analysing china's foreign direct investment in manufacturing from a high–low technology...

Emerging Markets Review 21 (2014) 82–95

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Emerging Markets Review

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Analysing China's foreign direct investment
in manufacturing from a high–lowtechnology perspective
Kelly Liu 1, Kevin Daly⁎, Maria Estela Varua 2

School of Business, University of Western Sydney, Australia

a r t i c l e i n f o

⁎ Corresponding author at: School of Business, Uni203546; fax: +61246 203769.

E-mail addresses: [email protected] (K. Liu), k.dal1 Tel.: +61 246 203250; fax: +61246 203769.2 Tel.: +61 246 203656; fax: +61246 2039105.

http://dx.doi.org/10.1016/j.ememar.2014.08.0031566-0141/© 2014 Elsevier B.V. All rights reserved.

a b s t r a c t

Article history:Received 13 February 2014Received in revised form 9 August 2014Accepted 12 August 2014Available online 20 August 2014

Since China opened its economy to foreign investment in 1979, it hasbecome the second largest foreign direct investment (FDI) destination inthe world after the USA. Over the past three decades, the manufacturingsector has dominated China's FDI inflow, however, whenmanufacturingactivity is bifurcated into low and high technology classes, it becomesevident that China is in a transition stagemoving from FDI in traditionallow-tech activity to a high-tech manufacturing environment. This paperattempts to analyse the key determinants of FDI inflow across lowand high technology manufacturing industry across four geographicalregions of China. In the paper we empirically investigate the determi-nants of FDI high–low tech inflow by market size, labour cost, labourquality, and government spending on human capita.

© 2014 Elsevier B.V. All rights reserved.

JEL classification:E22F21G11

Keywords:ChinaFDIManufacturing industryLow–high technology

1. Introduction

Since China reformed and adopted its ‘open up’policies in 1979, inward foreign direct investment(FDI) increased dramatically, Fig. 1 shows that in2012 China had attracted foreign direct invest-ment worth USD 111.7 billion, which increasedfrom US $0.057 billion in 1980 to $20 billion in1993 and to $53.5 billion in 2003. FDI inflow toChina has attracted a great deal of interest within

versity of West

[email protected]

both academia and the policy-making arena andtoday ranks as one of the major researched issuesin emerging markets Kearney (2012). FDI is widelyrecognized as the major driving force behindChina's phenomenal economic growth. Severalstudies capturing the spill-over effects of thisgrowth are available; Whalley and Xin (2010)examine the effects on productivity, Wei and Liu(2006) analyse the contributions to efficiency,

ern Sydney, Campbelltown, NSW 2570. Australia. Tel.: +61 246

(K. Daly), [email protected] (M.E. Varua).

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http://dx.doi.org/10.1016/j.ememar.2014.08.003

mailto:[email protected]



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http://dx.doi.org/10.1016/j.ememar.2014.08.003

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Fig. 1. Annual FDI inflows to China, 1982–2012 (USD millions).Source: MOFCOM website: www.fdi.gove.cn.

83K. Liu et al. / Emerging Markets Review 21 (2014) 82–95

Liu's (2008) study provides an account of technological innovations while Yao's (2006) researchinvestigates the effects of FDI on management know-how. More general FDI studies by Groh and Wich(2012) provide evidence as to why FDI flows are concentrated in advanced economies, which still accountfor 75% of inward FDI while Jadhav (2012) explores the role of economic, institutional and political factorsin attracting foreign direct investment to BRICS (Brazil, Russia, India, China & South Africa) economies byproviding comparative weightage for these factors in attracting FDI.

By contrast few recent studies have researched the economic effects of the regional distribution of FDIacross China. Location factors are pronounced in China, where there are wide regional differences insocioeconomic development and many internal barriers to resource mobility. Previous studies by Weiet al. (2008) and Pan and Tse (2000) indicated that specific regions in China (typically along the coast) arepreferable not only because they are prioritized by the central government of the People's Republic ofChina (PRC) but also because a majority of these regions have historically been commercial and industrialcentres. The government of the PRC provides incentives for FDI in areas such as special economic zonesand open cities. To contribute to the literature this study investigates the economic effects of the unevenregional distribution of FDI across China. In particular we investigate the disparities of FDI allocation aswell as the comparative advantages of China's coastal and non-coastal regions viz the northeast region,central region and western region. Our objective in this study is to highlight the unbalanced distribution ofFDI regionally across China and discuss the consequence of the latter for the economic development of theless developed hinterland. In particular our results suggest that a more balanced distribution of FDI basedon exploiting regional comparative advantage has the potential to reduce the geographic unbalancednature of FDI with the consequences of reducing China's regional development disparities which in turncould benefit both the whole of China and foreign investors. This research identifies the regionaldeterminants of FDI inflows according to high and low tech intensive activities. In particular our researchsuggests that a policy of matching China's regional resources particularly labour to the demands of FDIdesignated by high–low technologies would realise a more balanced economic development of Chinaproviding employment opportunities across China's newly created hinterland of urban centres. Thisreallocation of FDI would in turn reduce the migration and concentration of labour particularly low orunskilled labour across the already densely populated and congested coastal or eastern regions of China.

