective, prior research proposes that the purpose of FDI forenterprises is to tap into markets, natural resources or strategic assets unavail-able in the home country (Chung and Alcacer, 2002).

In general, geographic proximity can decrease transportation, managementand supervision costs and business risks (Davidson, 1980). Other paperssuggest that the investment location of enterprises influences the efficiency withwhich enterprises acquire resources, and thus the effectiveness of the enterprise(Alcacer and Chung, 2007; Nachum et al, 2008). In other words, the attractive-ness of a certain location to investors is determined by its proximity to desiredresources. On the basis of this idea, this study characterizes each region byproximity to various resources distributed across China. As the three mainresources that foreign enterprises seek are knowledge, market and labor (Jensenand Pedersen, 2011), we focus on examining how proximity to these threeresources affects FDI location in China.

According to resource-based theory, resources can be classified into tangible(such as land, factories and equipment) and intangible (such as brand, repu-tation, patents, technology and knowledge) (Hill and Jones, 1998). Intangibleresources, including knowledge, are difficult to imitate, which provides enter-prises with the distinctiveness necessary for sustainability and competitiveadvantage (Liebeskind, 1996). However, in a rapidly changing environment,organizations seeking to maintain advantage in knowledge must continuallygain more knowledge to build on existing resources and further promoteorganizational competitiveness (Grant, 1987). Knowledge can be producedwithin an enterprise or absorbed from the external environment. Recently,cross-border knowledge exchange has become more important in the exploi-tation of knowledge (Spencer, 2003). Therefore, increasing exposure to poten-tial knowledge spillover is an important consideration for enterprises decidinglocation.

Knowledge is considered partially tacit, and the transfer of knowledgerequires regular interaction, meaning that physical proximity is necessary forenterprises to acquire knowledge from a given location (Kogut and Zander,1992). Geographical proximity can contribute to information exchangeamong enterprises and can provide opportunities for face-to-face exchange(McDermott and Taylor, 1982). On the other hand, distance increases thedifficulty of acquiring knowledge (Nachum and Zaheer, 2005). Moreover,Spencer (2003) found that distance plays a role in cross-border exchanges ofknowledge and technology, meaning that having a source of proximate know-ledge is important in acquiring knowledge. In addition, many empirical studiesdemonstrate that knowledge spillover exists, and that its extent increases with



proximity (Almeida and Kogut, 1999; Storper and Venables, 2004). Furthermore,geographic distance increases the cost of knowledge and technology transfer(Leamer and Storper, 2001). Therefore, regions closer to areas of dense know-ledge in China have more knowledge that can be acquired, and enterprises aremore willing to invest in those regions.

Furthermore, with the increasing speed of knowledge exploitation, thesooner knowledge is acquired, the higher its value. As geographic proximity isbeneficial for earlier knowledge acquisition (Gaspar and Glaeser, 1998),geographic proximity has high value for enterprises. On the other hand,distance makes knowledge transmission and transfer difficult, and the knowl-edge base in remote regions is weaker (Chung and Alcacer, 2002). Enterprisesoperating in remote regions are thus at an information disadvantage, which cannegatively impact on investment performance. Therefore, the chance of enter-prises choosing to invest in such regions may decrease. That is, the closer theregions are to areas where knowledge is distributed, the more likely theenterprises are to invest in these regions.

Hypothesis 1: Greater proximity of a province (region) to knowledge in Chinaincreases the probability of Taiwanese enterprises investing inthe region.

Market potential is an important factor for enterprises in deciding whetherto invest overseas (Choi et al, 1986). In addition, several papers find that thehigher the market growth potential, the more likely a region is to becomea location for overseas investment (for example, Galan and Gonzalez-Benito,2001). This is the main reason that China, with a population of 1.45 billion, hasgreat market potential and has attracted Taiwanese investment. Market-orientedenterprises mainly focus on market size and growth rate in the investmentregion, and in order to lower transportation costs, they will most probablyinvest in locations close to the market (Enright and Scott, 2000).

The Economist (2005) reported that enterprises are unwilling to investoverseas mainly because of high transportation costs. Moreover, enterprisesinvesting close to the market are better able to grasp the local culture, economicactivity and laws and regulations faster than their competitors (Craig et al,1992); hence, they can better serve the regional market under the system set upby the country. Geographically closer regions can more easily establish partner-ships to facilitate market access between them.

Hypothesis 2: Greater proximity of a province (region) to the market in Chinaincreases the probability of Taiwanese enterprises investing inthe region.

Carr et al (2001) found that asset-seeking enterprises, which pursue low costsfor production and trade, observe comparative advantages and divide business

Yu and Shen


activities according to categories such as supply of raw materials, production,export, sales and retail operations in different regions, to decrease productionand exchange costs. Many empirical studies find that foreign enterprises investin China mainly to take advantage of its high-quality/low-cost labor resources(Cheng and Stough, 2006; Gao, 2005). Therefore, a region’s proximity to laborresources increases the potential for investment there. In particular, as laborresources are farther from the investment region, expected returns are lower,reducing the incentive for enterprises to invest in those regions (Redding andSchott, 2003). Moreover, current research states that geographic proximityhelps promote learning of product technology and commercial trade (Salomonand Shaver, 2005), decreases costs for information and management, andlowers monitoring costs and risks due to uncertainty, which increases theeffectiveness of resource management and distribution (Davidson, 1980).Therefore, a region’s greater proximity to abundant labor resources lowerslocal production costs for enterprises, and thus helps attract investment.

Hypothesis 3: Greater proximity of a province (region) to labor resources inChina increases the probability of Taiwanese enterprises invest-ing in the region.

Research Method

Sample and data collection

Firms listed on the Taiwan Stock Exchange Corporation and investing in Chinaover the 7-year period 2001–2007 comprise the sample for this study. Informa-tion is mainly collected from the Taiwan Economic Journal (TEJ), the MarketObservation Post System (MOPS) and the China Statistical Yearbook publishedby China Statistics Press.

Empirical models

The research utilizes the conditional logit model by McFadden (1973) in thecontext of econometrics, which is well suited for the modeling of polychoto-mous choice situations (Hoffman and Duncan, 1988). Compared with otherdiscrete-choice models, such as the multinomial logit model, the conditionallogit model is particularly appropriate for models in which the choice amongalternatives is modeled as a function of the characteristics of the alternatives, inaddition to the characteristics of the individual making the choice (So andKuhfeld, 1995; Greene, 2008). This methodology has been well developedand widely used in estimating location choices of FDI (for example, Belderbos and



Carree, 2002; Alcacer and Chung, 2007). Following prior studies, we employthe conditional logit model to analyze the location choices of Taiwan investorsin China. After considering controls and variables, the study tests the followingtheoretical model:

INVijt¼ a0þ a1NKijt� 1þ a2NMijt� 1þ a3NLRijt� 1þ a4KNOWijt� 1

þ a5MARKETijt� 1þ a6LABORijt� 1þ a7GDPijt� 1

þ a8INFRASINVijt� 1þ a9INDUSIZEijt� 1þ a10COASTALijt� 1

þ a11SIZEitþ a12PER SALEitþ a13RDitþ a14GSit

þ a15CAPINTSIVEitþX3

k¼ 1

bkINDUiktþX6

n¼ 1

gnYEARintþ e

INVijt whether enterprise i invests in province (region) j duringperiod t

NKijt�1 proximity to knowledge of province (region) j for enterprisei during period t�1

NMijt�1 proximity to market of province (region) j for enterprise iduring period t�1

NLRijt�1 proximity to labor of province (region) j for enterprise iduring period t�1

KNOWijt�1 knowledge stock of province (region) j for enterprise iduring period t�1

MARKETijt�1 market potential of province (region) j for enterprise iduring period t�1

LABORijt�1 labor resources of province (region) j for enterprise i duringperiod t�1

