Changes in Product Quality in Marginal Intra- Industry Trade and Adjustment: The China and ASEAN Experience

Transcription

1 Changes in Product Quality in Marginal Intra- Industry Trade and Adjustment: The China and ASEAN Experience A.K.M. Azhar* Graduate School of Management, Universiti Putra, UPM, Serdang, Selangor, Malaysia. Tel: Robert J.R. Elliott Department of Economics, University of Birmingham, Edgbaston, Birmingham, UK r.j.elliott@bham.ac.uk. Tel: Junting Liu Department of Economics, Liaoning University, Shenyang City. Liaoning Province. P.R. China junting_liu@hotmail.com This work is very preliminary. Please do not quote or cite. Abstract In recent years China s rapid industrialisation and export lead growth has led to fears that significant competitive pressures are being exerted on the exports from the rest of East Asia and that they may struggle to adjust to China s low wages and increasingly efficient manufacturing sector. The common belief among East Asian economies is that to remain competitive they must move production up the value chain, that is, to improve the quality of their exports relative to those of China. In this paper we document and examine the differences in the quality of intra-asean trade flows. More specifically, we combine two strands of the recent international trade literature concerned with the measurement of intra-industry trade (IIT): measures of marginal or dynamic IIT that are concerned with the adjustment implications of changes in matched trade; and measures of vertical and horizontal intra-industry trade that are concerned with the differences in the quality of products that are imported and exported from the same industry. By applying a new dynamic measure of quality differentiated IIT we are able to show how the emergence of China has affected the quality of exports and imports to and from Malaysia, Singapore and Indonesia to China. Our results suggest that for the most part China s neighbours have benefited from China s increasing demand for imported goods and that all three countries have managed to maintain their position as the producer of high quality varieties. JEL classification: F19 Keywords: exports, intra-industry trade, adjustment costs, quality, product differentiation, ASEAN. Remark: A.K.M. Azhar is grateful to the Malaysian Ministry of Science, Technology and Innovation for funding grant number The usual disclaimer applies. 1

2 Introduction China s recent and rapid export driven growth has led to much soul searching among its East Asian neighbours as to whether China s growth presents an opportunity or a threat. One of the fears of policymakers in the ASEAN region is that domestic manufacturing firms will find it increasingly difficult to compete with China s low wage economy. The perceived wisdom is that if countries such as Malaysia, Singapore and Indonesia are to continue to compete successfully in the global market they will need to move existing production up the value chain, that is, to improve the quality of their manufacturing goods relative to those produced in China. The process by which product quality can be enhanced in ASEAN countries is not straightforward. China itself is already making great strides to broaden the quality range of its exports. In terms of human capital, China is also investing heavily and is currently producing 4.95 million graduates including an estimated 600,000 engineering graduates a year. 1 In addition, China has been very successful in attracting inward FDI from some of the most developed countries and from some of the most technologically advanced foreign multinationals. In 2004 and 2005 China attracted non-financial FDI of approximately $60 billion (UNCTAD 2007). China also has the benefit of large and increasing domestic consumption which will naturally benefit domestic firms who have the advantages of any home biased demand and better local knowledge. The primary concern for ASEAN countries, that themselves in the main grew on the back of export orientated development policies, is that in the face of such rapid Chinese growth, manufacturing jobs may be lost that in turn will lead to higher unemployment and associated social pressures. In economic growth terms the worry is that ASEAN countries will become caught in a middle income trap and be unable to make the step up to high-income status. On the other hand, as China continues to grow, a new and large middle class is emerging that constitutes an increasingly powerful and dynamic consumer society that will demand more goods, both domestically produced and imported. Moreover, the growth of the Chinese manufacturing sector will also mean an increase in demand for raw materials and intermediate inputs, a proportion of which are likely to be sourced 1 These figures come from the article in China Daily Job shortage to affect graduates and from Does the US face an engineering gap? 2

3 from abroad, offering ASEAN exporters an opportunity to grow their market share and thus enhance the employment prospects in their local economies. In this paper we outline three possible competing China-ASEAN trade affects: first, a scale effect whereby Chinese growth leads to an increase in demand for imports, many of which may be sourced from China s near neighbours (a potential increase in exports and possibly no change in imports); second, a composition effect whereby countries react to Chinese competition by changing the composition of the goods traded and produced where ASEAN countries may choose to exit the production of certain products in the face of Chinese competition (a potential decrease in exports and a possible increase in imports); third, a technique effect whereby other countries react to China s growth by altering the combination of factor inputs used in production and thus changing the quality of goods produced (which may require the country to move up or down the value chain). The implicit implication of the technique effect is that by altering the quality of the product produced, that net trade may be unaffected, but that such changes will manifest themselves in increases in the simultaneous import and export of goods from the same industry, commonly known as intra-industry trade (IIT) (exports and imports increase) in contrast to the scale and composition effects that are concerned with changes in net trade. In this paper we concentrate on the technique effect. The empirical approach that enables us to get a better understanding of the competing affects of China s growth on ASEAN production and trade patterns is to compare the quality of goods that are both imported to and exported from China. By studying differences in the quality of traded varieties of the same product over time we are able to identify whether a country has improved the quality of it exports to China relative to its imports from the same country. The method we employ in this paper is to measure intra-industry trade (the simultaneous import and export of goods from the same industry) and then use quantities and prices via the generation of crude unit values (UVs) to define quality where we assume that price is an indicator of quality (Stiglitz 1987). 2 We then split the intra-industry trade (IIT) flows into two components, vertically or quality differentiated IIT (VIIT) and horizontally differentiated IIT (HIIT) where the degree of the former 2 The premise for using UV s is that goods of a higher quality should demand a higher price (Stiglitz 1987). Thus, price can be considered an, albeit, imperfect indicator of quality. For further discussion see Greenaway et al. (1994) and Aiginger (1997). 3