This paper focuses on the regional determinants of China's manufacturing industry by performing abifurcation of manufacturing into high and low technology categories. Our study confirms that inward FDIflows to China are predominately concentrated within the manufacturing sector to the extent ofapproximately 60% of total utilised FDI inflow. Our research objective is to assess the determinants of thisFDI inflow along the lines of assessing the regional distribution of FDI inflows across both high and lowtechnologies.

image of Fig.�1

http://www.fdi.gove.cn

84 K. Liu et al. / Emerging Markets Review 21 (2014) 82–95

China's economic development portrays similar characteristics to other developing countries such asBrazil, still in the early stages of industrialization. In these economies FDI in manufacturing plays animportant role in advancing their economies along the path toward development. Studies by Yang (2010)and OECD (2009) provide strong evidence to support the links between FDI and these economies'industrial capacity, technical advancement, and export competitiveness. FDI provides access to newtechnology, capital, research & development facilities and management know-how for a host locationwhich in turn increases the rate of economic development. However, some scholars (Wei et al., 2009),argue that the uneven distribution of FDI across China is responsible for widening the gap in economicdevelopment experienced between coastal and non-coastal regions. Here, Sun and Chai (1998) and Linet al. (2011) argue that the positive spillover effects of FDI depend heavily on a host region's absorptiveability. Kalotay and Sulstarova (2010) note that “absorptive capacity” denotes the maximum amount ofFDI that a host economy can assimilate or integrate into the economy in a meaningful manner. In turn,what appears to be the driver of regional disparity in economic development are location specific factorswhich determine the kind of FDI attracted to a particular region.

Table 1 indicates that in 2010 some 16% of inward FDI in China took place in Central andWestern Chinacompared to 84% in the Eastern Region, when one considers that there are 300 million people residing inthe western region of China, this alone provides a significant domestic market for MNCs. However theallocation of realised FDI on a per-capita basis for the eastern Region significantly dwarfs both the centraland western regions.

The imperative question is what kind of FDI will most likely pour into China's hinterland by using thecomparative advantages of the area. We will employ the bifurcation of technology into high–lowtechnology as a means to analyse China's FDI location determinants. We are however mindful that thepromotion of FDI to China's hinterland is ultimately decided by different stakeholders. Here the PRCcentral and local governments play the role of facilitators of FDI. However, there have been enormousvariations in FDI policies from central and local governments across both provinces and time, with atendency to increase the level of competition in the area of incentives provided to investors at provinciallevel, these strategies in turn can hinder balanced development unless they are centrally managed andcoordinated along the lines advocated by this paper. On the other hand, MNCs like Ericsson and Nokiahave become major investors in China's hinterland regions such as Chongquing. No doubt other MNCs willfollow by taking advantage of China's hinterland natural resources, potential market and relatively lowerlabour costs.

The implications for policy makers is that China should continue to attract FDI, while each regionshould find the type of FDI which best suits their comparative advantage, i.e. the coastal region with highlytrained labour and better industrial development should focus on attracting FDI in high technologymanufacturing industries, at the same time, Western and Central regions rich in natural resource andunskilled labour, should focus on low-technology manufacturing. Attracting FDI which is suitable to localfactor conditions provides a long term sustainable path toward development for all regions, getting thebalance right between FDI and locally available factor inputs is currently China's most pressingdevelopment issue. Unless planned and balanced the allocation of FDI will widen the development gapand economic inequality between China's coastal and non-coastal regions.

To determine a region's appropriate technology we examine the location determinants for both high andlow-tech manufacturing industries across China's four regions, these determinants include the sizeof economy, labour cost, labour quality, physical and telecommunication infrastructures. The structure of the

Table 1FDI inflow to China's regions 2010 (USD billions).

No. of projects % Utilised FDI %

Eastern region 22,992 83.9 90 78.3Central region 3,056 11.1 7 6.0Western region 1,358 5.0 9 7.9Government 14 0.1 9 7.8TOTAL 27,420 100.0 115 100.00

Source: MOFCOM website: www.fdi.gov.cn.

http://www.fdi.gov.cn


paper is as follows: Section 2 describes the transformation and regional distribution of FDI in manufacturingacross China's four major geographic regions. Section 3 reviews the previous studies on location determinantsof FDIwhile Section 4 presents the determinants of FDI inflows. Our analytical framework andempirical resultsare presented in Sections 5 and6, respectively, finally in Section 7we provide a summary and conclusion of thepapers contribution. Due to the non-availability of consistent data covering the variables in our models laterthan 2008 our estimated results cover the most current available data up to 2008.

2. Transformation and regional distribution of FDI inflows in both high and low-technologymanufacturing industries

The OECD classifies all manufacturing industries3 into 4 categories based on technology intensity, namely:high technology category, medium-high technology category, medium-low technology category and lowtechnology category. Technology intensity is measured by two indicators: R&D expenditure divided by valueadded andR&Dexpenditure dividedbyproduction. Industries ranked inhigher technology category havehigherresearch intensity indicators compared to industries in lower category. For convenience, this paper classifiesmanufacturing industries in China into only two categories, namely: low (low and medium-low technology)and high (medium-high and high technology) categories. Table 2 indicates that the high-technologymanufacturing industries include pharmaceutical, medical chemicals and botanical products, telecommunica-tion, office machinery, chemical and chemical products, machinery and equipment, electrical machinery andtransport equipment. By contrast the low-technology manufacturing industries include food and beveragesproduction, tobacco production, textiles, wearing apparel, paper and paper products, coke and petroleumproduction, non-metallic mineral production, basic metal production and fabricated metal production.

Fig. 2 illustrates the utilised amount of FDI inflows for both high and low-techmanufacturing industries inselected years. The data confirms that FDI in both categories expanded dramatically, especially for high-techindustries. The total utilised FDI in low-tech categories grew from USD 30.3 billion in 2001 to USD 106.1billion in 2008, an increase of 250% while the high-tech category grew from USD 40.2 billion to USD 208.3billion, equivalent to an increase of 418%. Although China still has a strong comparative advantage inlow-tech, labour-intensive activities owing to its abundant labour supply, it also has a comparative advantagein high-tech activities due to faster economic growth, huge improvements in human capital development andtechnology and spill-overs from previous FDI (Shaukat and Guo, 2005; Chen, 2011). The share of FDIinvestment across high and low-tech categories has however changed significantly over the relatively shorttime frame from 2001 to 2008. For example Table 3 indicates that the share of utilised FDI in high-techindustries reached 66.3% of the total national utilised manufacturing FDI in 2008 (Column 4), an increase of9.2% compared to that in 2001, while the share of FDI invested in low-tech category has continued to fall over2001–08, falling from 42.9% (Column 2 low-tech) in 2001 to 33.7% (Column 4) in 2008.

