Abstract This study investigates the patterns and determinants of China‟s intra-industry trade (IIT) with her major two trading partners in Northeast Asia, namely, Korea and Japan Using numerical measures, we examine the evolution of trading characteristics of these three countries and find that, due to high growth rate and rapid integration into the world market, trade patterns of these countries are experiencing a great change Although inter-industry trade still accounts for the majority, its importance relative to intra-industry trade is declining significantly In addition, based on cross-industry panel data from 1992 to 2003, we develop econometric models to test the specific determinants of IIT between China and Japan and between China and Korea The empirical results show that product differentiation, research intensity and inward foreign direct investment played an important role in driving Chinese bilateral intra-industry trade with Japan and Korea, while economies of scale, trade imbalance and income inequality are negatively correlated with China‟s IIT Findings generally conform to what we expected under the economic theory Key Words: Intra-industry trade, Product differentiation, Economies of scale Introduction Since the introduction of the concept of intra-industry trade1 (IIT), i.e simultaneous exports and imports of commodities within the same „industry‟ or product category, there have been extensive studies examining the phenomenon Studies on this topic can be classified into three groups The first concerns the measurement as well as the magnitude of intra-industry trade [e.g Grubel and Lloyd (1975), Greenaway (1983) and Brulhart (1994)] The second category attempts to develop theoretical explanations for its existence [e.g Krugman (1979), Lancaster (1980) and Falvey (1981)], while the third is seeking to evaluate the determinants of intra-industry trade in an econometric framework [e.g Balassa and Bauwens (1987), Greenaway et al (1994) and Hu and Ma (1999)] The current study belongs to the third category for the special case of China Since the implementation of the “open door” policy in late 1970s, China has pursued significant trade liberalization and rapid integration into the world market It is observed that during the years 1979-2006, not only China‟s international trade volume expanded greatly, its trade patterns also experienced a noticeable change, especially in terms of the increasing share of intra-industry trade Although inter-industry trade still accounts for the majority, its importance relative to intra-industry trade is declining In this study, we focus on China‟s intra-industry trade with her major two trading Balassa introduced the concept of intra-industry - as compared to inter-industry – trade in 1966 partners in Northeast Asia, namely, Japan and Korea The purpose is to investigate the changing nature of China‟s trading patterns and the determinants of intra-industry trade The paper is comprised of both descriptive and empirical analysis The descriptive analysis will investigate the major trade characteristics of Northeast Asia and the evolution of China's trade relation with Japan and Korea It will further evaluate China‟s intra-industry trade pattern of various industries In the empirical part, we will test several hypotheses regarding the determinants of IIT using cross-industry panel data By exploring the case of China, the present study will contribute to empirical works on intra-industry trade in two ways First, most empirical studies concerning IIT focus on industrial countries where IIT is more likely to occur Although trade in developing countries and newly industrializing countries has attracted increasing academic interest since 1980s, intra-industry trade studies involving emerging economies are still limited, empirical analysis concerning China‟s intra-industry trade is even scarcer In fact, China has made great strides in manufacturing and has become an important supplier of manufactured exports over the past three decades, thus we can expect that China‟s IIT has also increased significantly Second, most cross-industry studies only incorporate a limited set of industries or merely employ one-year data in their estimations In contrast, we establish a rich data set, covering See, for example, Loertscher and Wolter (1980), Caves (1981), Bergstrand (1983), Kol and Mennes (1983), Greenaway and Milner (1984), and Clark and Stanley (2003) more than 3003 of four-digit SITC4 industries over a 12-year period This provides a unique opportunity to conduct quality cross-industry analysis The paper proceeds as follows Chapter reviews the relevant literature Chapter provides an overview of China‟s total trade and the development patterns of China‟s bilateral trade with Japan and Korea Chapter presents the methodology and the hypotheses to be tested in econometric models and Chapter discusses model estimation procedures and reports the empirical results Chapter offers some concluding remarks 409 industries for China-Japan, 302 industries for China-Korea Standard International Trade Classification Review of the literature 2.1 Theoretical Literature Review The 1960s witnessed a revolution in international trade theory as new trade patterns represented by intra-industry trade emerged The traditional trade theories such as comparative advantage theory and factor endowments theory set out by David Ricardo and Heckscher-Ohlin provided no explanation towards intra-industry trade5 as intra-industry trade suggests that a country has a comparative advantage and disadvantage in the same product (since it both exports and imports the