International Intra-Industry Trade of China

Transcription

1 Published in Weltwirtschaftliches Archiv (Review of World Economics), Jan. 1999, Vol.135, No.1, pp , Kiel Institute for World Economics, Germany. * International Intra-Industry Trade of China a b Xiaoling Hu and Yue Ma a Southampton Institute of Higher Education and Portsmouth University, UK b Lingnan University, Hong Kong Corresponding author and address: Dr Yue Ma Dept of Economics, Lingnan University, Tuen Mun, Hong Kong Fax: + (852) ; Tel (852) ; yuema@ln.edu.hk Internet: * The authors are grateful for useful comments from an anonymous referee, Professors Anthony Clunies Ross, James H Love, Robert Hine and David Watkins. Xiaoling Hu would like to acknowledge financial support from SIHE and the RES and Yue Ma is grateful for the financial support from The Leverhulme Trust and the ESRC. The SITC-SIC translation table was kindly provided by Professor David Greenaway. Abstract The purpose of this paper is to measure the extent of the international intra-industry trade of China, and to test empirically various country-specific and industry-specific hypotheses concerning the determinants of vertical and horizonal intra-industry trade between China and her major trading partners. It is revealed that China has possessed the prerequisite of intraindustry trade and that China's intra-industry trade follows the similar patterns of those in developed countries as China is moving towards a market-oriented economy.

2 1. Introduction Over the past three decades there has been extensive interest among academics in the theory of intra-industry trade, that is the simultaneous export and import of commodities in the same statistical product group, which was considered to be a new development in international trade theory. However, empirical and theoretical studies concerning intra-industry trade have been undertaken mostly for Western countries, especially for West European Countries. There are too few literature on this subject which relates to developing countries. The purpose of this paper is to measure the extent of the international intra-industry trade of China, and to contribute economic analysis of the factors which influence the China's intraindustry trade pattern, and to test empirically various country-specific and industry-specific hypotheses concerning the determinants of intra-industry trade between China and her major 45 trading partners. We will consider the intra-industry trade of China (a developing country) across trading partners in various industrial groups, and with one of the partner countries in the EU (the UK) across industries. This will make it possible to examine both industry characteristics and country characteristics at the same time and to draw some conclusions about the co-relationship between them. The reminder of the paper is structured as the following. Section 2 will briefly analyse the macroeconomic performance of China's foreign trade in the post war era. China's composition of exports and imports will be examined in two parts: (1) The geographical composition of China's foreign trade, and (2) the commodity composition of China's foreign trade. The levels of the intra-industry trade are computed. The explanation of the data sources is given in the Appendix. The measurement will be performed across (a) China's trading partners, and (b) industries. The analysis of the factors which influence China's horizontal and vertical intraindustry trade pattern will be given. In Section 3, we give the results of regressions designed to test prevailing hypothesis about both partner-country and industry factors affecting the share of intra-industry trade. There are two sets of regressions involved. First, across trading 1

3 partners and then across 3-digit manufacturing industries. In the first set of regressions, We examine whether the relationship which has been found among industrial countries between intra-industry trade (both vertical and horizontal) and various determinants holds for trade between China and her major trading partners. Meanwhile, since theoretical explanations of intra-industry trade have focused on the presence of product differentiation and economies of scale, surrogates for these variable are prominent in the second set of regression which is within the cross-industry frame. Finally, conclusion is given is Section The General Performance of China's International Trade In the first decade the post war era, China remained largely closed to the West. Foreign trade was neglected and export earnings grew slowly because Government pursued a relatively autarkic strategy based on the Stalinist model and a belief in "self reliance" (Wang, 1992). Since the late 1970s, in an attempt to modernise and develop their economy, the Chinese Government has pursued an "open door" policy. The important role of foreign trade in the economy has begun to be acknowledged by the policy-makers. It is found that during the years , China greatly expanded the volume of international trade, broadened the range of her trading partners and diversified the commodities she sold. The ratio of exports and imports to GDP has been rising consistently over time. It is also shown that manufactured goods account for a large percentage in Chinese exports (Table 1). The open door policy has stimulated China's economic relations with not only Hong Kong and Japan, but also the USA, the EU, and other developed and developing countries (Table 2). As China has become an increasingly active player in the international economy, her share in world trade increased from 0.9% in 1980 to 2.9% in 1995 with her ranking in the world table rising from 26 to 11 for the 1 corresponding years. 1. Statistical Yearbook of China,