The expansion of investment across high-tech manufacturing industries (Column 5, Table 3) was moresignificant in categories; radio, television and communication equipment industry,machinery and equipmentand transport equipment industries, accounting for 23% of the total utilised FDI in manufacturing industry.Among low-tech manufacturing industries, the manufacturing of basic metal increased from 3.6% share oftotal FDI in 2001 to 5% by 2008. The tobacco industry is the only industry where foreign invested capitaldecreased falling by 57% over the period from 2001 to 2008.

FDI inflows to China in both high-tech and low-tech manufacturing industries have had a stronglocation preference, with a high concentration across the coastal region, with little going to the northeast,central and western regions. Fig. 3a and 3b provides clear evidence of the uneven regional distribution ofutilised FDI in both high and low-technology manufacturing categories over the period of 2001–2008. Forhigh-tech industry activities, the coastal region received 88% of total FDI, while the northeast, central andwestern regions only accounted for 5, 4 and 3%, respectively (Fig. 3a). Compared with high-tech industryclassifications, the share of utilised FDI in low-tech activities situated across China's coastal regions wasonly slightly smaller, accounting for 85%; while the central region accounted for 6% of the national FDItotal for the entire manufacturing industry (Fig. 3b). The location preference is a result of a variety of

3 All industries are classified based on International Standard Industrial Classification of all economic activities (ISIC) Rev.3. Formore details explanation are provided by OECD Directorate for Science, Technology and Industry Economic Analysis and StatisticsDivision.

Table 2Classifications of manufacturing industries by high and low technology categorya.

High-technology category Manufacture of pharmaceuticals, medical chemicals and botanical productsManufacture of radio, television and communication equipment and apparatusManufacture of office, accounting and computing machineryManufacture of chemicals, chemical products and fibresManufacture of machinery and equipment n.e.c.Manufacture of electrical machinery and apparatus n.e.c.Manufacture of transport equipment

Low-technology category Manufacture of food products and beveragesManufacture of tobacco productsManufacture of textilesManufacture of wearing apparel; dressing and footwearManufacture of paper and paper productsManufacture of coke, refined petroleum products and nuclear fuelManufacture of other non-metallic mineral productsManufacture of basic metalsManufacture of fabricated metal products, except machinery and equipment

Source: OECD and National Bureau of Statistics of China. Note: n.e.c. indicates not elsewhere classified.a With the continuation of ‘open up’ policy China adopted the ‘Industrial Classification for National Economic Activities (GB/

T475-2011) in 2011 thereby providing a means to make comparisons against the International Standards of Industrial Classification(ISIC). This classification mainly follows the same principle, methods and industrial classification system as ISIC Rev.3, but withadjustment for some classes. For instance, the manufacturing industry has 30 divisions in GB/T44754-2011, but ISIC Rev.3 has 23divisions. The authors construct corresponding classifications and translate the Chinese manufacturing industry classification intoISIC Rev.3.


factors, including FDI policies and regional economic development. The coastal region received privilegedstatus which in turn brought forward comparative advantages in infrastructure, capital, technology andmanagement skills and is responsible for leading China in high technology and high value-addedmanufacturing activity. By comparison the three non-coastal regions have an abundance of semi-skilledlabour, but inadequate capital, infrastructure and technology.

3. Studies of location determinants of FDI in China

There exists a significant background literature researching the determinants of foreign capitalinvestment in China, such as Dees (1998), Sun et al. (2002), Hou (2002), Cheng and Kwan (2000), Gao(2005) and Groh andWich (2012). However, little research has been undertaken on identifying the spatialdistribution of FDI in the case of developing countries, apart from Andreosso-O'Callaghan and Wei (2002)To our knowledge there is no study that researches the distribution of FDI by identifying the determinantswhich drive FDI in manufacturing on the basis of their technological characteristics namely high–lowtechnology. In the case of China, this is an important research area as FDI inflow into China is not only

Source: China Industrial Economic Statistical Yearbook and authors’ own calculation

Fig. 2. Utilised FDI inflow in both high and low-tech manufacturing industry in China, 2005,2005 & 2008 (USD billion).Source: China Industrial Economic Statistical Yearbook and authors' own calculation.

image of Fig.�2

Table 3Amount and shares of utilised FDI in manufacturing by industries (USD billion & %).

2001 2008 Change in dollaramount

Amount Share Amount Share

(1) (2) (3) (4) (5)

High-technology categoryPharmaceuticals, medicinal chemicals and botanical products 1.7 2.5 7.2 2.3 315.9Radio, television and communication equipment and apparatus 12.2 17.3 72.2 23.0 493.1Office, accounting and computing machinery 1.4 2.0 6.4 2.0 351.3Chemicals, chemical products and fibres 7.2 10.2 33.9 10.8 371.7Machinery and equipment n.e.c. 5.7 8.1 33.1 10.5 479.7Electrical machinery and apparatus n.e.c. 6.3 8.9 26.0 8.3 314.5Transport equipment 5.7 8.2 29.5 9.4 413.9Sub-total 40.2 57.1 208.3 66.3 417.7

Low-technology categoryFood products and beverages 9.3 13.2 16.7 5.3 80.1Tobacco products 0.0 0.1 0.0 0.0 −56.7Textiles 5.1 7.3 19.1 6.1 273.5Wearing apparel; dressing and footwear – 10.2 3.2 –

Paper and paper products 3.1 4.5 12.3 3.9 290.1Coke, refined petroleum products and nuclear fuel 0.9 1.3 3.3 1.1 269.7Non-metallic mineral products 5.0 7.2 15.9 5.0 214.4Basic metals 2.6 3.6 15.9 5.0 517.3Fabricated metal products, except machinery and equipment 4.2 5.9 12.6 4.0 203.8Sub-total 30.3 42.9 106.1 33.7 250.2

Source: China Industrial Statistical Yearbook and authors own calculation.


transforming from a traditional labour-intensive, low-technology manufacturing sector to high-tech,capital-intensive manufacturing sectors but also the unbalanced nature of China's FDI distribution iscreating enormous regional disparities in terms of China's economic development.