product) Hence, considerable academic attention has been devoted to providing proper interpretation of the phenomenon over the past four decades The evolution of intra-industry trade theories can be divided into three stages 2.1.1 Early Hypotheses According to Toh‟s (1982) summarization, in the early 1970s, there were several hypotheses concerning intra-industry trade The representatives include seasonal variation, border trade, and entrepot trade Seasonal trade is mainly applied to agricultural products, such as Chinese apple, Krugman & Obstfeld (1991) which can be exported all around the world during a particularly good apple growing season, but imported during a poor one Border trade is induced by taking transport costs into account Although certain goods already produces domestically, the existence of long common borders of two adjoining countries may find it less costly to trade with a neighboring country, thus intra-industry trade occurs Entrepot trade is a special kind of re-exportation, it is the export without further processing or transformation of a good that has been imported Entrepot trade often happens in large ports such as Rotterdam in Netherland and Singapore that charges lower or no tariffs and act as holding places for goods which are then sold on However, starting from Pomfret (1976) and Lipsey (1976), it has been argued that intra-industry trade is a statistical artifact arising from improper aggregation of trade data Because defining industries as “the same” is a matter of classification, intra-industry trade would disappear at the finest level of disaggregation As a result, how to establish meaningful industry categories became the center of the debate However, as Finger (1978) recognized, categorical aggregation is not a significant reason of intra-industry trade Sufficient empirical evidence have emerged to suggest that there exists intra-industry trade even if industries are disaggregated into extremely fine levels [e.g Gray (1979) and Bergstrand (1983)] and moreover, instead of a higher level of disaggregation, a neither too fine nor too broad industry category could be a scientific way for the meaningful analysis of IIT [Menon and Dixon (1996)] With these considerations in mind, we adopt four digit Standard International Trade Classification data in our estimation6 2.1.2 Early Theoretical Explanations As a possible solution to the famous Leontief paradox, which rejected empirically the traditional factor proportions theory, Linder (1961) proposed an alternative theory that was consistent with Leontief's findings Linder‟s Theory focuses on the role of demand, rather than supply, on trade patterns and relates trade to a country's development level and domestic demand composition Linder hypothesized that consumers‟ tastes depend on their income levels and countries with similar preferences would develop similar industries With similar demand, these countries would then trade back and forth in similar but differentiated products Accordingly, the closer are the income levels per consumer, the higher are the level of intra-industry trade Dreze (1961) further postulated that larger countries have better abilities to produce differentiated goods than smaller ones and, on the other hand, larger markets can accommodate a larger demand for different varieties, so market size also contribute to intra-industry trade Meanwhile, international economists also attempted to explain IIT by reference to dynamic extensions of the traditional static factor proportion model Posner (1961) proposed the “technological gap” theory which claims that changes in international Toh (1982) has shown that at the four-digit level of disaggregation bias ought to be of lesser significance trade are reflected by the relative technological sophistication of countries The most highly industrialized nations export new high-technology products to other nations Foreign firms take times to acquire the new technology after which they can reduce imports and even occupy markets abroad While the technological gap model emphasizes the time lag in the imitation process, the product cycle model propounded by Vernon (1966) stresses the standardization process The theory suggests that early in a product's life-cycle this product is produced and consumed only in the area where it was invented After the mass-production techniques are developed and the product becomes widely adopted and used in the world markets, through imitation and technology transfer, the location of production gradually moves to developing countries, where they can enjoy a lower cost These developing countries then export the relative older versions of product to the original countries, simultaneously import the latest versions in the same product category 2.1.3 Synthetical Research Since late 1970s, many new models have been designed to analyze IIT in a synthetical way The most comprehensive and widely accepted explanation is Paul Krugman's new trade theory which emphasizes the role of economies of scale Krugman reveals that, with increasing returns to scale, each producer within any particular industry will specialize in a limited variety of production in order to reap the advantages of economies of scale Grubel and Lloyd (1975) made another contribution to the theory of intra-industry trade as they divided intra-industry trade into horizontal and vertical types The horizontal intra-industry trade (HIIT) is trade in differentiated product with similar quality but different attributes, while vertical intra-industry trade (VIIT) involves vertically differentiated goods that are distinguished by quality This