GDPijt�1 GDP of province (region) j for enterprise i during periodt�1

INFRASINVijt�1 basic infrastructure investment of province (region) j forenterprise i during period t�1

INDUSIZEijt�1 level of industrialization of province (region) j for enterprisei during period t�1

COASTALijt�1 whether province (region) j is a coastal region for enterprisei during period t�1

SIZEit firm size of enterprise i during period tPER_SALEit average sales per employee for enterprise i during period tRDit R&D intensity for enterprise i during period tGSit sales growth rate of enterprise i during period tCAPINTSIVEit capital intensity of enterprise i during period tINDUikt industry dummies k for enterprise i during period tYEARint year dummies n for enterprise i during period t

Yu and Shen


Measurement of variables

Dependent variablesThe dependent variable is derived from the annual reports of listed enterprises,and analysis of the TEJ database and MOPS ‘Summary of Investments inChina’. When defining investment regions, the study adopts China’s classifi-cation of regions, as all vital statistics in China appear by region. According tothe definitions used by the China Bureau of Statistics, 31 economic areasspan China. Specifically, China is officially organized into 22 provinces(Anhui, Fujian, Gansu, Guangdong, Guizhou, Hainan, Hebei, Heilongjiang,Henan, Hubei, Hunan, Jiangsu, Jiangxi, Jilin, Liaoning, Qinghai, Shandong,Shaanxi, Shanxi, Sichuan, Yunnan and Zhejiang), 4 municipalities(Beijing, Tianjin, Shanghai and Chongqing) and 5 autonomous regions (InnerMongolia, Guangxi Zhuang, Tibet, Ningxia Hui and Xinjiang Uyghur). Ifenterprise i remits investment during year t to a specific Chinese province(region) j, INVijt¼ 1, otherwise INVijt¼ 0.

Independent variablesThe independent variables include proximity to knowledge, proximity tomarket and proximity to labor resources. For measurement of proximity, thestudy applies the metrics of Nachum et al (2008) and Alfaro et al (2008). Thestudy includes proximity of the region j to knowledge, market and laborresources in all other regions r.

Proximity to knowledge : NKj ¼X30

r¼ 1

½DISjr�PATENTr�

Proximity to market : NMj ¼X30

r¼ 1

½DISjr�PCGDPr�

Proximity to labor : NLRj ¼X30

r¼ 1

½DISjr�LABORr�

DISjr standardized distance between the province (region) j, where theenterprise invests, and the province (region) r

PATENTr number of patents approved in province (region) rPCGDPr average per capita GDP in province (region) rLABORr number of people with an education level above middle school in

province (region) r

For independent variables, we first compute all the bilateral distancesbetween regions j and regions r, and then subtract the average distance divided



by the standard deviation as standardized values. We then multiply thestandardized distance (DISjr) by knowledge (PATENTr), market (PCGDPr)and labor (LABORr). The values obtained are then converted to an index from1 to 100 using the following formula:

Index Value¼ Original Value�Original Min Value

Original Max Value�Original Min Value�ð100� 1Þþ 1

This index will produce scores from 1 to 100. The index measures proximityto knowledge, market and labor. The smaller the number, the greater theproximity; the larger the number, the greater the distance.

The following three variables are used to measure the intrinsic endowmentsof each region and examine how a region’s intrinsic endowments influence theinvestment location of enterprises. In economics, a country’s factor endowmentis commonly understood as the amount of production factors that a countrypossesses (Krueger, 1968; Wilson, 1991). Intrinsic endowments here refer to theamount of resources inherent to a region. Prior studies indicate that regionswith a large resource endowment tend to be more desirable for foreign investorsthan those with a small endowment, all other things being equal (for example,Gao, 2005). In particular, three kinds of resources are important, namelyknowledge, market and labor resources (Jensen and Pedersen, 2011). Therefore,we measure a region’s intrinsic endowments by its current knowledge stock,market potential and labor force, and include these variables in the model ascontrols.

1. Knowledge stock (KNOW ): The number of patents approved in eachinvestment province (region).

2. Market potential (MARKET ): Average per capita GDP in each investmentprovince (region).

3. Labor resources (LABOR): Number of people with education level abovemiddle school in each investment province (region).

Control variablesTo control for factors such as special characteristics of each province (region)and special characteristics of the enterprises, the model includes the followingcontrol variables:

1. Basic infrastructure (INFRASINV): Many empirical studies find that basicinfrastructure is important in attracting overseas investment; the morecomplete the basic infrastructure of an investment region, the lower theproduction cost for enterprises, which helps attract enterprises to invest andset up factories in the region (Cheng and Stough, 2006). Therefore,

Yu and Shen


INFRASINV is a control variable measured by the amount of investment inbasic infrastructure in each province (region) each year.

2. GDP (GDP): GDP represents the development potential for the marketand the market size. The higher the GDP, the greater the market potentialand market size, and the more enterprises the region attracts (Belderbosand Carree, 2002). Therefore, the study includes GDP as a measure ofmarket potential.

3. Level of industrialization (INDUSIZE): The level of industrialization canshow the development level of a region’s economy. A higher level ofindustrialization indicates that an economy of scale has developed, whichhelps lower production costs for enterprises. This study uses the ratio ofworkers in the manufacturing industry to total workers in the province(region) to measure the level of industrialization.

4. Coastal region (COASTAL): The differences in economic performanceof Chinese coastal and inland regions are vast. Therefore, coastal regionis used as a control variable to test whether Taiwanese enterprises havea preference for coastal regions for investment. Enterprises investing incoastal provinces (regions) are designated 1, otherwise 0.

5. Firm size (SIZE): The size of the enterprise influences whether an enterpriseinvests overseas, as larger enterprises have more potential for transnationalinvestments than smaller enterprises. Capital invested is used as a measureof firm size.

6. Firm performance (PER_SALE): Enterprise performance influenceswhether the enterprise invests overseas, because enterprises invest overseasto efficiently manage and distribute resources in ways more beneficial tothem, allowing them to better utilize resources and promote overallcompetitiveness. On the basis of research such as that by Nachum et al(2008), this study uses total sales divided by total employees as a measure offirm performance.

7. R&D intensity (RD): R&D intensity reflects the commitment of anenterprise to R&D. Recent studies on overseas investment found thatextensive knowledge exploration offers enterprises the ability to developappropriate solutions and avoid falling into the technological framework ofexisting models. Therefore, R&D ability influences whether enterprises arewilling to invest overseas. The ratio of R&D expenses to net sales is used asa measure of R&D intensity.

8. Sales growth (GS): Sales growth shows an enterprise’s potential for growth;a higher potential for growth will lead to increased foreign investment toobtain greater profit (Allen and Pantzalis, 1996). Corporate potentialgrowth is measured by corporate-profit growth rate.

9. Capital intensity (CAPINTSIVE): Capital intensity shows the specialcharacteristics of an enterprise. As China has the abundant labor resources



that Taiwan lacks, enterprises with lower capital intensity will invest inChina to take advantage of the abundant and cheap labor to reduceproduction costs. The ratio of an enterprise’s fixed assets to total assets isused to measure capital intensity.

10. Industry dummies (INDU): To control for different industries, this studydesigns three dummy variables to measure whether enterprises are in theelectronics, service or food industries.

11. Year dummies (YEAR): As the sample period covers 7 different years, thisstudy incorporates six dummy variables in the model to control fordifferences between years.

Empirical Results

Descriptive statistics

Our research sample comprises Taiwanese-listed enterprises investing in China.This study obtained proof of investment from the TEJ, and data on yearlyenterprise investment remittance from the MOPS. The sample covered a 7-yearperiod and a total of 5416 enterprises, mostly from the electronics manufactur-ing industry (61.34 per cent); the rest are enterprises in other manufacturingindustries (32.98 per cent), service industry (3.56 per cent) and food industry(2.12 per cent). Of the 5416 enterprises, 120 had missing information in theindustry category, and were eliminated, leaving 5296. As enterprises choose toinvest in one of the 31 provinces (regions) in China, the sample is 5296� 31,giving a total sample size of 164 176 for testing the logistic model.