Previous research has identified a number of determinants of foreign investment; in particular foreigninvestors choose to invest in a particular location based on the availability of the following factors; percapita income, agglomeration, labour quality, labour cost, transportation network and expenditures. Wangand Swain (1997) found that FDI in manufacturing sectors is positively related to China's GDP, GDPgrowth, wages and trade barriers, but negatively related to interest rate and exchange rate for the periodof 1978–1992. Cheng and Kwan (2000) report that good infrastructure positively influences the locationdecision of foreign investors in China, Sun et al. (2002), similarly report that good infrastructure hada positive effect on FDI inflow into China in the period of 1986–1998. Interestingly, Mudambi andMudambi's (2005) study shows that generally, infrastructure support does not always attract significantFDI, especially into the high-technology sector. Zhang and Yuk (1998) examine the determinants of FDI inmanufacturing industry from Hong Kong's investors in Guangdong Province in China by comparingcapital-intensive and labour-intensive FDI. They found that labour-intensive industries are attracted tomore export-oriented FDI, while capital-intensive industries' FDI is more domestic market-oriented. Theyalso found that the most important determinants for FDI location are cheap labour and land, stablepolitical environment, government incentive policies, good infrastructure, absence of language barriersand geographical proximity between Guangdong Province and Hong Kong. When NG and Tuan (2006)tested the determinants of FDI in manufacturing in Guangdong Province by employing firm-level data,they found that institutional and agglomeration factors both have positive effects on attracting FDI. Theavailability of human capital is a locational advantage for foreign investors since it implies the existence ofa soft infrastructure. Industries that are more technology-intensive require qualified workers, for exampleTaiwanese IT firms moved to the Yangtze River Delta to manufacture high value-added products such asnotebooks and displays, motivated in part by the local availability of qualified IT personnel.

Based on the theory of comparative advantage, Qiu (2003) constructs a model where the host country'scomparative advantage is identified as the major attraction for FDI. By employing this model, he explainsthat prominent FDI in China's labour-intensive sector is attracted by the large scale supply of labour and its

Source: China Industrial Statistical Yearbook, 2002-2009

Fig. 3. Regional distribution of utilised FDI in (a) high and (b) low-tech manufacturing industries, 2001–2008 (%).Source: China Industrial Statistical Yearbook, 2002–2009.


relative low cost. Milner and Pentecost (2006) also confirmed this finding when they tested US foreigndirect investment in the UK's manufacturing sector, suggesting that comparative advantage in UK in termsof unskilled labour is an important factor attracting U.S. FDI. Furthermore, Dunning (2009) also arguedthat when foreign affiliates became more embedded in the host countries, this led firms to engage in moreinnovatory activities.

4. Determinant factors of FDI in manufacturing industry in both low and high-techmanufacturing industries

The theoretical foundations used in this paper are derived fromDunning's (1988) OLI or eclectic paradigm,namely ownership advantage, location advantage and internalisation advantage. According to this paradigm,to offset the disadvantages of setting up foreign production operations, the MNCs must have an ownershipadvantage; this may take the form of innovatory capacity, trademarks, reputation, or other assets. Thelocation advantages of a host country arise from better factor quality, transport development, endowments,government policies and low cost. The internalisation advantage refers to the firm's location advantageallowing them to manufacture internally rather than outsourcing. In this paper, the ownership andinternalisation advantages are taken as given, then, in linewith the OLI paradigm, the level of inward FDI to aparticular destination might be explained in terms of different location characteristics.

Based on OLI paradigm and empirical studies previously discussed, eight potentially importantdeterminants of FDI inflows across four regions in China were identified. These variables as discussed in

image of Fig.�3


Table 4 are: market size, absorptive capacity, and supply of unskilled labour, labour cost, physicalinfrastructure, telecommunication, government incentives and agglomeration.

Market size ought to have a positive impact as a regions attraction for foreign capital in both high andlow-tech manufacturing industries, because this factor directly affects the expected revenue from thedomesticmarket. In fact, one of themotivations for setting up foreign affiliates in host locations is to supply thelocal market. This kind of investmentmaybe be undertaken to exploit newmarkets, thus, market size, marketgrowth rate, and degree of development of host location are very important draw factors for FDI. The broadassumption is that the larger the host market, the faster the rate of economic growth, and the more attractivethe region is to locate FDI. In this paper, GDP per capita is used to measure market size (GDPPC, USD dollar).

The effectiveness of technology transformation from home country to host location depends on thehosting region's absorptive ability (Lin et al., 2011). The host country must have a moderate technologicalgap with MNEs in order to attract MNEs to establish foreign subsidiaries and transfer advancedtechnology. If the technological gap is too wide, hosts may not have the ability to adopt the technologyassociated with MNEs leading to a potential reduction in total productivity (Kinoshita, 2001). Thus, weargue that governments with higher expenditure on R&D can potentially increase its regional capacity toabsorb more advanced foreign technology, which in turn, attracts more high-tech manufacturing FDI. Thispaper employs government spending on R&D to proxy absorption capacity (RESEARCH, USD million).

Supply of unskilled labour is a crucial factor in attracting FDI, especially to low-tech manufacturingindustries. Initially, FDI in China has been driven by comparative advantage in labour-intensive productionlocated in the non-coastal regions due to the large amount of unskilled and cheap labour. However, thesignificant disparity in the regional distribution of foreign direct investment has increased the wagedisparity across regions, for instance Yu, et al. (2011) estimated that the averagewage in coastal regions is onaverage some 25% higher compared to all three non-coastal regions, thus the low-tech, labour-intensivemanufacturing production is shifting from coastal region to inland regions, the large amount of unskilledlabour in those regions appears to be an essential factor for low-tech manufacturing FDI, thus, unskilledlabour should have a positive relationship with FDI (UNSKILL, %).