4 2.1 The Measurement of Intra-Industry Trade The significance of intra-industry trade in the international trade, the level of the intensity of the phenomenon, is defined by the Grubel and Lloyd (1975) index: B k '[1& X k &M k X k %M k ] 100 (1) where B is the intra-industry trade index for industry k, and X and M are exports and k k k imports in industry k valued at home country's currency. Thus, the Grubel and Lloyd index in equation (1) measures the intensity or proportion of intra-industry trade in branch k. To give an overall measure of IIT across industries, Grubel and Lloyd (1975) propose the following weighted IIT index: n B k ' E k'1 B k n n E (X k %M k )& E X k &M k X k %M k k'1 k'1 x100' x100 n n (2) E (X k %M k ) k'1 E (X k %M k ) k'1 where n is the number of industries at a chosen level of aggregation. This index measures average IIT directly as a percentage of the export plus import trade. However, Grubel and Lloyd observed that the above index (2) is a downward biased measure of IIT in presence of an imbalanced country's commodities trade or when the index refers to a subset of industries whose exports and imports are not balanced. They therefore adjust the index by the following formula: n n E (X k %M k )& E X k &M k k'1 k'1 C k ' x100 (3) n E k'1 n (X k %M k )& E (X k &M k ) k'1 3

5 The adjusted IIT index (3) is constructed by subtracting the total trade imbalance from the total trade volume. Consequently it is measured with respect to the total balanced trade Variation of China's IIT across Countries In this section we discuss the China's intra-industry trade with her major trading partners, including both low and high income countries. From the theoretical explanations that have been offered for intra-industry trade, its presence is said to be unlikely in developing countries. Therefore, very few studies deal with developing countries. Some rare examples are: Tharakan (1984), Manrique (1987), Balassa and Bauwens (1987), and Tharakan and Kerstens (1995). However, hardly any attempt has been made to disentangle the vertical and horizontal intra-industry trade since the pioneering work by Greenaway et al (1994). This study, therefore, will add some new elements to previous studies. First, we computed China's bilateral intra-industry trade indices with its 45 trading partners aggregated over industrial groups of SITC 5 to SITC 8 by using the G&L adjusted index of intra-industry trade (equation 3 in our context). To overcome the notorious categorical problem, we followed the procedure advanced by Greenaway and Milner (1983). That is, instead of summing exports an imports at the third digit and taking their absolute difference for the numerator, we summed the individual third digit imbalance to obtain the numerator. Second, China's bilateral vertical and horizontal intra-industry trade indices have been identified by the unit value. Namely, horizontal intra-industry trade is defined as the simultaneous export and import of the 3-digit SITC products where the unit value of exports (measured f.o.b) relative to the unit value imports (measured c.i.f) was within a range of 25%. Where the relative unit values were outside this range, the intra-industry trade is considered to be vertical. The rationale of using the unit value as the criteria to classify the vertical and horizontal intra-industry trade and the theoretical underpinning of the vertical and horizontal intra-industry trade itself have been propounded by other researchers, such as Flam and Helpman (1987), Falvery and Kierzkowski (1987), and Greenaway et al (1994). We used the range of 25% to allow the size of China 4

6 2 play a more important role in explaining the variation of the unit cost. We look first at how the intra-industry trade index varies across different trading partners for the four main manufacturing sector. Calculated values of China's IIT indices [by equation (3)] with her major trading partner countries for chemicals (SITC 3), manufactured goods classified chiefly by material (SITC 6), machinery and transport equipment (SITC 7), miscellaneous manufactured articles (SITC 8), all at the 3-digit level, are given in Table 3 for the year 1995, subject to the data availability. The differences of total IIT indices among countries are quite pronounced. The indices range from as low as 3.5% (with Sri Lanka) to as high as 85.4% (with Hong Kong). In general, those countries appear at the higher end of the scale include Macau (77.7%), Argentina (62.5%) and Switzerland (62.5%). The high proportion of IIT in China's total trade with Hong Kong can be explained by their particularly closed relationship. First, Hong Kong has been a major entrepot through which China can sell exports and procure imports. Of the China's exports to Hong Kong, only about 40% were consumed in the territory, while the remaining 60% were re-exported to third countries. Similarly, about 84.5% of China's imports from Hong Kong came from outside the territory (Wang, 1992). These transit trade appears in the imports and exports of China, could therefore treated as IIT. The second reason which may account for the high IIT index between China and Hong Kong may be the cultural and language similarity. 98% of the population in Hong Kong are ethnic Chinese, so that the demand for characteristically Chinese consumer goods is sizable. Many of such goods are produced and consumed on both sides of the border. Table 3 also decomposes the total IIT index into vertical and horizontal indices. It shows that the vertical IIT dominates the Chinese IIT. This indicates that the Chinese IIT is more 2. Greenaway et al (1994) chose a range of 15% for the UK. We increase this range to 25% as we consider that China is such a vast country that we should provide a larger scope for the transport costs to account for the variation in her exports and imports unit values. 5