The descriptive statistics for the variables in the empirical model can be foundin Table 1. The means for proximity to knowledge (NK), proximity to market(NM) and proximity to labor (NLR) are close. Moreover, the highest valuefor GDP (GDP) is 26 204.47, whereas the lowest is only 117.46, showing thatmarket size and potential of certain regions are far higher than for others. Thehighest value for basic infrastructure (INFRASINV) is 29 657.32, whereasthe lowest is just 64.05, with a standard deviation of 5869.82, showing that thebasic infrastructure of some regions is much better than for others. Observingvariables measuring enterprise characteristics, namely, R&D intensity (RD),sales growth (GS) and firm size (SIZE), significant differences were foundamong the enterprises.

To perform a logit analysis, we first performed a Pearson correlationanalysis, with the resulting matrix shown in Table 2. Proximity to knowledge(NK), proximity to market (NM) and proximity to labor (NLR) have a signifi-cant positive correlation, suggesting that knowledge, market and labor areconcentrated in certain regions in China. Meanwhile, there are significant

Yu and Shen


negative correlations (coefficients between �0.44 and �0.26) when comparingproximity to knowledge, (NK), proximity to market (NM) and proximity tolabor (NLR) with knowledge stock (KNOW), market potential (MARKET) andlabor resources (LABOR), respectively, indicating that regions with abundantresources do not necessarily have lower proximity measures.

In addition, for the most part, the correlation coefficients between thevariables for firm characteristics (including enterprise size (SIZE), firm perfor-mance (PER_SALE ), R&D intensity (RD), sales growth (GS) and capitalintensity (CAPINTSIVE ) and region variables (including proximity to knowl-edge (NK), proximity to market (NM) and proximity to labor resources (NLR),as well as basic infrastructure (INFRASINV ) and level of industrialization(INDUSIZE ) are lower than 0.3, indicating no multicollinearity problem.Variance inflation factor (VIF) analysis is further performed to test for multi-collinearity; the results show that the VIF is no greater than 10 (Hair et al,1998), indicating that no multicollinearity problem exists in the regressionmodel. Thus, all variables are included in the logit analysis.

Regression results

Table 3 presents the regression results. According to the table, a region’sintrinsic knowledge, market and labor resources consistently show positive

Table 1: Descriptive statistics

Variable Code N Mean Standard

deviation

Minimum Maximum

Proximity to

knowledge

NK 167 896 41.65 25.75 1.00 100.00

Proximity to market NM 167 896 42.90 31.50 1.00 100.00

Proximity to labor NLR 167 896 34.23 26.12 1.00 100.00

Intrinsic knowledge KNOW 167 896 6440.38 10 274.74 7.00 72 220.00

Intrinsic market MARKET 167 896 1.47 1.12 0.28 7.30

Intrinsic labor LABOR 167 896 22 471.27 15 257.86 292.36 71 940.60

GDP GDP 167 896 5043.52 4600.76 117.46 26 204.47

Infrastructure INFRASINV 167 896 4853.09 5869.82 64.05 29 657.32

Industrialization INDUSIZE 167 896 0.23 0.10 0.05 0.51

Coastal area COASTAL 167 896 0.35 0.488 0 1

Firm size SIZE 167 896 15.07 1.34 11.94 20.25

Firm performance PER_SALE 164 610 13 213.85 21 023.08 11.52 459 318.00

R&D intensity RD 164 610 0.03 0.14 0.00 7.00

Sales growth GS 167 462 0.18 1.04 �1.00 50.82

Capital intensity CAPINTSIVE 167 276 0.22 0.21 0.00 8.16



Table 2: Correlation matrix

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

1. Proximity toknowledge

1 — — — — — — — — — — — — — —

2. Proximity tomarket

0.709�� 1 — — — — — — — — — — — — —

3. Proximity tolabor

0.866�� 0.828�� 1 — — — — — — — — — — — —

4. Intrinsicknowledge

�0.278�� 0.259�� 0.299�� 1 — — — — — — — — — — —

5. Intrinsic market �0.288�� 0.438�� 0.399�� 0.520�� 1 — — — — — — — — — —6. Intrinsic labor �0.374�� 0.386�� 0.380�� 0.500�� 0.005�� 1 — — — — — — — — —7. GDP �0.390�� 0.435�� 0.409�� 0.802�� 0.496�� 0.740�� 1 — — — — — — — —8. Infrastructure �0.263�� 0.271�� 0.304�� 0.781�� 0.605�� 0.469�� 0.849�� 1 — — — — — — —9. Industrialization �0.504�� 0.576�� 0.616�� 0.576�� 0.585�� 0.354�� 0.627�� 0.545�� 1 — — — — — —

10. Coastal area �0.501�� 0.413�� 0.393�� 0.472�� 0.437�� 0.258�� 0.512�� 0.341�� 0.579�� 1 — — — — —11. Firm size 0.001 �0.001 0.000 0.011�� 0.025�� 0.000 0.018�� 0.035�� 0.010�� 0.000 1 — — — —12. Firm

performance0.003 �0.003 �0.009�� 0.027�� 0.049�� 0.008�� 0.037�� 0.068�� 0.026�� 0.000 0.258�� 1 — — —

13. R&D intensity 0.000 0.000 �0.001 0.002 0.007�� 0.000 0.004� 0.008�� 0.001 0.000 �0.120�� 0.075�� 1 — —14. Sales growth 0.000 0.000 �0.006�� 0.008�� 0.007�� 0.001 �0.007�� 0.019�� 0.001 0.000 �0.010�� 0.050�� 0.071�� 1 —15. Capital intensity �0.003 0.003 0.010�� 0.024�� 0.051�� 0.005�� 0.037�� 0.066�� 0.025�� 0.000 0.072�� 0.218�� 0.028�� 0.030�� 1

� , �� and �� indicate 10 per cent, 5 per cent and 1 per cent significance level, respectively.

Note: Number of observations included in the analysis of correlations between variables is 164 176.

YuandShen

364

r2013Macm

illanPublish

ersLtd.1472-4782

Asia

nBusin

ess&

Managem

ent

Vol.12,3,351–380

effects on location choice by Taiwanese enterprises investing in China. This isconsistent with current theories, and shows that an abundance of knowledge,market and labor resources in a region is very important in influencinginvestors’ location choice.

Table 3: The impact of proximity measures on location choice

Variables Code Expected

sign

W/O proximity

measures

With proximity

measures

Intercept INTERCEPT ? �10.370�� (2026.908) �9.259�� (1430.376)Proximity measures

Proximity to

knowledge

NK � — �0.038�� (249.814)

Proximity to market NM � — 0.014�� (41.964)Proximity to labor NLR � — �0.007� (3.642)

Intrinsic endowments

Intrinsic knowledge KNOW þ 0.000�� (37.277) 0.000�� (40.083)Intrinsic market MARKET þ 0.824�� (1132.370) 0.795�� (984.640)Intrinsic labor LABOR þ 0.000�� (4.782) 0.000�� (3.825)

Control variables

GDP GDP þ 0.000�� (839.486) 0.000�� (832.977)Infrastructure INFRASINV þ 0.000�� (352.010) 0.000�� (406.815)Industrialization INDUSIZE þ 5.028�� (189.765) 2.871�� (61.150)Coastal area COASTAL ? �0.008 (0.009) �0.261�� (8.410)Firm size SIZE þ 0.204�� (267.303) 0.206�� (269.215)Firm performance PER_SALE þ 0.000�� (15.781) 0.000�� (15.933)R&D intensity RD ? 0.068(0.320) 0.069 (0.324)

Sales growth GS þ 0.038�� (4.926) 0.039�� (4.975)Capital intensity CAPINTSIVE � �0.615�� (28.443) �0.620�� (28.695)

Year and industry dummies

Electronics industry INDU_elect ? 0.309�� (66.709) 0.312�� (67.245)Food industry INDU_food ? �0.237�(3.269) �0.239� (3.297)Service industry INDU_service ? �0.012 (0.014) �0.012 (0.014)Year 2002 YEAR_2002 ? �0.308�� (19.660) �0.379�� (23.222)Year 2003 YEAR_2003 ? �0.887�� (151.409) �0.939�� (129.975)Year 2004 YEAR_2004 ? �1.838�� (540.784) �1.805�� (409.599)Year 2005 YEAR_2005 ? �0.618�� (38.211) �0.354�� (10.875)Year 2006 YEAR_2006 ? �1.892�� (203.067) �1.189�� (66.259)Year 2007 YEAR_2007 ? �3.313�� (471.666) �2.522�� (241.748)

Degree of freedom (DF) 21 24

�2�log-likelihood 28 249.50 27 727.20

Nagelkerke R2 0.326 0.340

N 164 176 164 176


Note: Wald statistics included in parentheses.