Labour cost, as measured by average wage paid to employees in the manufacturing industry isexpected to have both a positive and negative effect on FDI. On one hand, higher wages mean lower profit;reducing the attractiveness as a location for FDI. On the other hand, in recent years, China attracts foreigninvestment in more technology-intensive industries where multinationals are paying premiums to theirworkers, this is because multinational firms want to hire quality workers, where labour costs also reflectlabour quality, and thus the sign on thewage variable can be both positive and negative (WAGE, USD dollar).

Physical infrastructure and telecommunication development is another major determinant of FDI.Adequate and effective transportation impacts a firm's costs and revenues and hence a firms location

Table 4Determinants of FDI inflow.

Independent variable Proxy variable (variable name) Expected sign

Market size GDP per capita (GDPPC) +Absorptive capacity Government spending on research and

development (RESEARCH)+ (High-tech manufacturing only)

Labour cost Annual average wage paid in manufacturingindustry (WAGE)

+/−

Supply of unskilled labour Percentage of total labour force with primarydegree of below (UNSKILL)

+ (Low-tech manufacturing only)

Physical infrastructure Total length of highway and railway and inlandwaterway (HRWLENGTH)

+

Telecommunication Length of cable (TELECOM) +Government incentives Total number of different types of zones (SZONES) +

+/−Agglomeration Foreign capital in low-tech industry in previous

year (AGGLOL); Foreign capital in high-techindustry in previous year (AGGLOH)

In model 1 the dependent variable FDIH represents high-tech manufacturing industries.In model 2 the dependent variable FDIL represents low-tech manufacturing industries.


decision. The level of infrastructure development of a particular region should therefore be positivelycorrelated to FDI. Direct measures of physical infrastructure include the total length of highways andrailways (HRWLENGTH). Graf and Mudambi (2005) argue that telecommunication is especially importantfor IT-enabled business, and the availability of telecommunication infrastructure is a significant attractionfor the location of FDI, in this paper, the telecommunication infrastructure development is measured bytotal length of cable (TELECOM, kilometres).

Government incentive to attract FDI is captured by the number of special zones in each region. In thesespecial zones, foreign investors can enjoy preferential policies, such as exemption or reduction of profittaxes, land fees, import duties and priority in obtaining infrastructure services. In turn, the regions withmore zones become more attractive for foreign investment. Thus, we believe that the number of zones ineach region (SZONES, unit) should have a positive relationship with FDI inflow for both high and low-techmanufacturing industries.

Agglomeration refers to the concentration of economic activities that can increase the economies of scaleandpositive externalities (Sun et al., 2002). The level of agglomeration of a particular region should be positivelyrelated to their attraction as a location for FDI. In other words, the existence of FDI in high-tech manufacturingalready in the host country may provide an additional attraction for high-tech MNC investment in the hostlocation and the existence of FDI in low-tech manufacturing industries should attract more FDI in low-techsectors. However, high concentration of one category may cause competition which reduces the attractivenessof a location, thus the agglomeration effect on FDI can be both positive and negative. In this chapter, the foreigncapital received in the previous year in low and high-tech manufacturing industries is used to measure theagglomeration effects on both high and low-tech FDI, respectively (AGGLOH and AGGLOL, USD million).

5. Data and analytical framework

This paper employs panel data for manufacturing industries reported by the China Industrial EconomicStatistical Yearbook. This is the only official sourcewhich provides detailed and consistent data under separatecategories for industries by provinces. There is however a mismatch between the sectoral classifications forChina's industrial statistical report (GB/T 4754) and the ISIV.3. By carefully comparing the definitions acrossboth sources and combining selected industries under GB/T 4754 classification we have constructed abalanced panel data set, containing sixteen (16) manufacturing sectors. Seven (7) of these are manufacturingindustries classified as hi-tech category and nine (9) as low-tech category as described in Table 3.

The empirical strategy was to estimate two models for testing the determinants of FDI in bothhigh-tech and low-tech manufacturing industries separately and then apply the same models to differentregions. We argue that testing the determinants of both high and low-tech manufacturing using the samemodel, may provide misleading estimations as different categories of FDI (i.e. low-tech and hi-tech) maybe attracted by different determinants across different locations.

All determinants, except the number of special zones are lagged by one (1) period. This was necessaryfor two reasons. Firstly, decisions to undertake FDI in a current year will not be realised in the sense thatactual FDI flows do not eventuate until a year later, in other words, multinational FDI activities in a givenyear are based on information from the previous year. Secondly, the amount of FDI inflows and theindependent variable may affect each other. In order to avoid the endogeneity problem, the GDPPC,WAGE, HRWLENGTH, TELECOM, RESEARCH and UNSKILL are transformed to natural logarithm. Theregression model (1) is the location determinants of high-tech manufacturing industry, while the model(2) is the location determinants for low-tech manufacturing industry.

whereare de

FDIHit ¼ αþ β1GDPPCi;t−1 þ β2WAGEi;t−1 þ β3RESEARCHi;t−1 þ β4HRWLENGTHi;t−1þ β5TELECOMi;t−1 þ β6SZONESi;t þ β7AGGLOHi;t−1 þ εit

ð1Þ

FDILit ¼ αþ β1GDPPCi;t−1 þ β2WAGEi;t−1 þ β3UNSKILLi;t−1 þ β4HRWLENGTHi;t−1þ β5TELECOMi;t−1 þ β6SZONESi;t þ β7AGGLOLi;t−1 þ εit

ð2Þ

subscript i refers to individual provinces, t refers to years from 2002 to 2010, all variables used herefined in Table 3.