7 differentiated by quality than preference between similar products. It also shows that there still exists a big technological gap between China and developed countries in the manufacturing export sector. 2.3 Variation of China's IIT across Industries Table 4 shows that the level of China's IIT varies greatly across industries. China's IIT with the rest of the world rises from 0.01% for some industries to 97.8% for office machine parts (SITC 759). Whilst the China's IIT with the UK can be as high as 86.9% for automatic data processing equipment (SITC 752). The policies taken to change from a planned economy to a market one have had different effects on particular branches of industry; this, of course, influenced the level of IIT. For example, the IIT (with the rest of the world) index is impressively high in SITC 759 (office machine parts 97.8%), SITC 655(Kinteed fabrics, 96.4%) and SITC 772 (switch gear, 92.7%). The IIT with the UK, although is lower in general, can also find high index numbers such as Automatic data processing equipment of SITC 752 (86.9%). This, however, can not be simply explained by Krugman's (1980) hypothesis that intra-industry trade is largely caused by product differentiation triggered by different tastes, rather it is due to the particular and historically determined unique characteristics of China's economic structure; in particular, the very partial operation of the market mechanism, with a number of separately protected local and regional markets. Since 1978, aggregate production and demand have been increasingly connected by the market mechanism. Producers in private and township enterprises driven by profit respond more quickly to the market than state enterprises do. Investment in those industries relating to consumer goods, such as textiles and other light industries, expanded considerably, which increased the variety of commodities available in the domestic market. At the same time, the increase of income expanded demand. With the increase of income and the demonstration effect brought about by the open-door policy, the growth of demand for durable consumer goods represented by electronics and sound equipment speeded up. 6

8 Moreover, the strong demonstration effect led to the appearance of high-grade consumption in China, which means that the increase of spending on new durable consumer goods has been quicker than the increase of income. To put it another way, their income elasticity of demand has exceeded one. This phenomenon is known as "consumption ahead of time" in China. This change of consumption pattern has had two effects on China's economy: on the one hand, it has brought a direct increase in imports; on the other, it has stimulated the creation of domestic industries producing up-to-date durable consumer goods with the help of the foreign direct investment. However, because of the particular difficulties confronted by the reform, China's import profile reflects the fact that new production capacity in any one of the activities is often widely dispersed in small scale units. For example, among 80 washing-machine enterprises, only 14 can produce as many as 20,000 units annually, thus reaching the minimum efficient scale. There are 13 enterprises producing cars in China, with total production capacity of 87,000 a year, but less than 30% of the Chinese car market is provided by 3 domestic production. Generally speaking, at the beginning of the development of new industries in a relatively freemarket economy, many small firms or plants will appear. In a thoroughgoing market economy, economies of scale would then be realized by competition which would lead to exits, mergers and concentration. What happened in China was that there was some competition but few merging. Moreover, enterprises faced no threat of becoming bankrupt before 1992 because all the plants and enterprises belonged to different administrative sectors or different administrative regions. The fear of political and social instability exacerbate the situation. Even though the Bankruptcy Law was promulgated in 1986, the rate of bankruptcy of the state-own 4 enterprises had been around 1% until The duplication of new production lines across administrative units has led to an excess of production capacity, compounded with the declining propensity to consume in recent years. Within China, there are 900 main product 3. The Structural Problem in Industrial Growth, Sichuan Peoples Press, p.105, China, 1988 (in Chinese). 4. China Economic Daily, 20, July,

9 groups, however, only half of them have reached 60% capacity. In some industries, this figure is even lower, for example, the utilization rate within the air-conditioning industry is 30%, washing machines industry 40%. In the textile industry, there is an excess of 40% over the domestic demand; and in television industry, this figure is 60%; 75% for the electronics 5 industry. As the market is still in a immature stage in China, very often an enterprise has to not only complete the whole process of production of a single product, but also build up its own energy-supply system and other necessary accessories. Such arrangements produce conflicts with those that would follow from the free flow of market forces. This results in a distinctive pattern of IIT in China. While some enterprises experiencing excess capacity for the domestic market re-exported their equipment, others in different administrative regions were still importing the same lines of equipment. 3. Testing for the Determinants of Intra-Industry Trade In this section, we test for the determinants of the level of intra-industry trade between China and her major trading partners through cross-section regression analysis of the 3-digit SITC data (1995). We examine whether the hypothesised relationships between various determinants and intra-industry trade among industrial countries hold for trade between China and her major partners. There is no a priori reason to expect the hypothesised relationships not to hold, although differences are likely to exist among China's major trading partners since the structure of China's trade will tend to vary among partner countries. 5. Chinese News Digest, Vol May,

