Published in Weltwirtschaftliches Archiv (Review of World Economics), Jan. 1999, Vol.135,

No.1, pp.82-101, Kiel Institute for World Economics, Germany.

International Intra-Industry Trade of China *

Xiaoling Hu and Yue Maa b

Southampton Institute of Higher Education and Portsmouth University, UKa

Lingnan University, Hong Kongb

Corresponding author and address:

Dr Yue Ma

Dept of Economics, Lingnan University, Tuen Mun, Hong Kong

Fax: + (852) 2891 7940; Tel (852)2616 7202; Email: yuema@Ln.edu.hk

Internet: http://www.Ln.edu.hk/econ/staff/yuema

* 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 intra-

industry 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.

1

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 intra-

industry 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 intra-

industry 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. Statistical Yearbook of China, 1995.

2

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 4.

2. 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 1979-1996, 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

corresponding years. 1

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3

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:

where B is the intra-industry trade index for industry k, and X and M are exports andk 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:

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:

4

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.

2.2. 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

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 thetransport costs to account for the variation in her exports and imports unit values.

5

play a more important role in explaining the variation of the unit cost .2

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

6

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.

3. The Structural Problem in Industrial Growth, Sichuan Peopleís Press, p.105, China, 1988(in Chinese).

4. China Economic Daily, 20, July, 1997.

7

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

domestic production . 3

Generally speaking, at the beginning of the development of new industries in a relatively free-

market 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

enterprises had been around 1% until 1996 . The duplication of new production lines across4

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

5. Chinese News Digest, Vol. 370. 1 May, 1998.

8

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

industry . 5

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.

6. For a detailed discussion, see Section 3.3(a).

9

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

positively or negatively correlated with intra-industry trade (Caves, 1981, Balassa, 1986).6

(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

IITij' "i0 % j m"imZijm% Uij , where i'h,v (4)

10

aggregation is high (Greenaway, 1984).

3.2. The Method of Estimation

In the preceding section, some hypotheses were advanced to account for the China's intra-

industry 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:

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 ofhj

country characteristic variables Z , which are the conventional factors influencing intra-hjm

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 thevj

IITkj ' $0j % j n$njXknj% Ukj (5)

11

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 and IIT are computed byhj vj

using the G&L aggregation measure explained in equation (3).

(2) Intra-industry trade across industries, with a particular partner country, the UK:

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,

12

divided by total industry shipments; the degree of industry aggregation, proxied by the

internationally adjusted concentration ratio, which was derived by dividing the traditional five-

firm 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.

3.3. 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

13

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

14

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 intra-

industry 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

15

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 1995. 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 intra-

industry 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

16

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

17

that this was because most studies had tested the relationship between the level of intra-

industry 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 intra-

industry 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) intra-

industry 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

18

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

19

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), 1997. 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, 1997.

20

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, 1995. 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, 1995.

References

Balassa, B. (1986), The Determinant of Intra-industry Specialization in US Trade, Oxford

Economic Papers, Vol. 38, pp.220-33.

Balassa, B. and L Bauwens (1987), Intra-Industry Specialisation in Multi-country and Multi-

industry Framework, Economic Journal, Vol.97, pp.923-39.

21

Caves, R.E.(1981), Intra-industry Trade and Market Structure in the Industrial Countries,

Oxford Economic Papers, Vol. 33, pp. 203-33.

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.

143-161.

Flam, H. and E. Helpman (1987), Vertical Product Differentiation and North-South Trade,

American Economic Review, Vol. 77, pp. 810-822.

Greenaway, David (1984), A Cross Section Analysis of Intra-industry Trade in the UK,

European Economic Review, Vol. 25, pp. 39-57.

Greenaway, David and C R Milner (1983), On the Measurement of Intra-industry

Trade,Economic Journal, Vol. 93, pp. 900-8.

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, 99. 77-100.

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,

22

International Journal of Manpower, Vol. 12 No. 2, pp. 43-51.

Krugman, P.R. (1980) Scale Economies, Product Differentiation and the Pattern of Trade,

American Economic Review, Vol. 70, No.5, pp.950-9.