classification was then proved to be important in theoretical research because the two types are driven by different or even contradictory forces The theoretical work which had attempted to explain intra-industry trade using models of monopolistic competition with increasing returns to scale mainly concentrated on horizontal differentiation Following Dixit and Stiglitz (1977), the “love of variety” approach, and Lancaster (1979), the “ideal variety” approach, Krugman (1979) first developed models highlighting the key role of scale economies and product differentiation in determining IIT As noted above, this approach became the foundation of the following relevant literature Helpman (1981) and Helpman and Krugman (1985) subsequently refined and synthesized the previous models into an unifying one incorporating factor endowments, decreasing costs and product differentiation This so-called Chamberlin–Heckscher–Ohlin (CHOS) model shows how the share of intra-industry trade in total trade is related to consumer patterns and factor endowments As a result, a theoretical framework associating different country-specific characters with intra-industry trade was popularized By extending the previous theoretical work, Bergstrand (1990) further rationalized relationships between the share of intra-industry trade and the average levels of and inequalities between their economic size, per capita incomes, capital-labor endowment ratios, and tariffs He established a unified theoretical framework which includes all those particular variables and analyzed how each of those determinants specifically affects the share of intra-industry trade in a given commodity group Studies of vertical IIT are derived from the seminal paper of Falvey (1981) that pointed out goods under the same statistical category but of different quality may be produced using different combination of factor inputs, so that the differences in factor endowments may have a large impact on IIT Falvey (1981) and Falvey and Kierzkowski (1987) constructed models assuming that goods are distinguished by the perceived quality By associating the product quality with capital intensity, they both indicated that goods with high capital intensity tend to have high quality They also showed that the relatively high-income and capital-abundant countries will specialize in and export capital-intensive products of high quality, whilst the relatively low-income and labor-abundant countries export low quality labor intensive products In addition, on the basis of the traditional comparative advantage theory, Flam and Helpman (1987) emphasizes the role of technology, they claimed that technological differences are the main source of different varieties Since market structure was ignored in the above studies, a complementary work with a “natural oligopoly” context was done by Shaked and Sutton (1984) They assumed that, R&D input, which is considered as a kind of fixed cost, plays an important role in determining product quality Thus, with entry barriers, only the best firms bearing big advantage of 10 Trade liberalization is another important factor stimulating China‟s IIT, in particular in the past three decades However, as the measure of the degree of openness of the economy, tariff data is not available currently at a disaggregate level, thus we have to omit this variable 4.2 Model Specification The empirical analysis of the determinants of IIT in the next step is performed using pooled time-series and cross-section data IIT of China-Japan and China-Korea are tested separately across the period 1992–2003, taking the 4-digit SITC industry21 as the unit of observation The unadjusted G-L indices which are calculated based on the selected 4-digit SITC trade data between the two countries are used as the dependent variable, while the explanatory variables included are based on the hypotheses introduced in previous part The regression model is given as follows, with the expected signs shown in parentheses IITkt    1 PDkt   SE kt   RDkt   FDI k (t 1)   5TIMBkt   6YDt  u (+) (?) (+) (+) (-) (1) (-) where IIT stands for the intra-industry trade index, and PD the level of product differentiation SE denotes scale economies, and RD the research intensity FDI represents foreign direct investment and TIMB trade imbalance YD is the difference of GDP per capita  j (j=0, ……6) are parameters to be estimated, and k, t are 21 Specifically, 409 are included in China-Japan‟s case and 302 industries are included in China-Korea‟s case 40 industry and time suffix respectively u is a standard error term 4.3 Sources of Data The four digit SITC (Rev 3) trade data was transformed from digit HS data through the industry concordance provided by Jon Haveman The raw HS data was extracted from China Customs The product differentiation and trade imbalance data for each industry are calculated using the trade data and the SITC code We recognize that there exists a possible drawback to the data set It could be better if all the selected industries have the corresponding industrial-characteristic data, but we cannot make it due to data availability The data on research intensity (RD), scale economies (SE) and foreign direct investment (FDI) 22 are based on the eight sectors we have mentioned above, which means all the industries that fall into the same sector will have same value for the three variables Japan and Korea‟s industry data are used for the three industry characteristic variables Data on RD and SE are derived from OECD: STAN Industry Structural Analysis Database In the regression for China-Japan (China-Korea) trade, Japanese data (Korean data) on RD and