10 3.1. The Hypotheses on the Determinants of Intra-Industry Trade Many factors have been proposed to explain the occurrence of intra-industry trade in the literature. In this paper, it is proposed to distinguish between country differences and industry differences in explaining the level of intra-industry trade. (a) Country Differences In the Level of Intra-industry Trade Among country differences, the IIT is expected to be positively correlated with the absolute level of a country's per capita income or the difference between country's development level (as measured by the absolute differences in their per capita income); the importance of manufactures among exports, the human-capital intensity. It is expected to be negatively correlated with the similarity of income distribution. There are two alternative hypotheses about the role of the market size and foreign direct investment. They could be either 6 positively or negatively correlated with intra-industry trade (Caves, 1981, Balassa, 1986). (b) Industry Difference in the Level of Intra-industry Trade The industry hypotheses postulate that intra-industry trade will be high if the degree or potential of product differentiation is high (Krugman, 1980); if the potential for economies of scale is high; if transport costs for the industry are low; and if the degree of industry 6. For a detailed discussion, see Section 3.3(a). 9

11 aggregation is high (Greenaway, 1984) The Method of Estimation In the preceding section, some hypotheses were advanced to account for the China's intraindustry trade with her major trading partners, including both low and high income countries. These hypotheses can be tested in two sets of regression models given as follows. (1) Intra-industry trade across partner countries: IIT ij ' " i0 % j m " im Z ijm % U ij, where i'h,v (4) where " and " are coefficients and U is an error term. i0 im ij There are two set of regressions involved. In the first one, the horizontal intra-industry trade index IIT (i=h), between China and her trading partner country (j) depends on a set of hj country characteristic variables Z, which are the conventional factors influencing intrahjm industry trade. They include income level as measured by GDP per capita (GDP); the percentage of manufactured export products among a partner country's exports (EX-SHARE); the market size measured by population; the Hufbauer index (HUF), a proxy for product differentiation. Our calculation of the Hufbauer index was based on the coefficient of variation for China's export unit values at the 3-digit SITC level. In the second regression of country difference, the dependent variable, IIT (i=v), is the vj 10

12 bilateral vertical intra-industry trade index between China and each of her major trading partners (j). Since vertical intra-industry trade is determined by quality factors, we believe that the human-capital intensity would be the most important factor influencing the this type of intra-industry trade. The explanatory variables we used are: foreign direct investment (FDI); the enrolment ratio of degree students in the particular age group (ENROL-RATIO); the share of education expenditure in GDP (ED-GDP); the income distribution dummy (INCOME DUMMY) which measures the similarity of income distribution in the countries concerned. Following Tharakan and Kerstens (1995), the value of 1 is given to the cases where the ratio between the average Gini coefficient of a partner country and China falls into the range of 1.1 & 0.9 and the value of 0 is given to the rest cases. Both the IIT hj and IIT vj are computed by using the G&L aggregation measure explained in equation (3). (2) Intra-industry trade across industries, with a particular partner country, the UK: IIT kj ' $ 0j % j n $ nj X knj % U kj (5) where $ and $ are coefficients and U is an error term. 0j nj kj Intra-industry trade between China and a particular partner (j=uk) across industries (k), IIT, kj depends on a set of variables (X ) reflecting industrial characteristics. These variables include: knj the degree of product differentiation, measured by the ratio of research and development (R&D) expenditures to sales and the innovation ratio; the extent of economies of scale, proxied by minimum efficient plant scale (MES), which was measured by the average size (shipments) of the largest plants accounting for (approximately) one-half of industry shipment, 11

13 divided by total industry shipments; the degree of industry aggregation, proxied by the internationally adjusted concentration ratio, which was derived by dividing the traditional fivefirm concentration ratio by the share of imports in the industry's domestic market. Equations (4) are estimated by ordinary-least-squares (OLS) method with linear specifications. Whilst equation (5) is estimated by a TOBIT method since there are quite a few observations of the dependent variable are truncated to zero Regression Results The explanation of data sources is given in the Appendix. The results of the regression analysis for the determinants of intra-industry trade between China and her major trading partners are given in Table 5 and Table 6. Overall, they give broad support to some of the hypotheses concerning the determinants of China's intra-industry trade. (a) Cross-country Regression Results Horizontal IIT regression results (Table 5A) In general, almost each variable has yielded the expected sign. The most important determinants of horizontal intra-industry trade according to the regression are the share of manufactured exports in total exports (EX-SHARE) and the Hufbauer index. The share of 12