6. Empirical results

The descriptive statistics for all variables across our four regions are presented in Tables 5a to 5d. There areten (10) provinces across the coastal region, three (3) provinces in the northeast region and six (6) and eleven(11) provinces in the central andwestern regions, respectively. Based on themean value of FDI in each region,the coastal region has the highest average inflow in both high-tech and low-tech manufacturing industries(USD 12,163 million and USD 6520 million) respectively, followed by the northeast region, western andcentral region,where FDI is USD2159million (high-tech) andUSD1391million (low-tech), USD1334million(high-tech) and USD 810 million (low-tech) and USD 328 million (high-tech) and USD 372 million(low-tech), respectively. However, the coastal region also has the highest standard deviation in bothcategories. This indicates that the FDI inflows into the 10 provinces in the coastal region are more variedcompared to the other regions. GDP per capita indicates that the coastal region has the highest GDP per capita,followed bynortheast region, central region andwestern region.Wages asmeasuredby average annual salarypaid to employees in the manufacturing industry, suggest that the western region has a higher annual wagecompared to the central region, this can be explained by the subsidies paid forworking in plateau areas in thewestern region. Unskilled labour is the percentage of total employees without a primary degree; here thecentral and western regions have a significant higher percentage compared to the coastal and northeastregions.

Government spending indicates regional observing capacity for new and high technologies. Coastalregion government invests significant higher amount than any other regions (USD 2660 million), theamount in the northeast, central and western regions were USD 1020 million, USD 845 million and USD450million, respectively. The physical infrastructure and telecommunication are denoted by HRWLENGTHand TELECOM, respectively. As expected, the total length of highway, railway and inland waterway andcable are much longer than coastal region as they represent a much larger land area. In terms of specialzones, coastal region has the highest zones, the highest number of special zones in a province in coastalregion was 40, in the northeast, central and western region, and they were 16, 11 and 11, respectively.

The estimation results from the models are presented separately in two tables. Table 6a shows thedeterminants of foreign capital in low-technology manufacturing industries, while Table 6b shows thehigh-tech manufacturing industries. Overall, the results show a mixed picture. The model has poorexplanatory power for the northeast region, where the overall R-square is comparatively low with 0.2742and 0.0016 recorded for low-tech and high-tech industries respectively, this result may due to the smallnumber of observations (27 observations) used in the model. What is pleasing is that the Wooldridge(2002) test results for serial correlation suggest that there is no serial correlation while the modifiedWaldtest implies homoscedasticity for all the regions. The panel regression results show strong evidence ofagglomeration effects for both low-tech and high-tech categories across all four regions, signifying thepositive influence of agglomeration on foreign investment from all source countries. This result indicatesthat the more developed high-tech manufacturing industries will attract more foreign investment not onlyin high-tech, but also in low-tech manufacturing industries.

Table 5aDescriptive statistics—coastal region (USD millions).

Variable Observations Mean Std. dev. Min Max

FDIH 90 12163.1 14831.1 32.9 70474.0FDIL 90 6519.9 6646.9 207.4 28141.4GDPPC 90 3827.8 2455.4 862.0 11563.3WAGE 90 2552.6 1274.2 935.7 6832.4UNSKILL 90 25.8 10.9 6.6 44.0RESEARCH 90 2659.8 2823.4 9.7 12558.9HRWLENGTH 90 74892.7 62952.9 8366.5 231391.0TELECOM 90 14919.0 12952.2 333.0 45807.0SZONES 90 12.8 8.2 3.0 40.0AGGLOH 90 10111.3 12747.1 32.9 63104.2AGGLOL 90 5567.3 5794.2 188.6 26105.8

Table 5bDescriptive statistics—northeast region.




Market size measured by GDP per capita (GDPPC) is positive and statistically significant for coastal andwestern regions for low-tech categories (Table 6a). Overall (apart fromminor exceptions i.e. central (HT) andnortheast (LT)) the positive relationship between GDPPC and inward FDI flow confirms the hypothesis thatthe amount of foreign capital inflow is positively related to the host location market size. The importance ofthemarket size for all regions but particularly for coastal andwestern regions in low tech industries indicatesthat foreign firms are attractive to that region not only as an export platform, but also as a domestic market.However, the market size has no significant effect on high-tech FDI among all regions.

The wage variable (WAGE) relationship with FDI inflow suggests a negative and significant relationshiponly for low-tech manufacturing in the coastal region, but has no significant effect on high-techmanufacturing across all regions. This finding confirmed our view that for high-techmanufacturing industry,MNCs need to hire skilled or highly trained labour, and they are willing to pay premium for them. By contrastfor low-tech industries FDI inflow to the coastal region shows a negative and significant relationshipsuggesting that lower wages are an attraction for these MNCs investing in low tech industries.

The effect of government investment in research and development is a significant and positive variablefor foreign investment in high tech industries in northeast region while positive for all other regions. Oneexplanation offered here is that foreign investors may be more willing to increase their labour's absorptivecapacity by on-job training. The coefficient of the variable representing government incentives to attractFDI inflows (SZONES) is positive among all regions for high-tech manufacturing FDI and significant for thenortheast, this indicates that the government policies were not a significant factor in attracting more FDIto coastal central and western regions, this in part may be explained by competition between regions toattract FDI for high-tech FDI.

Physical infrastructure (HRWLENGTH) as captured by the total length of highway and railway has apositive influence on all regions for low tech FDI inflowwhich is significant for thewestern region,where a 1%

Table 5cDescriptive statistics—central region.


FDIH 54 1033.6 965.0 80.8 3883.9FDIL 54 810.2 609.6 130.2 2450.0GDPPC 54 1542.3 788.1 630.8 3319.7WAGE 54 1866.4 872.7 805.1 3686.6UNSKILL 54 33.8 7.8 20.7 48.6RESEARCH 54 845.0 783.1 94.2 3866.2HRWLENGTH 54 123686.5 56171.8 60348.2 247530.0TELECOM 54 23060.5 6287.7 11352.0 36431.0SZONES 54 3.8 1.1 2.0 11..0AGGLOH 54 849.3 835.8 80.8 3883.9AGGLOL 54 665.2 509.1 121.3 2338.9

Table 5dDescriptive statistics—western region.