SE are used23 FDI data from Japan and Korea are obtained from Japan Ministry of Finance and the Export-Import Bank of Korea respectively Data on per capita GDP is obtained from the online database of United Nations 22 The Korean FDI data was released in a highly aggregate level, so each of the eight sectors is given the same value, which is the total FDI volume in manufacturing 23 Note that when testing the role of those determinants under a bilateral framework, it could be better to take into consideration both parties‟ situations, and ideally we would use another variables indicating the average levels of or the differences in RD and SE between China and Japan (or Korea) But it is not practicable because Chinese information is not available through open sources 41 Empirical Analysis Equation (1) was estimated for both China-Japan and China-Korea trade In the literature, linear model using the standard OLS method is most frequently adopted in the regression analysis The correctness of this approach is somewhat questionable as it generates predicted values that fall outside the feasible interval24 In order to overcome this problem, transforming the dependent variable using the logit method 25 is usually applied as an alternative Nevertheless, there is no consensus among the researchers regarding which of the two methods is more efficient in the empirical study of IIT26 In the present study, the prediction error of OLS method is of less concern for the aim is to investigate the determinants of IIT not the forecasting, so we will not use the logit transformation of IIT As noted above, the foremost purpose of the estimation is to test the hypotheses generated from several theoretical models Therefore, because of data limitation and the inevitable proxy problems encountered in regression analysis, we will mainly focus on the signs of the selected determinants and their statistical significance rather than the magnitude of their effects Estimation results of the random effect model for both China-Japan and China-Korea are presented in table 24 The value of the dependent variable (IIT) lies within the range of and 100 For further discussion on the logit transformation method see Gujarati (1995, pp 556-57) 26 For example, Loertscher and Wolter (1980) and Cave (1981) prefer the idea of logit transformation while Greenaway and Milner (1986) argue that it is not necessary for such transformation, because the prediction error becomes less critical when the main focus of the research is hypothesis testing and not forecasting Furthermore, Balassa (1986) and Balassa and Bauwens (1988) find that OLS and logit models give similar results as to the significance of coefficients and the overall explanatory power of equations in studies of IIT 25 42 Table Determinants of China’s IIT, using panel data Independent Variable China-Japan China-Korea Intercept 328.21 (38.14) *** 221.87 (28.60) *** FDIt-1 2.04 (9.95) *** 8.47 (24.27) *** PD 2.42 (5.35) *** 2.20 (4.04) *** RD 1.02 (8.833) *** 1.47 (6.17) *** SE -66.60 (-4.84) *** -147.15 (-5.97) *** TIMB -12.23 (-58.19) *** -12.10 (-49.91) *** YD -111.68 (-22.33) *** -41.61 (-9.42) *** Number of observations 4908 3624 0.457 0.442 0.000 0.000 R-square Prob> chi2 Notes: 1．t-ratios are given in the parentheses 2． *** denote statistical significance in t-tests at 1% level Given the possible existence of relevant omitted variables, the weak surrogates for the determinants to evaluate, and the limitation of industry-characteristic data for each specific industry, the overall explanatory power of the equations are quite satisfactory for a panel study As we can see from table 5, R2 is 0.457 and 0.442 for China-Japan and China-Korea respectively, conferring both regressions a relatively high degree of robustness Furthermore, the results strongly support the IIT hypotheses and all the coefficients are statistically significant at 1% level The relationships indicated by the 43 sign of the coefficients are consonant for both China-Japan and China-Korea cases, showing that all the determinants under investigation work in the same manner The coefficients of lagged FDI have a positive sign This indicates that by relocating their production facilities in China, FDI from Japan and Korea are IIT-enhancing As expected the coefficients of product differentiation (PD) have positive signs, indicating that higher level of product differentiation promote bilateral IIT This finding is consistent with the earlier study by Greenaway and Milner (1984) for U.K manufacturing Loertscher and Wolter (1980) as well as Balassa and Luc Bauwens (1987) also obtained similar conclusions for industrialized countries in a multilateral framework As a proxy for research intensity, the research and development variable (RD) also have positive coefficients, showing that technological complexity contributed remarkably to the growth of China‟s IIT The negative signs for bilateral trade imbalance (TIMB) are in line with previous findings that trade imbalances between any trading partners will substantially bias down the calculated IIT index whenever the unadjusted G-L index is utilized The per capita income difference variables (YD) generate negative signs, which fit well with the theoretical expectation that the share of intra-industry trade will be large if per capita income of the two economies is similar What is noteworthy is that the proxy for the scale economies (SE) is significant with negative signs The results seem to provide a reasonable fit to the interpretation that scale economies are likely to restrict production of differentiated 44 product through entry barrier