14 manufactured exports in total exports has yielded the expected positive sign and is significant at the 5% level. The Hufbauer index (HUF), a proxy of product differentiation, has also generate a expected negative sign and is significant at the 10% level. Although the per-capita income has the expected sign, it is not significant. The result for per-capita income might possibly be explained by an interfering factor such as the similarity of income between China and the low-income partner countries, which might in itself encourage intra-industry trade between them. Two hypotheses for the roles of income in intra-industry trade give opposite predictions about the sign of the coefficient on GDP per capita: that it will be positive because rich countries have higher income and will hence, have more intra-industry trade, or that it will be negative because similar countries have more intra-industry trade. We find that the empirical results seem to support the first hypothesis rather than the second. That is, China's intra-industry trade is positively related to the absolute per-capita income level of the partner countries, not negatively related to the difference between the partner's income level and that of China. We now turn to discuss other variables. The size variable measured by population is insignificant but with a negative sign as expected. This result apparently differs from the findings of Loertscher and Wolter (1980) and Hine (1991), where the "size" variable has a significant positive coefficient. However, the measure they used was an average of the GDP of the two countries and not the absolute level of population, as in this study. The weak result for the size variable does not, however, mean that economies of scale are unimportant in determining the level of intra-industry trade in an industry. The expected negative sign 13

15 suggests at least that the bigger the economy at any given level of GDP per capita, the more variety of types that it can produce and the less need there is to undertake international trade in order to enjoy them. This empirical result happens to support MacCharles' (1987) hypothesis. Vertical IIT regression results (Table 5B) In the vertical intra-industry trade regression, we have also obtained the expected signs for all the coefficients. Both FDI and the share of education expenditure in GDP are significant at the 5% level and the income dummy is significant at the 10% level. These results clearly show that there are different factors influencing different kinds of intra-industry trade. The quality indicator, the human-capital intensity (proxied by the share of education expenditure in GDP), is a vital factor in explaining China's vertical bilateral intra-industry trade with its major trading partners. It supports the assertion that, the difference of human-capital intensity provides the different quality of goods or services and countries with different pattern of income distribution generated the demand for these quality differentiated goods so that intraindustry trade occurs even between the rich country and poor country (Tharakan and Kerstens, 1995). (b) Cross-Industry Regression Results The TOBIT regression results for the intra-industry trade between China and the UK at the 14

16 level of 3-digit SITC industries are summarised in Table 8. The dependent variable in the regression equation is the bilateral intra-industry trade index in The model does seem to provide a reasonable fit to the data: all the explanatory variables have the expected signs and all of them are significant at the 5% level. The coefficient on the inverse Mills ratio, F, is also significant at the 5% level. This indicates the importance to include those industries which IIT indices happen to be zeros. Otherwise, it will induce a sample selection bias in our estimation. Our regression includes a proxy for product differentiation: the ratio of research and development expenditures to sales (R&D) as a measure of the degree of product innovation. The result suggests that the R&D ratio has a strong direct bearing on levels of intra-industry trade in China. This result is contrary to that of Caves (1981) and Greenaway (1984), who found either insignificant coefficient estimates or a negative sign. In fact, the R&D factor may well work both as source of increased product differentiation leading to permanent intraindustry trade, and as a source of product-cycle, or transitory, patterns of intra-industry trade. In fact, the expected relationship between the industrial- concentration ratio and intra-industry trade is controversial. Some models have predicted that high levels of intra-industry trade would be found in highly concentrated oligopolistic industries (Caves, 1981). Intra-industry trade is thought of a stage in the international expansion of rival oligopolies. But a monopolistically-competitive market structure, in which a large number of firms sell a differentiated product, may equally well lead to intra-industry trade. Econometric research provides little evidence for a positive relationship between intra-industry trade and a 15