The log-likelihood ratio (�2�log-likelihood) for the model including theproximity measures and for the model without proximity measures is 27 727.20and 28 249.50, respectively, which are both significant at 99 per cent confidencelevel. In addition, the predictability of the model with the measures forproximity is higher than the predictability of the model without the measures,indicating that proximity measures have incremental explanatory power withregard to location choice.

As shown in Table 3, proximity to knowledge, proximity to market andproximity to labor resources each have a different influence on location choiceby Taiwanese enterprises. The coefficient for proximity to knowledge (NK)is �0.038, which is significant at the 1 per cent level, suggesting that regionsmore centrally located with regard to the spatial distribution of the country’sknowledge are more likely to be chosen for investment, which supportsHypothesis 1. Moreover, this suggests that the cost of accessing knowledge,higher in regions that are farther away, decreases an enterprise’s desire to investin a region.

The coefficient for proximity to market (NM) has a significant positive effect(0.014, P-valueo0.01), indicating that regions less centrally located with regardto the country’s markets are more likely to be chosen for Taiwanese enterprises’investment locations, which is contrary to Hypothesis 2. It is generally observedthat many Taiwanese manufacturers entering China early set up productionbases in China; however, they mainly export their products, instead of focusingon the Chinese market. Therefore, being close to the market has relatively fewerbenefits, particularly as the facility costs are higher in high-population areas.As the sample period is 2001–2007 and the sample for this study mainlycomprises manufacturers, particularly electronics manufacturers, the resultsfrom this study are consistent with practical observations.

Finally, consistent with Hypothesis 3, the coefficient for proximity to laborresources (NLR) has a significant negative effect (�0.007), suggesting thatproximity to regions with large labor pools also increases the likelihood ofTaiwanese enterprises investing in that region.

Furthermore, according to Table 3, most of the control variables are stati-stically significant, which is consistent with the expectations. First, the coeffi-cients for basic infrastructure (INFRASINV) and level of industrialization(INDUSIZE) are positive and statistically significant, indicating that the higherthe level of basic infrastructure and industrialization, the more likely Taiwaneseenterprises are to invest in the region. This is consistent with past location-choice research, and shows that geography is an important influence in locationchoice. If these factors are absent from the regional model, judgment aboutlocation choice by enterprises will include bias. On the other hand, the coeffi-cients for the enterprise characteristics variables, including enterprise size(SIZE), firm performance (PER_SALE) and sales growth (GS), are positive

Yu and Shen


and statistically significant, suggesting that the larger the enterprise, the betterthe firm performance, the greater the sales growth – therefore, the more likelythe enterprise is to invest in China.

The coefficient for capital intensity (CAPINTSIVE) is negative and statisti-cally significant, suggesting that a labor-intensive enterprise will most probablyinvest in China. This is consistent with the main reasons given by Taiwaneseenterprises. In addition, the industry dummies are statistically significant,meaning that different industries might have different reasons and strategiesfor investing in China. The effect on the electronics industry is positive, whereasthe effect on the food industry is negative, showing that compared with othermanufacturing industries, enterprises in electronics manufacturing will mostprobably choose to invest in China. However, the food industry is less likely todo so. As the Chinese economy has developed and consumer purchasing powerhas increased, the number of enterprises investing in China is changing.However, these results are consistent with actual facts during the sample period.

Different industries have various considerations for location choice, due todifferent investment purposes. To gain additional insight into industrialvariations on the impact of proximity, this study examines location choiceacross various industries, specifically viewing the manufacturing, food andservices industries. Table 4 shows the results.

As shown in Table 4, the explanatory powers of the model for themanufacturing, food and services industries (Nagelkerke R2) are 0.349, 0.242and 0.270, respectively, whereas the log-likelihood ratios (�2�log-likelihood)are 26 118.95, 527.53 and 868.52, respectively, all of which are significant at 99per cent confidence level. However, the influence of the different proximityfactors on the various industries differs.

For manufacturers, the intrinsic characteristics of the regions significantlyinfluence location choice by enterprises, as knowledge stock (KNOW), marketpotential (MARKET) and labor resources (LABOR) all have a significantpositive effect. This indicates that the higher the knowledge stock, the greaterthe market potential, and the greater the labor resources of a region – therefore,the more likely Taiwanese enterprises are to invest in that region. Beyond theintrinsic characteristics of a region, the influence of proximity measures oninvestment position is also statistically significant, as proximity to knowledge(NK) and proximity to labor resources (NLR) have a significant negative effect,whereas proximity to market (NM) has a significant positive effect. Thisindicates that for manufacturers, regions closer to China’s knowledge and laborresources are more likely to attract investment. Meanwhile, as the effect ofproximity to market is significantly positive, this suggests that manufacturersare less likely to invest in regions that are proximate to Chinese markets.

For the food and services industries, the effect of knowledge endowments(KNOW) and labor resources (LABOR) is insignificant. Rather, the market is



Table 4: The impact of proximity measures on location choice_by industry

Variables Code Expected sign Food Service Manufacturing

Intercept INTERCEPT ? �8.334�� (10.306) �6.983�� (23.626) �9.355�� (1377.733)Proximity measures

Proximity to knowledge NK � 0.001 (0.013) �0.010 (0.926) �0.041�� (261.197)Proximity to market NM � �0.029�� (5.548) 0.037�� (10.288) 0.014�� (38.386)Proximity to labor NLR � 0.026 (2.552) �0.011 (0.491) �0.008�� (4.306)

Intrinsic endowmentsIntrinsic knowledge KNOW þ 0.000 (2.488) 0.000 (0.487) 0.000�� (46.938)Intrinsic market MARKET þ 0.432�� ((6.408) 1.063�� (63.065) 0.802�� (927.781)Intrinsic labor LABOR þ 0.000 (0.698) 0.000 (0.882) 0.000�� (5.499)

Control variablesGDP GDP þ 0.000 (0.167) 0.000�� (5.838) 0.000�� (844.196)Infrastructure INFRASINV þ 0.000 (0.083) 0.000 (1.425) 0.000�� (426.039)Industrialization INDUSIZE þ 4.879�� (3.857) 4.022�� (4.740) 2.774�� (52.768)Coastal area COASTAL ? 0.871� (2.819) 0.398 (0.819) �0.320�� (11.382)Firm size SIZE þ 0.270� (3.376) �0.059 (0.627) 0.213�� (274.787)Firm performance PER_SALE þ 0.000�� (6.649) 0.000 (2.050) 0.000�� (16.926)R&D intensity RD ? �64.550� (3.033) 18.762 (0.955) 0.069 (0.330)Sales growth GS þ 1.639 (2.075) �0.174 (0.744) 0.042�� (5.949)Capital intensity CAPINTSIVE ? �1.519 (1.175) 1.464�� (9.453) �0.702�� (23.325)