Variable Observations Mean Std. Dev. Min Max



increase in infrastructure development increases low-tech FDI inflow by 0.44% (Table 6a column (d)). Thephysical infrastructure is also positive for three regions namely (a), (b) and (c) in high-tech manufacturingand significant for region (b). This result is in general in agreement with previous studies which suggest thatphysical infrastructure has a positive effect in attracting FDI into regions. Finally the (TELECOM) variablecaptures the development of communications as a determinant of FDI inflow; here (Table 6b) the positive andsignificant effect of this variable for coastal (a), northeast (b) and central (c) regions in high-tech industries isin agreement with several studies with similar results, especially with regards to high-tech industries.

7. Summary and conclusion

This paper begins by describing the phenomenal growth of FDI inflow to China since China adopted its‘open up’ policies in the late seventies. The contribution which the paper makes to the extant literature isthreefold: firstly this study is one of a handful which has researched the economic effects of China'sregional distribution of FDI, where we investigate the disparities of FDI allocations with the objective ofhighlighting the unbalanced distribution of FDI and the consequences of the latter for the economicdevelopment of the whole of China. Secondly we advocate that the identification of the allocation of FDIaccording to high and low technology intensive industries. In particular our research suggests that a policyof matching region resources particularly labour to the demands of FDI as designated by high and low

Table 6aDeterminants of foreign capital in low-tech manufacturing industry among four regions.

Coastal (a) Northeast (b) Central (c) Western (d)

CONS −0.1060 −6.5670 −1.3539 −5.8572(0.910) (0.027)** (0.584) (0.034)**

GDPPC 0.7600 −0.0031 0.1546 1.5108(0.006)*** (0.995) (0.755) (0.052)*

WAGE −0.4791 0.3616 0.2599 −0.9733(0.075)* (0.632) (0.602) (0.330)

UNSKILL −0.0113 −0.0037 −0.0052 0.0001(0.152) (0.813) (0.603) (0.982)

HRWLENGTH 0.1689 0.3276 0.0838 0.4393(0.124) (0.165) (0.614) (0.091)*

TELECOM 0.0579 0.3783 −0.0160 0.2841(0.213) (0.290) (0.954) (0.246)

SZONES 0.0014 −0.1320 −0.0424 −0.0130(0.880) (0.007)** (0.119) (0.858)

AGGLOL 0.4777 0.6668 0.6844 0.0006(0.000)*** (0.001)*** (0.000)*** (0.996)

R-sq (overall) 0.9133 0.2742 0.8825 0.4439Prob N chi2 0.0000 0.0000 0.0000 0.0000

Note: () represents p-value, ***, **, * denote statistical significance at 1%, 5% and 10% level, respectively.

Table 6bDeterminants of foreign capital in high-tech manufacturing industry among four regions.

Coastal (a) Northeast (b) Central (c) Western (d)

CONS −2.2356 −10.1900 −3.4573 0.4994(0.034) (0.004)** (0.271) (0.794)

GDPPC 0.4004 0.6206 −0.2520 0.5805(0.167) (0.384) (0.705) (0.318)

WAGE −0.1805 −1.3179 −0.3818 −0.7477(0.453) (0.264) (0.561) (0.291)

RESEARCH 0.0035 0.8207 0.3976 0.1847(0.975) (0.015)** (0.282) (0.344)

HRWLENGTH 0.1194 0.7014 0.0041 −0.0219(0.181) (0.010)*** (0.983) (0.910)

TELECOM 0.1832 0.9367 0.6561 0.1860(0.000)*** (0.019)** (0.060)* (0.314)

SZONES 0.0066 0.1298 0.0588 0.0142(0.430) (0.023)** (0.180) (0.793)

AGGLOH 0.7269 0.1212 0.9071 0.7045(0.000)*** (0.594) (0.000)*** (0.000)***

R-sq (overall) 0.9758 0.0016 0.8877 0.9501Prob N chi2 0.0000 0.0000 0.0000 0.0000

Note: () represents p-value, ***, **, * denote statistical significance at 1%, 5% and 10% level, respectively.


technologies would in turn provide employment opportunities across China's newly created urban centreswhile simultaneously reducing the migration and concentration of labour particularly low and unskilledlabour toward the already densely populated and congested coastal or eastern regions of China.

The paper documents the growth in China's FDI inflows according to high-low technology intensiveindustries where our research demonstrates that the share of FDI investment across the high-tech sector'schanges dramatically to reach approximately two thirds of the total national utilised manufacturing FDIinflow over a relatively short timeframe (2001–2008).

Our research then shows that although high tech industries dominate the inflow of FDI today themajority of which is located in the coastal region this investment is complemented by an equal amount oflow-tech FDI investment allocated to the coastal region. This situation in turn leads to a very unevenallocation of FDI inflow across China particularly China's hinterland accounts for approximately only 12%of high tech and 15% of low tech utilised FDI.

Our research methodology bi-furcates China's FDI inflow into manufacturing industries according tolow and high tech categories allocated across China's four major geographic zones where we find that eachregion has significantly different characteristics which vary in the degree to which they are both suitableand attractive for FDI on the basis of technological intensity. Finally we analyse and summarise thedeterminants of FDI for both low and high tech industrial production across China's four regions.

The empirical testing confirmed the following conclusions. Firstly, agglomeration effects have a positiveand significant impact on both high-tech and low-techmanufacturing industries. Secondly, in order to attractlow-tech manufacturing FDI, physical infrastructure is a crucial driver given the vastness of China's regionalland area. For high-tech FDI, telecommunication development has a more significant effect than the physicalinfrastructure development. Thirdly, government preferential policies should be more location specific andindustry based, due to the lower but positive spillover-effect on FDI in the inland region's productivity. The‘Go West’ policy should pay more attention to low-tech manufacturing FDI and encourage infrastructuredevelopment, for coastal regions, the government should continually invest in research and development,due to the significance of human capital as a key factor in promoting technological progress.