Loertscher, R. and F. Wolter (1980) Determinants of Intra-industry Trade: Among Countries

and Across Industries, Weltwirtschaftliches Archiv, Vol. 116, pp.280-93.

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, pp481-494.

Tharakan, P.K.M.(1984) Intra-industry Trade between the Industrial Countries and the

Developing World, European Economic Review, Vol. 25, pp. 213-27.

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. 86-105.

Toh, K (1982) A Cross-Section Analysis of Intra-Industry Trade in US manufacturing

Industries, Weltwirtschaftliches Archiv, Vol. 118, pp. 282-301.

Wang, Hong (1992) China's Export Promotion since 1978, Cambridge.

23

Table 1. International Trade of China

Year Exports/GDP Imports/GDP Share of Manufactured Goods in Exports (%) (%) (%)

))))))))))))))))))))))))))))))))))))))))))))))1980 6.1 6.7 47.51985 9.5 14.8 35.91990 16.9 14.6 61.61991 19.0 16.8 75.71992 19.2 18.2 78.71993 18.0 19.6 80.6 1994 19.9 19.0 82.31995 21.9 19.0 85.2)))))))))))))))))))))))))))))))))))))))))))))) Sources: Figures for 1980-1992 are from: UN, Handbook of International Trade andDevelopment Statistics, pp. 143, 1992 and pp. 143-149, 1993; figures for 1993 to 1995 arecomputed using data from UN: International Trade Statistics, 1995 and IMF: InternationalFinancial Statistics, 1996.

24

Table 2. China's Trade by Selected Exports Destinations and Imports Sources (%)

Share of China's Share of China's Share of TotalExports Imports Trade

Partner 1985 1990 1995 1985 1990 1995 1985 1990 1995

Japan 22.3 14.6 19.1 35.7 14.2 22.0 30.4 14.4 20.5

USA 8.5 8.5 16.6 12.2 12.2 12.2 10.8 10.2 14.6

Hong 26.2 43.2 24.2 11.2 6.5 6.5 17.0 35.7 15.9Kong

the EU 8.7 10.0 12.9 15.8 17.0 16.1 13.0 13.2 14.4

Australia 0.7 0.7 1.1 2.6 2.5 2.0 1.9 1.6 1.5

NIEs-3 7.6 4.4 8.9 2.8 5.8 21.6 4.7 5.0 14.9a

ASEAN-4 2.7 2.9 3.7 2.1 4.0 4.5 2.3 3.4 4.1b

Rest of 23.3 15.7 13.5 17.6 17.3 15.1 19.8 16.4 14.2World

World 100 100 100 100 100 100 100 100 100Total

Notes: NIEs-3 comprises Singapore, Taiwan and the Republic of Korea.a

ASEAN-4 comprises Philippines, Indonesia, Malaysia and Thailand.b

Source : IMF Direction of Trade, 1980-1995

25

Table 3. China's Intra-Industry Trade Index with Her Major Trading Partnersin 1995 (SITC 3-digit level, %)

PARTNER Total Vertical Horizontal PARTNER Total Vertical HorizontalCOUNTRY IIT IIT IIT COUNTRY IIT IIT IIT