Year dummiesYear 2002 YEAR_2002 ? �0.492 (1.042) �0.391 (0.779) �0.394�� (23.325)Year 2003 YEAR_2003 ? �0.960� (3.549) �0.824� (3.419) �0.975�� (129.264)Year 2004 YEAR_2004 ? �1.074�� (4.308) �2.057�� (16.617) �1.848�� (394.492)Year 2005 YEAR_2005 ? �1.780�� (5.726) �1.099� (3.801) �0.305�� (7.482)Year 2006 YEAR_2006 ? �1.500� (3.279) �2.388�� (9.723) �1.090�� (51.318)Year 2007 YEAR_2007 ? �3.094�� (9.115) �3.680�� (18.591) �2.452�� (211.351)

Degree of freedom (DF) 21 21 21�2�log-likelihood 527.53 868.52 26 118.95Nagelkerke R2 0.242 0.270 0.349N 3565 4836 155 775



YuandShen

368

r2013Macm

illanPublish

ersLtd.1472-4782

Asia

nBusin

ess&

Managem

ent

Vol.12,3,351–380

the most important consideration for location choice by these investors,particularly the intrinsic market potential of the region. Enterprises in theservice and food industries are more likely to invest in regions with highermarket potential.

Enterprises in the food industry are more likely to choose regions proximateto a market as an investment location. However, for the service industry,avoiding competition is more important, so Taiwanese enterprises will mostprobably choose regions that are higher in proximity, meaning that if theenterprises are not investing in regions with high market potential, they chooseremote regions away from markets to avoid competition, as a way to establish anew market.

Sensitivity analyses

The correlation coefficient for the relationship between proximity to knowledge(NK) and proximity to labor resources (NLR) is 0.866. Although the VIF didnot show any problems of multicollinearity, to eliminate any statistical issuesdue to the relationship between proximity to knowledge and proximity to laborresources (Allison, 1977), the study uses a method from prior literature (forexample, Mosteller and Tukey, 1977; Smith and Sasaki, 1979) to eliminatefactors related to proximity to knowledge in the measure for proximity to labor,and produce a new measure for proximity to labor. This test is implemented intwo steps. First, we estimate a pooled regression of proximity to labor on aconstant and on proximity to knowledge. The coefficient estimates on theconstant and proximity to knowledge are then used to construct estimates ofnew proximity to labor resources. That is, new proximity to labor resources isestimated as the residual in the regression of proximity to labor resources on aconstant and on proximity to knowledge; hence, the new proximity to laborresources is unrelated to proximity to knowledge. Second, original proximity tolabor resources is replaced with new proximity to labor resources and theempirical model is then estimated. As Smith and Sasaki (1979) demonstrate, thecoefficient for the new proximity to labor resources measure from the regressionusing this method is unbiased. The first column of Table 5 presents the results ofthe test.

As shown in Table 5, the intrinsic endowments of a region have a signifi-cant positive effect on investment location choice, whereas the measures forproximity have incremental explanatory power for location choice. Proximityto knowledge and proximity to labor resources have a significant negative effecton location choice, indicating that regions more centrally located in the spatialdistribution of China’s knowledge and labor resources are more likely tobecome investment locations. On the other hand, proximity to markets has



a significant positive effect on location choice, suggesting that if Taiwaneseenterprises do not choose locations for market potential, they will chooseregions distant from the Chinese markets to develop new markets.

Table 5: Sensitivity analyses

Variables Code Expected

sign

Alternative

measure no. 1

Alternative

measure no. 2

Intercept INTERCEPT ? �9.234�� (1417.205) �9.148�� (1399.663)Proximity measures

Proximity to

knowledge

NK � �0.043�� (313.532) �0.031�� (298.676)

Proximity to market NM � 0.014�� (41.964) 0.012�� (52.577)Proximity to labor NLR � �0.007� (3.642) �0.007� (3.642)


Intrinsic knowledge KNOW þ 0.000�� (40.083) 0.000�� (40.083)Intrinsic market MARKET þ 0.795�� (984.640) 0.795�� (948.640)Intrinsic labor LABOR þ 0.000�� (3.825) 0.000�� (3.825)

Control variables

GDP GDP þ 0.000�� (832.977) 0.000�� (832.977)Infrastructure INFRASINV þ 0.000�� (406.815) 0.000�� (406.815)Industrialization INDUSIZE þ 2.871�� (61.150) 2.871�� (61.150)Coastal area COASTAL ? �0.261�� (8.410) �0.261�� (8.410)Firm size SIZE þ 0.206�� (269.215) 0.206�� (269.215)Firm performance PER_SALE þ 0.000�� (15.933) 0.000�� (15.933)R&D intensity RD ? 0.069 (0.324) 0.069 (0.324)

Sales growth GS þ 0.039�� (4.975) 0.039�� (4.975)Capital intensity CAPINTSIVE � �0.620�� (28.695) �0.620��(28.695)


Electronics INDU_elect ? �0.312�� (241.748) 0.312�� (67.245)Food INDU_food ? �0.239� (3.297) �0.239� (3.297)Service INDU_service ? �0.012 (0.014) �0.012 (3.297)Year 2002 YEAR_2002 ? �0.379�� (23.222) �0.379�� (23.222)Year 2003 YEAR_2003 ? �0.939�� (129.975) �0.939�� (129.975)Year 2004 YEAR_2004 ? �1.805�� (409.599) �1.805�� (409.599)Year 2005 YEAR_2005 ? �0.354�� (10.875) �0.354�� (10.875)Year 2006 YEAR_2006 ? �1.189�� (66.259) �1.189�� (66.259)Year 2007 YEAR_2007 ? �2.522�� (241.748) �2.522�� (241.748)

Degree of freedom (DF) 24 24

�2�log-likelihood 27 727.20 27 727.20

Nagelkerke R2 0.340 0.340

N 164 176 164 176



Yu and Shen


To further decrease the correlation between the independent variables, thestudy uses a similar method to eliminate factors relating to proximity to know-ledge in the measure for proximity to market, to create a new measurefor proximity to market. In addition, this study eliminates factors relatedto proximity to knowledge and factors related to proximity to market in themeasures for proximity to labor resources, to create a new measure forproximity to labor resources. These two new measures are then run through aregression analysis, with the outcomes shown in the second column of Table 5.All three measures for proximity are statistically significant, and their directionis consistent with the above findings. This once again validates expectations inHypothesis 1 and Hypothesis 3.

Endogeneity issue

Another concern about our analysis is potential endogeneity, mainly reversecausality and omitted variables bias. In our empirical results above, we addresspossible endogeneity in several ways. First, extensive control variables areincluded in the model to resolve omitted variable bias. Specifically, all thefrequently examined determinants of FDI location choice in the literature areentered into the analysis, such as basic infrastructure (Cheng and Stough, 2006),gross domestic production (Zhao and Zhu, 2000), coastal area (Belderbos andCarree, 2002), level of industrialization (Sun et al, 2002) and firm characteristics(Makino et al, 2002).

Second, we use the discrete choice Instrumental Variable (IV) estimator tocontrol for endogeneity. Following the econometrics literature (for example,Smith and Blundell, 1985; Rivers and Vuong, 1988), this study employs a two-stage estimator. The estimation procedure is to model a continuous endogenousregressor as a linear function of the exogenous regressors and instrument.Predicted values from this regression are then used in the second stage discrete-choice model. This two-step method is suggested to be consistent (Adkins,2009). After reviewing available data, we find one variable to be an appropriateinstrument – regional density. This variable is measured by dividing the numberof firms distributed in one region by the floor-space of that region. It is highlycorrelated with the potentially endogenous regressors (that is, three proximitymeasures), but does not directly influence the dependent variable (that is,location choice). In addition, while estimating the discrete-choice IV model, weexclude one control variable, level of industrialization, from the model, becauseof its potential correlation with the instrument. The estimation results arereported in Table 6. The coefficient for proximity to knowledge is significantlynegative (�0.011), supporting Hypothesis 1. Consistent with Hypothesis 3, thecoefficient for proximity to labor has a significantly negative effect (�0.013).