17 conventional measure of oligopoly. If the level of industrial concentration is proxying the degree of oligopoly, the coefficient would be positive. However, a negative relationship between industrial concentration and product differentiation would tend to lessen the positive relationship between industrial concentration and intra-industry trade. It is possibly the extent to which an industry is dominated by a few large firms at the world level which is important for intra-industry trade. Hence, Toh (1982) has adjusted the domestic concentration ratio for the share of imports in output of an industry so as to allow for competition from abroad. However, even after such an adjustment (as here), there is no clear-cut evidence for a positive relationship between intra-industry trade and the degree of industrial concentration. In fact, Balassa (1986) obtained a negative relationship between the level of intra-industry trade and the internationally adjusted degree of concentration. One possible explanation for this is that a falling internationally adjusted concentration ratio measures the level of oligopolistic rivalry prevalent in a particular industry. It is the evidence of the interpenetration of each other's markets by rival firms. His result is consistent with the theoretical expectations concerning the relationship between oligopoly and intra-industry trade. The expected negative sign and significant result in the present study provides support for the hypothesis of the positive relationship between a monopolistically-competitive market structure and intra-industry trade. Finally, there had been a theoretical expectation that the level of intra-industry trade would increase with the importance of economies of scale. Somewhat surprisingly, econometric research appears to conflict with such a hypothesis. Most studies show a negative relationship between economies of scale and the level of intra-industry trade. Grimwade (1990) argued 16

18 that this was because most studies had tested the relationship between the level of intraindustry trade and the economies of large plant size. The minimum efficient scale (MES) is frequently used in such studies as a proxy for economies of scale. Balassa (1986) pointed out that industries in which such economies of scale are important were often characterised by high levels of standardisation and a low degree of product differentiation; for example, petrochemicals, steel manufactures, and so on. Hence the level of intra-industry trade is quite low. Using the same variable and proxy, the present study has yielded the expected positive sign, and the coefficient is statistically significant at the 5% level. This result has supported the theoretical hypothesis that economies of scale is a crucial determinant of intra-industry trade. 4. Conclusion Analysis of China's trade data in this paper reveals that intra-industry trade is an important component of China's international trade in manufactured goods, and that China's intraindustry trade index varies significantly across the trading partners and across industries. The study shows that intra-industry trade takes place not only between China and developing countries with similar factor endowments and tastes, but also between China and developed countries. More importantly, it is demonstrated that one country's (in our case, China) intraindustry trade may be disentangled into vertical and horizontal types which are influenced by different factors: in the vertical intra-industry trade, it is the human-capital intensity which creates the quality difference that determines the level of such trade. On the other hand, however, it is the product differentiation and economy of scale that determines the level and 17

19 scope of the horizontal intra-industry trade, which may be fitted into the "neo-chamberlinian" trade model. Most interestingly, China's intra-industry trade has some different features from that of industrialised countries. On the one hand, China's intra-industry trade is to a great extent complementary (within the same industry, there are imports from one group of countries and simultaneous exports to another group of countries). On the other hand, thanks to the weak vertical specialisation and the particular administrative system, whilst some enterprises experiencing excess capacity for the domestic market re-export their equipment or products, the others in different administrative regions are still importing the same lines of equipment or products. Our analysis has attempted to test for the determinants of intra-industry trade in the context of China's economy. The results overall conform, to some extent, with a priori expectations and can be interpreted as offering some empirical validation to the explanatory variables which have been suggested in the literature. It was found from the cross-country regression results, the share of manufactured goods in exports and the Hufbauer index are the most important factors to stimulate China's intra-industry trade. The GDP variable is of the most interest as it appears to support the assertion that some of China's intra-industry trade is initiated by her partner country's high income even though China herself shares more economic characteristics with developing countries than with developed countries. In the cross-industry regression, the R&D ratio (as a proxy for product differentiation) and the minimum efficient scale ( as a proxy for economies of scale) have shown their significance. All these have revealed that China has possessed the prerequisite of intra-industry trade and that China's intra-industry trade follows 18

20 the similar, though not exactly the identical, patterns of those in developed countries as China is moving towards a market-oriented economy. Appendix. Data Sources Cross-Country Data In this study, China's annual foreign-trade values with her major trading-partner countries for 1995, classified according the SITC system and broken down according to country of origin (for imports) and destination (for exports), were used to compute the G & L aggregation index, which was explained in equation (3). The data on exports and imports are derived from UN: International Trade Statistics, Trade by Country, 1995 (Microfiche). The cross-country model includes 45 countries with different economic structure. The selection of these country sample is subject to their data availability and is based the information provided in the UN database: World Atlas (China), Data on per-capita income and the share of manufactured goods in total exports, the enrolment ratio of degree students in the particular age group, and the share of education expenditure in GDP come directly from UNCTAD: Handbook of International Trade and Development Statistics, 1995, and the average GINI indices are from Deininger and Squire (1996) and the Word Bank: World Development Report,