AUSTRALIA 42.1 30.7 10.3 MACAU 77.7 56.4 21AUSTRIA 36 36.0 0.03 MALAYSIA 33.7 27.2 6.8BENELUX 31.4 25.8 5.6 MEXICO 57.2 57.2 0.02BANGLADESH 4 2 2 NEW-ZEALAND 10.6 8.5 2.1BRAZIL 18.5 18.4 0.07 NORWAY 35.6 35.6 0.01CANADA 35.2 18 17.2 PAKISTAN 17.6 11.9 5.7CHILE 4.0 3.9 0.08 PANAMA 4.1 4.1 0.03DENMARK 35.6 34.3 1.3 PERU 27.4 23.5 3.9EGYPT 5.8 5.7 0.09 PHILIPPINES 36.8 30.9 5.9FINLAND 8.3 8.3 8.3 PORTUGAL 14.7 13.1 1.6FORMER USSR 8.4 4.4 4 SOUTH-KOREA 39.4 33.8 5.6FRANCE 12.9 10.9 2 SINGAPORE 17.2 15.4 1.8GERMANY 16.4 14.3 2.1 SOUTH-AFRICA 16.8 14.5 2.3GREECE 30.1 18.1 12 SPAIN 49.6 44.1 5.5HOLLAND 31.4 24.8 6.6 SRI LANKA 3.5 2.8 0.7HONG KONG 85.4 59.1 26.3 SWEDEN 22.7 21.2 1.5HUNGARY 28.1 28.1 0.04 SWITZERLAND 62.2 57.5 4.7INDIA 32.7 17.5 8.2 THAILAND 54.2 44.1 10INDONESIA 32.4 29.3 3.9 TURKEY 24.7 21.1 3.5IRELAND 34.7 28.4 6.3 UK 55.4 49.5 5.9ISRAEL 57.3 57.2 0.07 USA 26 18.7 7.4ITALY 45.7 36.7 8.9 VIETNAM 6.8 5 1.8JAPAN 28.1 24.8 3.3

Sources: UN International Trade Statistics, Trade by Countries, Series C, 1995 & TradeStatistics for Pacific Countries, 1995.

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Table 4. China's Intra-Industry Trade in 1995 (All Matched 3-digit SITC, %)

CODE DESCRIPTION IIT with the IIT with the UKrest of theworld

512 Organic chemicals 0.02 38.8513 Carbonylic Acids etc. 70.6 68.9514 Nitrogen-function compounds 90.6 0515 Organic-inorganic compounds etc. 85.4 0533 Dyes, tanning 0.01 9.7541 Medical pharmacological product 40.9 37.3

612 Leather manufactures 81.2 0.05625 Wood 0.01 8.3635 Cork manufactures 0.02 14.6651 Textile yarn 92.4 3.9652 Cotton fabrics, woven 61.3 0.4655 Knitted, etc. fabrics 96.4 0671 Iron, steel 0.03 33.9674 Iron, steel, plate, sheet 41.4 0684 Aluminium 58.8 56.6

724 Textile, leather machinery 22.6 0752 Automatic data processing equipment 62.7 86.9759 Office machine parts 97.8 28.6764 Telecom equipment 73.9 0771 Electric power machine 68.0 22.1772 Switch gear etc. 92.7 33.4773 Electricity distributing equipment 84.9 38.6775 Electronic machine 0.01 32.9776 Transistors, valves, etc. 50.1 37.8778 Electrical machinery 87.2 52.1792 Aircraft 18.9 0793 Ships & boats 89.2 0

881 Photo Apparatus 77.7 0.3885 Watches & Clocks 73.0 0.2894 Toys, sporting goods, etc. 14.9 0.1899 Other manufactured goods 28.5 0

Sources: UN: International Trade Statistics, Trade by Country, 1995.

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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 4.71557 6.10530 .772373 [.445]GDP .042818 .464388 .092203 [.927]EX-SHARE .074206 .038073 1.99001** [.050]HUF 1.26760 .684061 1.85305* [.071]POPULATION -.720175 .711603 -1.01205 [.318]

(Standard Errors are heteroscedastic-consistent) R = 0.1952

------------------------------------------------------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 exportsHUF: 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 8.70526 6.90482 1.26075 [.215]FDI 2.08944 0.88810 2.35271** [.024]ENROL-RATIO 0.01239 0.11800 0.10501 [.917]ED-SHARE 0.38851 0.19914 1.96093** [.050]INCOME DUMMY -8.35616 4.62631 -1.80622* [.078]

(Standard Errors are heteroscedastic-consistent) R = 0.1852

--------------------------------------------------------------------------------------------------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 incomedistribution in the countries concerned.

28

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 75.8938 51.6965 1.46807 [.142]R&D 4.47343 2.20546 2.02835 ** [.043]MES 0.13443 0.03919 3.43007 ** [.001]CONCENTRATION -4.71452 1.59628 -2.95344 ** [.003]SIGMA 59.3185 12.7075 4.66799 ** [.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 scaleSIGMA: the inverse Mills ratio