Table 6: Results of the discrete-choice IV estimator

Variables Code Expected sign Model 1 Model 2 Model 3

Intercept INTERCEPT ? �4.311�� (�26.31) �5.461�� (�26.40) �4.036�� (�8.46)Proximity measures

Proximity to knowledge NK � �0.011�� (�6.13) — —

Proximity to market NM � — 0.006�� (3.31) —

Proximity to labor NLR � — — �0.013� (�1.92)


Intrinsic knowledge KNOW þ 0.000�� (11.31) 0.000�� (3.33) 0.000�� (4.63)Intrinsic market MARKET þ 0.343�� (16.01) 0.366�� (15.27) 0.225�� (2.60)Intrinsic labor LABOR þ 0.000�� (2.07) 0.000�� (2.29) 0.000�� (2.73)

Control variables

GDP GDP þ 0.000�� (25.58) 0.000�� (20.95) 0.000�� (11.78)Infrastructure INFRASINV þ �0.000�� (�21.85) �0.000�� (�20.74) �0.000 (�1.55)Coastal area COASTAL ? �0.065 (�1.22) 0.145�� (4.52) 0.445�� (6.73)Firm size SIZE þ 0.107�� (17.37) 0.106�� (17.37) 0.102�� (17.05)Firm performance PER_SALE þ �0.000�� (�3.87) �0.000�� (�3.89) �0.000�� (�3.95)R&D intensity RD ? 0.084� (1.70) 0.072 (1.43) 0.067 (1.34)

Sales growth GS þ 0.018�� (2.06) 0.018� (1.94) 0.018�� (2.06)Capital intensity CAPINTSIVE � �0.278�� (�4.98) �0.265�� (�4.83) �0.260�� (�4.82)

YuandShen

372

r2013Macm

illanPublish

ersLtd.1472-4782

Asia

nBusin

ess&

Managem

ent

Vol.12,3,351–380


Electronics INDU_elect ? 0.119�� (6.49) 0.118�� (6.52) 0.121�� (6.77)Food INDU_food ? �0.064 (�1.07) �0.082 (�1.38) �0.084 (�1.42)Service INDU_ service ? 0.018 (0.37) �0.001 (�0.02) 0.007 (0.14)

Year 2002 YEAR_2002 ? �0.116�� (�3.45) �0.06�� (�1.96) �0.108�� (�2.01)Year 2003 YEAR_2003 ? �0.3767�� (�10.59) �0.212�� (�6.56) �0.264�� (�5.41)Year 2004 YEAR_2004 ? �0.682�� (�17.13) �0.629�� (�15.98) �0.399�� (�11.08)Year 2005 YEAR_2005 ? �0.097� (�1.96) �0.725�� (�17.43) �1.002�� (�20.20)Year 2006 YEAR_2006 ? �0.399�� (�6.64) �1.473�� (�29.80) �0.7551�� (�12.41)Year 2007 YEAR_2007 ? �0.952�� (�12.95) �2.008�� (�34.44) �1.142�� (�12.40)Degree of freedom 21 21 21

Wald w2 6946.79 6834.53 5850.79

Prob4w2 0.0000 0.0000 0.0000

N 164 176 164 176 164 176

Wald test of exogeneity:

Degree of freedom 1 1 1

w2 2.15 2.43 0.58

Prob4w2 0.1426 0.1187 0.4473



Geographicproxim

ityandforeig

ndirect

investm

ent

373

r2013Macm

illanPublish

ersLtd.1472-4782

Asia

nBusin

ess&

Managem

ent

Vol.12,3,351–380

As for the coefficient for proximity to market, it is significantly positive (0.006).These results are all consistent with our prior analyses. In addition, the resultsfrom the Wald test of exogeneity show that the null hypothesis of exogeneity isnot rejected for each model. Taken together, there is no harmful impact ofendogeneity in the main results of this study.

Third, we lagged the explanatory variables by 1 year to avoid possible reversecausality. The reverse causality issue here refers to the likelihood that the locationchoice of foreign investors could lead to changes in a region’s proximity to theresources distributed in the country. As location choice is made in year t and thethree proximity variables are measured in year t�1, it is unlikely for foreigninvestors’ investment decisions in the current year to have significant impact onthe proximity measures of investment regions in the prior year. The econometrictheory supports that lagging is able to free the model from simultaneity bias(Wooldridge, 2002), and it has been widely used to reduce the problem of reversecausality (for example, Wiklund and Shepherd, 2003). Finally, following priorstudies, we employ the discrete-choice model with firm-level data to furtherminimize potential endogeneity (for example, Du et al, 2008). In the country-levelanalysis with aggregated FDI data, reverse causality is more likely to be a con-cern, as a large volume of FDI may influence the distribution of resources acrossregions. However, it is generally believed that an individual firm’s locationdecision is unlikely to have such a large impact on the proximity of particularregions to resources distributed across the entire country.

Conclusion and Discussion

Following reforms and implementation of an open-door policy, China’s eco-nomy has been developing rapidly, supporting the trend of foreign enterprisesinvesting in China. Much current research on this trend investigates locationchoice by enterprises investing in China. However, most studies focus only onthe influence of factors such as the characteristics, systems, culture and basicinfrastructure of the regions themselves, while ignoring how the environ-ment where particular regions are embedded influences enterprise decisions.Therefore, using network theory as a foundation, this study analyzes how theembedded spatial context influences the likelihood of an enterprise to invest ina region.

In general, this study treats China as one system and specifically viewsvarious regions to understand the distribution of knowledge, market and laborresources. Then, this study analyzes the level of proximity to knowledge, marketand labor resources in a given region, while further studying how theseproximity measures influence the preference of enterprises for investing in thatregion. Empirical results show that in combination with a region’s resource

Yu and Shen


endowments, measures of proximity also have great explanatory power forenterprises’ location choice. This suggests that geographic characteristics,particularly spatial location, influence the decisions of enterprises investing inChina. In terms of direction, they will most probably invest in regionsproximate to knowledge and labor resources and far from markets.

This outcome is closely related to the particular characteristics of Taiwaneseenterprises investing in China, among which a large proportion are manu-facturers. Moreover, it is consistent with general observations of FDI in Asia.In the past decade, export orientation has played a central role in Asianeconomic development (Gaulier et al, 2007). A significant proportion of FDI inAsia is characterized as export-oriented investments to create a production sitefor exporting (Baek and Okawa, 2001). In particular, Asian FDI is dominatedby flows from more developed countries with high-technology economies (suchas the United States and Japan) to emerging countries with medium-technologyeconomies (such as China and Vietnam) (Petri, 2012). As their FDI is intendedfor production in the host country, investors from developed countries areseeking labor-cost savings and favor locations more proximate to abundantlabor resources and further away from regions with higher wage rates (Wakasugi,2005). In addition, export-oriented foreign investors are more responsive to thetechnological gap in the host country (Mataloni Jr, 2011; Petri, 2012) and preferlocations more proximate to technological knowledge. Given the strong exportorientation of Taiwan investors in China, our findings can be described as atypical phenomenon in Asia.

This research contributes to theory in several ways. First, past researchfocused on analyzing location choice at country level, whereas this studyprovides empirical evidence for location choice on a sub-national level. Second,past research focused on analyzing the influence of geographic distance betweenhost and home countries on location choice (for example, Alstyne andBrynjolfsson, 2005), whereas this study finds that in investment locations withhigh heterogeneity, such as China, in addition to geographic distance betweena given location and other regions, the knowledge-, market- and labor-resourceendowments of proximate regions are important considerations in locationchoice. Third, past research has focused on the influence of economic andinstitutional factors, whereas this study suggests that geographic factorsare also an important factor, particularly the spatial context in which a givenregion is embedded. Therefore, if these factors are ignored, predictionsregarding location choice by enterprises might not be as accurate. Fourth, thisstudy supports network theory, as apart from the intrinsic characteristics ofa region, the position of a region within the network also influences enterprises’willingness to invest there. Finally, this study demonstrates Nachum et al (2008)suggestion that measures for proximity may be used on smaller geographicunits.