21 Cross-Industry Data The size and industry composition of the sample chosen for the cross-industry study is determined by two criteria. First, each selected industry in the 3 & 4-digit UK SIC has to be defined comparably to one in the 3-digit SITC in order to ensure data convertibility between these two classification systems (Greenaway and Milner, 1983). Second, the selected industry must have all the industrial- characteristic data required for the empirical tests. Following these criteria, this study included 31 manufacturing industries. The dependent variable, the 3- digit intra-industry-trade index (IIT), is calculated from UN: International Trade Statistics, Trade by Countries, The explanatory variables, R& D ratio and innovation ratio, are derived from OECD: Science and Technology Indicators, No.2: R&D, Invention and Competitiveness, Paris, OECD, 1986 (as a proxy for the 1995 data). The other two explanatory variables, minimum efficient scale (MES) and the internationally adjusted five-firm concentration ratio, are obtained from the Report on the Census of Production, References Balassa, B. (1986), The Determinant of Intra-industry Specialization in US Trade, Oxford Economic Papers, Vol. 38, pp Balassa, B. and L Bauwens (1987), Intra-Industry Specialisation in Multi-country and Multiindustry Framework, Economic Journal, Vol.97, pp

22 Caves, R.E.(1981), Intra-industry Trade and Market Structure in the Industrial Countries, Oxford Economic Papers, Vol. 33, pp Deininger, K and L Squire (1996) A New Data Set Measuring Income Inequality, The World Bank Economic Review, Vol.10(3), September. Falvery, R.E, and H. Kierzkowski (1987), Product Quality, Intra-industry Trade and (Im)perfect Competition, In: H. Kierzkowske (ed) Protection and Competition in International Trade: Essays in Honour of W.M. Cordon, Oxford, Basil Blackwell, pp Flam, H. and E. Helpman (1987), Vertical Product Differentiation and North-South Trade, American Economic Review, Vol. 77, pp Greenaway, David (1984), A Cross Section Analysis of Intra-industry Trade in the UK, European Economic Review, Vol. 25, pp Greenaway, David and C R Milner (1983), On the Measurement of Intra-industry Trade,Economic Journal, Vol. 93, pp Greenaway, David, R.C. Hine and C.R. Milner (1994), Country-Specific Factors and the Pattern of Horizontal and Vertical Intra-Industry Trade in the UK, Weltwirtschaftliches Archiv, Vol. 130, Grimwade, N (1990) International Trade: New Patterns of Trade, Production and Investment, Routledge, London. Grubel,H.G and D.J.Lloyd (1975) Intra-industry Trade, The Theory and Measurement of International Trade in Differentiated Products, Macmillan, London. Hine, Robert (1991) Specialization of Manufacturing Industry in European Economic Space, 21

23 International Journal of Manpower, Vol. 12 No. 2, pp Krugman, P.R. (1980) Scale Economies, Product Differentiation and the Pattern of Trade, American Economic Review, Vol. 70, No.5, pp Loertscher, R. and F. Wolter (1980) Determinants of Intra-industry Trade: Among Countries and Across Industries, Weltwirtschaftliches Archiv, Vol. 116, pp MacCharles, Donald C (1987) Trade Among Multinationals: Intra-Industry Trade and National Competitiveness, Croom Helm Ltd. Manrique, G (1987) Intra-Industry Trade between Developed and Developing Countries: the United States and the NICs, Journal of Development Areas, Vol.21, pp Tharakan, P.K.M.(1984) Intra-industry Trade between the Industrial Countries and the Developing World, European Economic Review, Vol. 25, pp Tharakan, P.K. M. Matthew and Birgit Kerstens (1995), Does North-South Horizontal Intra- Industry Trade Really Exist? An Analysis of the Toy Industry, Weltwirtschaftliches Archiv, Vol.131, pp Toh, K (1982) A Cross-Section Analysis of Intra-Industry Trade in US manufacturing Industries, Weltwirtschaftliches Archiv, Vol. 118, pp Wang, Hong (1992) China's Export Promotion since 1978, Cambridge. 22

24 Table 1. International Trade of China Year Exports/GDP Imports/GDP Share of Manufactured Goods in Exports (%) (%) (%) )))))))))))))))))))))))))))))))))))))))))))))) )))))))))))))))))))))))))))))))))))))))))))))) Sources: Figures for are from: UN, Handbook of International Trade and Development Statistics, pp. 143, 1992 and pp , 1993; figures for 1993 to 1995 are computed using data from UN: International Trade Statistics, 1995 and IMF: International Financial Statistics,