References

Andreosso-O'Callaghan, B., Wei, X., 2002. EU FDI in CHINA: locational determinants and its role in CHINA'S hinterland. Proceedings ofthe 15th Annual Conference of the Association for Chinese Economics Studies Australia (ACESA) (http://mams.rmit.edu.au/6fe09elc6t8sz.pdf).

Chen, C., 2011. Foreign Direct Investment in China: Location Determinants, Investor Differences and Economic Impacts. EdwardElgar, Cheltenham.

http://mams.rmit.edu.au/6fe09elc6t8sz.pdf

http://mams.rmit.edu.au/6fe09elc6t8sz.pdf

http://refhub.elsevier.com/S1566-0141(14)00055-7/rf0005



Cheng, L., Kwan, Y.K., 2000. The location of foreign direct investment in Chinese regions: further analysis of labor quality. In: Krueger,A.O. (Ed.), The Role of Foreign Direct Investment in East Asian Economic Development. University of Chicago Press.

Dees, S., 1998. Foreign direct investment in China: determinants and effects. Econ. Plan. 31, 175–194.Dunning, J.H., 1988. Explaining International Production. Unwin Hyman, Boston, MA.Dunning, J.H., 2009. Location and the multinational enterprise: John Dunning's thoughts on receiving the “Journal of International

Business Studies” 2008 decade award. J. Int. Bus. Stud. 40, 20–34.Gao, T., 2005. Labor quality and the location of foreign direct investment: evidence from China. China Econ. Rev. 16, 274–292.Graf, M., Mudambi, S.M., 2005. The outsourcing of IT-enabled business process: a conceptual model of the location decision. J. Int.

Manag. 11, 253–268.Groh, A.P., Wich, M., 2012. Emerging economies' attraction of foreign direct investment. Emerg. Mark. Rev. 13 (2), 210–229 (June 2012).Hou, J.W., 2002. China's FDI Policy and Taiwanese Direct Investment (TDI) in China. Department of Economics, California State University.Jadhav, P., 2012. Determinants of foreign direct investment in BRICS economies: analysis of economic, institutional and political

factor. Procedia Soc. Behav. Sci. 37, 5–14.Kalotay, K., Sulstarova, A., 2010. Modelling Russian outward FDI. J. Int. Manag. 16 (2010), 131–142.Kearney, C., 2012. Emerging markets research: trends, issues and future directions review article. Emerg. Mark. Rev. 13 (2), 159–183

(June 2012).Kinoshita, Y., 2001. R&D and technology spillovers through FDI: innovation and absorptive capacity. C.E.P.R. Discussion Paper No.

2775. Centre for Economic Policy Research, London.Lin, C.H., Lee, C.M., Yang, C.H., 2011. Does foreign direct investment really enhance China's regional productivity? J. Int. Trade Econ.

Dev. 20, 741–768.Liu, Z., 2008. Foreign direct investment and technology spillover: theory and evidence. J. Dev. Econ. 85, 176–193.Milner, C., Pentecost, E., 2006. Locational advantage and US foreign direct investment in UK manufacturing. Appl. Econ. 28, 605–615.Mudambi, R., Mudambi, S.M., 2005. Multinational enterprises knowledge flows: the effect of government inward investment policy.

Manag. Int. Rev. 45, 155–178.Ng, L.F., Tuan, C., 2006. Spatial agglomeration, FDI and regional growth in China: locality of local and foreign manufacturing

investment. J. Asian Econ. 17, 691–713.Oecd Science, 2009. Technology and Industry Scoreboard. OECD.Pan, Y., Tse, D.K., 2000. The hierarchical model of market entry modes. J. Int. Bus. Stud. 31, 535–554. http://dx.doi.org/10.1057/

palgrave.jibs.8490921.Qiu, L.D., 2003. Comparing sectoral FDI incentives: comparative advantages and market opportunities. Ann. Econ. Financ. 4, 383–408.Shaukat, A., Guo, W., 2005. Determinants of FDI in China. J. Glob. Bus. Technol. 1, 21–33.Sun, H., Chai, J., 1998. Direct foreign investment and inter-regional economic disparity in China. Int. J. Soc. Econ. 25, 424–447.Sun, Q., Tong, W., Yu, Q., 2002. Determinants of foreign direct investment across China. J. Int. Money Financ. 21, 79–113.Wang, Z.Q., Swain, N., 1997. Determinants of inflow of foreign direct investment in Hungary and China: time-series approach. J. Int.

Dev. 9, 695–726.Wei, Y., Liu, X., 2006. Productivity spillovers from R&D, exports and FDI in China's manufacturing sector. J. Int. Bus. Stud. 37, 544–557.Wei, Y., Liu, X., Wang, C., 2008. Mutual productivity spillovers between foreign and local firms in China. Camb. J. Econ. 32, 609–631.Wei, K., Yao, S., Liu, A., 2009. Foreign direct investment and regional inequality in China. Rev. Dev. Econ. 13, 778–791.Whalley, J., Xin, X., 2010. China's FDI and non-FDI economies and the sustainability of future high Chinese growth. China Econ. Rev.

21, 123–135.Wooldridge, J.M., 2002. Econometric Analysis of Cross Section and Panel Data. MIT Press, Cambridge, MA.Yang, D., 2010. Foreign direct investment and development of high-growth industrial sectors in China, 1998–2006. Chin. Econ. 43,

93–114.Yao, S., 2006. On economic growth, FDi and exports in China. Appl. Econ. 38, 339–351.Yu, K., Xin, X., Guo, P., Liu, X., 2011. Foreign direct investment and China's regional income inequality. Econ. Model. 28, 1348–1353.Zhang, X., Yuk, H.P., 1998. Determinants of Hong Kong manufacturing investment in China: a survey. Market. Intell. Plan 16, 260–267.




























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analysing china's foreign direct investment in manufacturing from a high–low technology...

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