The outcomes have the following implications for management. First, thepresent study is a quantitative study of the geographic characteristics of Chineseregions, and the results can help foreign investors in China compare differences ingeographic characteristics of various regions and develop a better location-choicestrategy to increase their competitive advantage in China. Second, China is a verylarge country where the geographic environment and resource endowments ofvarious regions differ greatly, as well as the geographic distance between regions.Therefore, this study provides a model to assist enterprises in weighing the trade-off between geographic distance and resource endowments, which will helpenterprises establish firm-specific advantages through location choice.

Furthermore, the results show that beyond the intrinsic endowments of regions,the spatial context in which a specific region is embedded is also an importantfactor influencing a region’s potential for value creation, indicating that ifenterprises ignore this factor, they may not make an optimal location choice.Finally, this study finds that industries have differing concerns when they makelocation choice, and therefore the influence of geographic distance and resourcedistribution on location choice differs according to industry characteristics.

The research has limitations. As the study focuses only on Taiwanese-listedcompanies investing in China, the attributes of these Taiwanese enterprises mayhave influenced the results. Future research could focus on a different countryof origin for comparison and further analysis. In addition, as Taiwan law limitsinvestment in China in certain conditions, there may be a few Taiwaneseenterprises that reinvest through a third country, and do not report theseinvestments in their annual reports. However, as the sample includes all directinvestments in China by Taiwanese-listed companies, the missing informationshould not influence the results and findings.

Acknowledgements

The authors acknowledge constructive suggestions from two anonymous revie-wers and are grateful for comments by Mei-Chu Huang, Chun-Yuan ChristianUniversity, and Chun-Ju Liu, Tunghai University, on earlier drafts. The authorsalso acknowledge financial support from the National Science Council of Taiwan,Republic of China (NSC 96-2416-H-194-032-MY3). The authors alone are respon-sible for all limitations and errors that may relate to the study and the article.

About the Author

Sui-Hua Yu is currently an Associate Professor in the Department of Accountingat National Chung Hsing University, Taiwan. She received her PhD in

Yu and Shen


accounting from National Chengchi University, Taiwan. She has publishedin the areas of business and management accounting research. Her researchinterests include foreign direct investment in China, intangible assets andinnovation management.

Chen Feng Shen is currently a Senior Associate at Price Waterhouse CoopersTaiwan. He received his MA in accounting and information technology fromNational Chung Cheng University, Taiwan. His research interests center oncost accounting and foreign direct investment in China.

References

Adkins, L.C. (2009) An instrumental variables probit estimator using gretl. In: Econometrics with

Gretl: Proceedings of the Gretl Conference 2009. Bilbao, Spain: UPV/EHU, Universidad del Pais

Vasco, pp. 59–74.

Alcacer, J. and Chung, W. (2007) Location strategies and knowledge spillovers. Management

Science 53(5): 760–776.

Alfaro, L., Kalemli-Ozcan, S. and Volosovych, V. (2008) Why does not capital flow from rich to

poor countries? An empirical investigation. Review of Economics and Statistics 90(2): 347–368.

Allen, L. and Pantzalis, C. (1996) Valuation of the operating flexibility of multinational corpora-

tions. Journal of International Business Studies 27(4): 633–653.

Allison, P.D. (1977) Testing for interaction in multiple regression. American Journal of Sociology

83(1): 144–153.

Almeida, P. and Kogut, B. (1999) Localization of knowledge and the mobility of engineers in

regional networks. Management Science 45(7): 905–917.

Alstyne, M.V. and Brynjolfsson, E. (2005) Global village or cyberbalkans? Modeling and measuring

the integration of electronic communities. Management Science 51(6): 851–868.

Attfield, C.L.F., Cannon, E.S., Demery, D. and Duck, N.W. (2000) Economic growth and

geographic proximity. Economics Letters 68(1): 109–112.

Baek, I.M. and Okawa, T. (2001) Foreign exchange rates and Japanese foreign direct investment in

Asia. Journal of Economics and Business 53(1): 69–84.

Beamish, P.W. and Wang, H.Y. (1989) Investing in China via joint ventures. Management

International Review 29(1): 57–64.

Belderbos, R. and Carree, M. (2002) The location of Japanese investments in China: Agglomeration

effects, keiretsu and firm heterogeneity. Journal of the Japanese and International Economies

16(2): 194–211.

Blainey, G. (2001) The Tyranny of Distance: How Distance Shaped Australia’s History. Sydney,

Australia: Macmillan.

Blonigen, B.A., Davies, R.B., Waddell, G.R. and Naughton, H.T. (2007) FDI in space:

Spatial autoregressive relationships in foreign direct investment. European Economic Review

51(5): 1303–1325.

Borgatti, S.P. and Everett, M.G. (1992) Notions of position in social network analysis. Sociological

Methodology 22(1): 1–35.

Carr, D.L., Markusen, J.R. and Maskus, K.E. (2001) Estimating the knowledge-capital model of

the multinational enterprise. American Economic Review 91(3): 693–708.

Chadee, D.D., Qiu, F. and Rose, E.L. (2003) FDI location at the sub-national level: A study of

EJVs in China. Journal of Business Research 56(10): 835–845.



Chan, C.M., Makino, S. and Isobe, T. (2010) Does sub-national region matter? Foreign affiliate

performance in the United States and China. Strategic Management Journal 31(11): 1226–1243.

Cheng, S. and Stough, R.R. (2006) Location decisions of Japanese new manufacturing plants in

China: A discrete-choice analysis. Annals of Regional Science 40(2): 369–387.

China State Statistics Bureau. (1979–2000) China Foreign Economic Statistical Yearbook (Series).

Beijing: China Statistics Press.

Choi, S.R., Tschoegl, A.E. and Yu, C.M. (1986) Banks and the world’s major financial centers:

1970–1980. Review of World Economics 122(1): 48–63.

Chung, W. and Alcacer, J. (2002) Knowledge-seeking and location choice of foreign direct

investment in the United States. Management Science 48(12): 1534–1555.

Chung, W. and Song, J. (2004) Sequential investment, firm motivation and agglomeration of

Japanese electronics firms in the United States. Journal of Economics and Management Strategy

13(3): 539–560.

Craig, S.C., Douglas, S.P. and Grein, A. (1992) Patterns of convergence and divergence among

industrialized nations: 1960–1988. Journal of International Business Studies 23(4): 773–787.

Das, D.K. (2007) Foreign direct investment in China: Its impact on the neighboring Asian

economies. Asian Business & Management 6(3): 285–301.

Davidson, H.W. (1980) The location of foreign direct investment activity: Country characteristics

and experience effects. Journal of International Business Studies 11(2): 9–22.

Du, J., Lu, Y. and Tao, Z. (2008) Economic institutions and FDI location choice: Evidence fromUS

multinationals in China. Journal of Comparative Economics 36(3): 412–429.

Dunning, J.H. (2000) The eclectic paradigm as an envelope for economic and business theories of

MNE activity. International Business Review 9(2): 163–190.

Economist (2005) Boom and bust at sea: How long can the good times last for the shipping industry?

Economist 18(8): 47–48.

Enright, M.J. and Scott, E.E. (2000) The RHQ question. Business Asia 32(25): 1–4.

Galan, F.I. and Gonzalez-Benito, F. (2001) Determinant factors of foreign direct investment: Some

empirical evidence. European Business Review 13(5): 269–278.

Gao, T. (2005) Labor quality and the location of foreign direct investment: Evidence from China.

China Economic Review 16(3): 274–292.