and standardization, thus hinder intra-industry trade Concerning China‟s manufacturing industry, although it has been developing rapidly, it still lacks economies of scale compared to Japan and Korea This disadvantage could substantially impair the profitability and the competitiveness of Chinese manufacturing Since the data on SE applied to Japan (or Korea)‟s industries, a larger value may indicate a bigger gap of scale economies relative to China, thus act as an obstacle to intra-industry trade Although the signs of the coefficients are consistent for both China-Japan and China-Korea cases, people may still question the differences on the magnitude of the coefficients Based on this point, we construct another model to detect the relatively different effect of the determinants on the two country pairs We combine the data for the two groups into one data set, introducing a country dummy which indicates from which group the data came In particular, the dummy takes on value when the corresponding raw of data is subject to China-Japan‟s trade, otherwise the value of the dummy is Then we create interaction terms by multiplying the dummy variable times each other explanatory variables in turn, creating as many interaction terms as there are other independent variables, except YD The coefficients of interaction terms will facilitate the comparison of regression results obtained for two groups of subjects The results of the regression incorporating all the newly-added or created data are reported in table 45 Table Comparison of Different Country Pair Independent Variable Coef t p Intercept 234.61 32.13 0.000 PD 2.25 4.28 0.000 FDIt-1 7.87 24.07 0.000 SE -95.44 -4.16 0.000 RD 1.48 6.45 0.000 YD -68.85 -20.95 0.000 TIMB -12.39 -54.01 0.000 Country Dummy 44.63 4.41 0.000 D-PD -0.127 -0.18 0.857 D-FDIt-1 -5.89 -15.00 0.000 D-SE 32.48 1.20 0.232 D-RD -0.086 -0.34 0.735 D-TIMB 0.744 2.40 0.016 Number of observations 8532 R-square 0.442 Prob > chi2 0.000 Results shown in the upper half of the table further confirm our hypotheses All the variables have the expected sign and are highly statistically significant Furthermore, the coefficients of the interaction terms reveal that the impacts of the determinants are similar, though not exactly identical, on the two country pairs The interaction terms for PD, SE and RD are statistically insignificant, meaning that product differentiation, scale economies and research and development expenditure affect China-Japan and China-Korea‟s IIT in a quite same manner The significantly negative sign on the TIMB dummy indicates that trade imbalance has a more significant negative effect on China-Japan‟s trade However, the purpose to include TIMB at the beginning is to 46 control for any potential biased impact that the trade imbalance imposes on the calculation of IIT indexes Hence, the different magnitude may not have economic sense Lastly, the FDI dummy is significant with negative sign, but the economic intuition is ambiguous, for the result is probably ascribed to the poor data source of Korean FDI As noted above, data for Korean FDI are collected in a highly aggregate level, so there is no variation across industries and each of the eight sectors is given the same value, which is the total FDI volume in manufacturing This could cause a serious bias when comparing the industry-level FDI effect for two countries 47 Conclusion This study attempted to achieve two things Firstly, to analyze the changing nature of China‟s general trading patterns and the recent trends in China‟s intra-industry trade with Japan and Korea Secondly, to investigate various testable hypotheses of intra-industry trade with China‟s trade data The first analysis shows that due to the significant trade liberalization and trade expansion within East Asia, trading pattern of China and its two major trading partners has changed dramatically since 1980s In particular, China implemented industry upgrading policy to improve its competitiveness in the world market As a result, China‟s intra-industry trade with Japan and Korea have increased remarkably, and technology-intensive products such as machinery and electrical products accounts for the largest part In the empirical part, based on panel data of over 300 of 4-digit industries from 1992 to 2003, we find that China‟s IIT with Japan and Korea are significantly negatively correlated with scale economies (SE), bilateral trade imbalance (TIMB) and per capita income difference (YD) Foreign direct investment (FDI), product differentiation (PD) and research intensity (RD) are the factors to stimulate China's intra-industry trade Apart from overall satisfactory explanatory power, the results also strongly confirm the hypotheses concerning the determinants of IIT and consistent with most of the literature This evidence suggests that the industry-specific characteristics have generally identical impacts on China and the developed economies, as China is 48 pursuing a rapid structural transformation toward skilled-intensive and high value-added production Although this study provides a better understanding of China‟s intra-industry trade patterns, it is associated with some limitations that point the way to future work First, we have to use some weak proxies for the determinants due to the lack of appropriate data Second, we not disentangle total IIT into horizontal (HIIT) and vertical components (VIIT), when in fact these two different types of IIT may have different determinants Third, although the G-L index is proved to be the most appropriate measure of IIT and the 4-digit data is prevalent, trying others measures and different