25 Table 2. China's Trade by Selected Exports Destinations and Imports Sources (%) Share of China's Share of China's Share of Total Exports Imports Trade Partner Japan USA Hong Kong the EU Australia a NIEs b ASEAN Rest of World World Total Notes: a NIEs-3 comprises Singapore, Taiwan and the Republic of Korea. b ASEAN-4 comprises Philippines, Indonesia, Malaysia and Thailand. Source : IMF Direction of Trade,

26 Table 3. China's Intra-Industry Trade Index with Her Major Trading Partners in 1995 (SITC 3-digit level, %) PARTNER Total Vertical Horizontal PARTNER Total Vertical Horizontal COUNTRY IIT IIT IIT COUNTRY IIT IIT IIT AUSTRALIA MACAU AUSTRIA MALAYSIA BENELUX MEXICO BANGLADESH NEW-ZEALAND BRAZIL NORWAY CANADA PAKISTAN CHILE PANAMA DENMARK PERU EGYPT PHILIPPINES FINLAND PORTUGAL FORMER USSR SOUTH-KOREA FRANCE SINGAPORE GERMANY SOUTH-AFRICA GREECE SPAIN HOLLAND SRI LANKA HONG KONG SWEDEN HUNGARY SWITZERLAND INDIA THAILAND INDONESIA TURKEY IRELAND UK ISRAEL USA ITALY VIETNAM JAPAN Sources: UN International Trade Statistics, Trade by Countries, Series C, 1995 & Trade Statistics for Pacific Countries,

27 Table 4. China's Intra-Industry Trade in 1995 (All Matched 3-digit SITC, %) CODE DESCRIPTION IIT with the IIT with the UK rest of the world 512 Organic chemicals Carbonylic Acids etc Nitrogen-function compounds Organic-inorganic compounds etc Dyes, tanning Medical pharmacological product Leather manufactures Wood Cork manufactures Textile yarn Cotton fabrics, woven Knitted, etc. fabrics Iron, steel Iron, steel, plate, sheet Aluminium Textile, leather machinery Automatic data processing equipment Office machine parts Telecom equipment Electric power machine Switch gear etc Electricity distributing equipment Electronic machine Transistors, valves, etc Electrical machinery Aircraft Ships & boats Photo Apparatus Watches & Clocks Toys, sporting goods, etc Other manufactured goods Sources: UN: International Trade Statistics, Trade by Country,

28 Table 5A. China's Horizontal IIT Cross-Country OLS Regression Results (1995) No. of observations : 45 (see Table 3 for country names) Dependent Variable : IIT (China's horizontal intra-industry trade with partner countries) Regressor Coefficient Standard Error T-Statistics P-value CONSTANT [.445] GDP [.927] EX-SHARE ** [.050] HUF * [.071] POPULATION [.318] 2 (Standard Errors are heteroscedastic-consistent) R = Notes: * : significant at the 10% level, ** : significant at the 5% level. GDP: GDP per capita in US$ EX-SHARE: share of manufactured exports in total exports HUF: Hufbauer index (a measure of product differentiation) Table 5B. China's Vertical IIT Cross-Country OLS Regression Results (1995) No. of observations : 45 (see Table 3 for country names) Dependent Variable : IIT (China's vertical intra-industry trade with partner countries) Regressor Coefficient Standard Error T-Statistics P-value CONSTANT [.215] FDI ** [.024] ENROL-RATIO [.917] ED-SHARE ** [.050] INCOME DUMMY * [.078] (Standard Errors are heteroscedastic-consistent) R = Notes: * : significant at the 10% level, ** : significant at the 5% level. FDI: China's inward foreign direct investment from the partner countries, in US$; ENROL-RATIO: the enrolment ratio of degrees students in the schooling-age youth. ED-SHARE: the share of education expenditure in GDP; INCOME DUMMY: the income distribution dummy which measures the similarity of income distribution in the countries concerned. 27

29 Table 6. China-UK IIT Cross-Industry Tobit Regression Results (1995) Dependent Variable : IIT No. of observations : 31 (see Table 4 for industry names) Parameter Estimate Standard Error T-Statistics P-value CONSTANT [.142] R&D ** [.043] MES ** [.001] CONCENTRATION ** [.003] SIGMA ** [.000] (Standard Errors are heteroscedastic-consistent) Notes: * : significant at the 10% level, ** : significant at the 5% level. R&D: the ratio of R&D expenditures to sales CONCENTRATION: the five-firm concentration ratio MES: the minimum efficient scale SIGMA: the inverse Mills ratio 28