Gaspar, J. and Glaeser, E.I. (1998) Information technology and the future of cities. Journal of Urban

Economics 43(1): 136–156.

Gaulier, G., Lemoine, F. and Unal-Kesenci, D. (2007) China’s integration in East Asia: Production-

sharing, FDI and high-tech trade. Economic Change and Restructuring 40(1–2): 27–63.

Grant, R.M. (1987) Multinationality and performance among British manufacturing companies.

Journal of International Business Studies 18(3): 79–89.

Greene, W.H. (2008) Econometric Analysis, 6th edn. Upper Saddle River, NJ: Prentice-Hall.

Hair, J., Anderson, R., Tatham, R. and Black, W. (1998)Multivariate Data Analysis. Upper Saddle

River, NJ: Prentice-Hall.

Herander, M.G. and Saavedra, L.A. (2005) Exports and the structure of immigrant-based

networks: The role of geographic proximity. Review of Economics and Statistics 87(2): 323–335.

Hill, C.W.L. and Jones, G.R. (1998) Strategic Management Theory. Boston, MA: Houghton Mifflin.

Hoffman, S.D. and Duncan, G.J. (1988) Multinomial and conditional logit discrete-choice models

in demography. Demography 25(3): 415–427.

Jensen, P.D.Ø. and Pedersen, T. (2011) The economic geography of off-shoring: The fit between

activities and local context. Journal of Management Studies 48(2): 352–372.

Kang, S.J. and Lee, H.S. (2007) The determinants of location choice of South Korean FDI in China.

Japan and the World Economy 19(4): 441–460.

Kojima, K. (1973) A macroeconomic approach to foreign direct investment.Hitotsubashi Journal of

Economics 4(1): 1–21.

Yu and Shen


Kogut, B. and Zander, U. (1992) Knowledge of the firm, combinative capabilities and replication of

technology. Organization Science 3(3): 383–379.

Krueger, A.O. (1968) Factor endowments and per capita income differences among countries.

Economic Journal 78(311): 641–659.

Laumann, E.O. (1978) Community structure as inter-organizational linkages. Annual Review of

Sociology 4(1): 455–484.

Leamer, E. and Storper, M. (2001) The economic geography of the Internet age. Journal of

International Business Studies 32(4): 641–665.

Liebeskind, J.P. (1996) Knowledge, strategy and the theory of the firm. Strategic Management

Journal 17(Winter special issue): 93–107.

Lin, N. (1999) Building a network theory of social capital. Connections 22(1): 28–51.

Makino, S., Lau, C.M. and Yeh, R.S. (2002) Asset-exploitation versus asset-seeking: Implications

for location choice of foreign direct investment from newly industrialized economies. Journal of

International Business Studies 33(3): 403–421.

Mataloni, Jr R.J. (2011) The structure of location choice for new U.S. manufacturing investments in

Asia-Pacific. Journal of World Business 46(2): 154–165.

McDermott, P. and Taylor, M. (1982) Industrial Organisation and Location. Cambridge, UK:

Cambridge University Press.

McFadden, D. (1973) Conditional logit analysis of qualitative choice behavior. In: P. Zarembka

(ed.) Frontiers in Econometrics. New York: Academic Press, pp. 105–142.

Meyer, K.E. and Nguyen, H.V. (2005) Foreign investment strategies and sub-national institutions in

emerging markets: Evidence from Vietnam. Journal of Management Studies 42(1): 63–94.

Ministry of Economic Affairs of Taiwan. (2007) Evaluative Report on Influences to Cross-Strait

Trade and Investment. Taipei, Taiwan: Investment Commission, MOEA.

Mosteller, F. and Tukey, J.W. (1977) Data Analysis and Regression: A Second Course in Statistics.

Reading, MA: Addison-Wesley.

Murdoch, J. (2000) Networks: A new paradigm of rural development? Journal of Rural Studies

16(4): 407–419.

Nachum, L. and Zaheer, S. (2005) The persistence of distance? The impact of technology on MNE

motivations for foreign investment. Strategic Management Journal 26(8): 747–768.

Nachum, L., Zaheer, S. and Gross, S. (2008) Does it matter where countries are? Proximity to know-

ledge, markets and resources, andMNE location choices.Management Science 54(7): 1252–1265.

Petri, P.A. (2012) The determinants of bilateral FDI: Is Asia different? Journal of Asian Economics

23(3): 201–209.

Redding, S. and Schott, P.K. (2003) Distance, skill-deepening and development: Will peripheral

countries ever get rich? Journal of Development Economics 72(2): 515–541.

Rivers, D. and Vuong, Q.H. (1988) Limited information estimators and exogeneity tests for

simultaneous probit models. Journal of Economics 39(3): 347–366.

Salomon, R. and Shaver, J.M. (2005) Export and domestic sales: Their interrelationship and

determinants. Strategic Management Journal 26(9): 855–871.

Smith, D.A. and Timberlake, M. (2001) World city networks and hierarchies 1979–1999:

An empirical analysis of global air travel links. American Behavioral Scientist 44(10):

1656–1677.

Smith, R.J. and Blundell, R.W. (1985) An exogeneity test for a simultaneous equation Tobit model

with an application to labor supply. Econometrica 54(3): 679–685.

Smith, K.W. and Sasaki, M.S. (1979) Decreasing multicollinearity: A method for models with

multiplicative functions. Sociological Methods and Research 8(1): 35–56.

So, Y. and Kuhfeld, W.F. (1995) Multinomial logit models. In: SAS Conference Proceedings: SAS

User Group International 20 (SUGI 20), http://www.dina.com.cn/news/UploadFiles/t9.pdf,

accessed 17 January 2013.



Sohn, J. (2004) Do birds of a feather flock together? Economic linkage and geographic proximity.

Annals of Regional Science 38(1): 47–73.

Spencer, J. (2003) Firms’ knowledge-sharing strategies in the global innovation system: Empirical

evidence from the flat-panel display industry. Strategic Management Journal 24(3): 217–233.

Storper, M.A. and Venables, J. (2004) Buzz: Face-to-face contact and the urban economy. Journal

of Economic Geography 4(4): 351–370.

Strom, P. and Yoshino, N. (2009) Japanese financial service firms in East and Southeast Asia:

Location pattern and strategic response in changing economic conditions. Asian Business &

Management 8(1): 33–58.

Sun, Q., Tong, W. and Yu, Q. (2002) Determinants of foreign direct investment across China.

Journal of International Money and Finance 21(1): 79–113.

Tsang, E.K. and Yip, P.L. (2007) Economic distance and survival of foreign direct investment.

Academy of Management Journal 50(5): 1156–1168.

UNCTAD. (2005) UNCTAD Handbook of Statistics. New York/Geneva: UN.

Wakasugi, R. (2005) The effects of Chinese regional conditions on the location choice of Japanese

affiliates. Japanese Economic Review 56(4): 390–407.

Wiklund, J. and Shepherd, D. (2003) Knowledge-based resources, entrepreneurial orientation

and the performance of small and medium-sized businesses. Strategic Management Journal

24(13): 1307–1314.

Wilson, J.D. (1991) Tax competition with interregional differences in factor endowments. Regional

Science and Urban Economics 21(3): 423–451.

Wooldridge, J.M. (2002) Econometric Analysis of Cross Section and Panel Data. Cambridge, MA:

MIT Press.

Zhao, H. and Zhu, G. (2000) Location factors and country-of-origin differences: An empirical

analysis of FDI in China. Multinational Business Review 8(1): 60–73.

Zhou, C., Delios, A. and Yang, J.Y. (2002) Locational determinants of Japanese foreign direct

investment in China. Asia Pacific Journal of Management 19(1): 63–86.

Yu and Shen


Reproduced with permission of the copyright owner. Further reproduction prohibited withoutpermission.

Documents

Geographic Proximity and Location Choice of Foreign Direct Investment in China