level of aggregation may lead to substantial 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511-540 53 Appendix : Definition of Variables and Their Measurement IIT: Intra-Industry Trade Computed using unadjusted G-L index PD: Product differentiation, is defined as the number of five-digit sub-groups in each four-digit sub-groups SE: Scale economies, is proxied as the ratio of value added to the total output in each sector RD: Research intensity, is expressed as the ratio of research and development expenditures to value added in each sector FDI(t-1): Foreign direct investment, is defined as the natural logarithms value of the lagged FDI inflows TIMB: Trade imbalance, is proxied as the natural logarithms value of trade imbalance in a particular industry YD: Difference in per capita income, is derived according to the following formula: Dcj   wln w   1  wln 1  w ln Where w YPc YPc  YPj 54 [...]... for the development of intra- industry trade in manufacturing between China and Japan, and between China and Korea 3.3 Development of China’s Intra- Industry Trade with Japan and Korea Using the weighted G-L index defined in equation (3), we first calculate China‟s bilateral IIT level with Japan and Korea for all traded commodities as a whole The calculation is based on simple average of four-digit SITC... degree of IIT across industries Of the eight sectors, electrical and machinery have the highest share of IIT, followed by Chemical, wood, medal and transport products, while trade in food and textile sectors are predominant by inter -industry trade with the share of IIT less than 10 percent Table 3 Level of China’s Intra- Industry Trade with Japan and Korea Sector SITC Code Food and beverage China -Japan. .. The bilateral economic and trade ties with Japan and Korea has been rapidly developing in relative importance According to Japan External Trade Organization (JETRO), bilateral trade between China (excluding Hong Kong) and Japan amounted to 236.6 billion US dollars in 2007 China accounted for 17.7 percent of Japan' s total trade and replaced U.S (16.1%) as the biggest trading partner of Japan while Japan. .. empirical trade literature examining the industry- specific determinants of intra- industry trade It explores the impacts of inter -industry differences on intra- industry trade for the United Kingdom by reference to various industry structure variables Intra- industry trade level is measured at third digit of the SITC and the U.K SIC (Bj) Besides, an adjusted index (Cj), which is a weighted average of all... i  (1) Where Xi and Mi stand for exports and imports in industry i respectively and n is the number of industries taken into consideration It is an unweighted average of trade overlap for each industry and E measures the degree of a country‟s inter -industry specialization Accordingly, the lower the value of this index, the larger the share of intra- industry trade However, Grubel and Lloyd (1971) criticized... similar patterns of those in industrialized countries despite some different features Zhang et al (2005) investigate the features and determinants of Chinese intra- industry trade during the 1992–2001 period for 50 of China‟s trade partners They include a much broader range of explanatory variables (15 totally) in the estimation equation and disentangle intra- industry trade into vertical and horizontal intra- industry. .. unit values of exports and imports of a particular product falls in a range of ±15%（or a spread of ±25 %）, it is categorized as horizontal intra- industry trade, or else it is considered to be vertical intra- industry trade Following the two alternative criteria, Greenaway, Hine and Milner (1994, 1995, 1996) test respectively how country- and industry- specific and both factors affect vertical and horizontal... the 3rd place of China‟s largest trading partner, only behind the EU and the U.S Meanwhile, in accordance with the report by Korea' s Ministry of Commerce, Industry and Energy, 25 China maintained the largest trade partner and export destination of Korea in 2007, with the bilateral trade volume climbed up to 140.5 billion US dollars On the other hand, statistics from China indicated that Korea ranked... electrical and chemical industries in China sold on average 91.2 percent of their products to overseas markets including Korea Since most of the products exported back to Japan and Korea belong to the product categories which comprise China's major imports from those two countries16, we can expect that this kind of “reverse imports” contribute to the growth of China‟s intra- industry trade with Japan and Korea. .. 2 shows trade patterns of China, Japan and Korea It can be seen that their trading structures differed in many ways 30 years ago, but the disparities have reduced gradually over the past three decades Japan and Korea are both well-known for being resource-poor countries, and because of the shortage of farmland and the higher degree of industrialization, they are highly dependent on imports of agriculture ... China -Japan (China -Korea) trade, Japanese data (Korean data) on RD and SE are used23 FDI data from Japan and Korea are obtained from Japan Ministry of Finance and the Export-Import Bank of Korea. .. overview of China‟s total trade and the development patterns of China‟s bilateral trade with Japan and Korea Chapter presents the methodology and the hypotheses to be tested in econometric models and. .. inter-industry trade with the share of IIT less than 10 percent Table Level of China’s Intra-Industry Trade with Japan and Korea Sector SITC Code Food and beverage China -Japan China -Korea 1992IIT