4 provides an indicator of the differences in quality between traded goods from the same industry. However, a simple analysis of differences in product quality over time is only one part of story. Given the importance that policymakers place on possible trade induced adjustment costs, it is important to be able to measure how product quality has changed over time to be able to make inferences about the possible type and magnitude of adjustment costs that a country is likely to experience as a result of changes in the patterns of trade, specialisation and differences in the quality of the goods produced. Therefore, we calculate marginal quality (MQ) indices following Azhar and Elliott (2006c) that is essentially a dynamic measure of quality in IIT in the same way that marginal intra-industry trade (MIIT) is a dynamic measure of IIT. 3 In tandem with volume-based indices of matched trade we believe that such a measure provides an additional dimension to the analysis of where the most severe adjustment costs have fallen that reflect both changes in the volume of trade but also changes in the quality of the goods that are still traded allowing governments time to react, for example by providing support by way of funds to facilitate employee retraining or a more careful tailoring of strategic industrial policy. The contribution of this paper is therefore three-fold. First, we examine the patterns of intra-industry trade between China and: Malaysia; Singapore; and Indonesia to identify those products where IIT has grown significantly. Second, we disentangle vertical from horizontal IIT to examine how the quality of the products that constitute IIT flows differ. Third, we calculate an index of the change in quality in matched trade changes to provide an indication of how these three countries may have reacted to the increase in competition from China, whether this gives us any indication of what might happen in the future and what the policy implications may be. In doing so, this paper represents the first attempt to implement the three-stage empirical strategy outlined in Azhar and Elliott (2006c). The remainder of this paper is as follows: Section 2 reviews the literature in this area and describes the methodology. Section 3 provides a descriptive analysis of the data; Section 4 presents the results; Section 5 presents a discussion of the policy implications and concludes. 3 Various proposals for a measure of dynamic or marginal intra-industry trade have been suggested starting with Hamilton and Kniest (1991) followed by Greenaway et al. (1994), Brülhart (1994), Menon and Dixon (1997), Azhar et al. (1998) and Azhar and Elliott (2003). 4

5 Review of the Literature That changes in trade patterns can have adjustment costs is not contentious. See Davidson and Matusz (2001, 2004) and Bacchetta and Jansen (2003) for recent assessments of trade-induced adjustment costs. If, for example, there was a large change in inter-industry trade (net trade), such as a large fall in exports from a specific industry, then assuming there is no matched increase in domestic demand, the result will be a contraction of that industry that will usually be accompanied by the shedding of factors of production, most usually labour in the short run. Similarly, a large expansion in exports from a given industry ceteris paribus will still lead to adjustment costs as the increase in demand may mean workers need to be hired and trained and new machinery commissioned and capital expenditures planned. Moreover, any short-term shortage in labour or capital may manifest itself in terms of factor price inflation another type of trade induced adjustment cost. In both cases it would appear useful for governments to be aware of the type and magnitude of any trade induced costs so that they are able to react in a timely and appropriate manner. However, as has been previously documented (Brülhart and Elliott 1998 and Brülhart and Hine 1999) a significant majority of trade between developed countries is intra-industry in nature. Whilst all changes in trade patterns are likely to involve some form of adjustment costs, the smooth adjustment hypothesis (SAH) postulates that trade changes that are intra-industry in nature will experience adjustment costs that are less severe than the costs associated with inter-industry trade changes. The argument is that if both imports and exports of a product are increasing, assuming constant domestic demand, then whilst some reallocation of factors between firms within a given industry is inevitable as some firms expand and others contract, assuming factor demand within a given industry is likely to be relatively homogeneous, it should mean that factors can be reallocated between firms within an industry with relatively little cost. For example, if exports of firm 1 in industry A increase, while exports of firm 2 in industry A contract, workers and machinery from firm 2 can be easily re-employed in firm 1 with little or no adjustment cost. However, if firms 1 and 2 are in different industries there is no guarantee that workers employed in B will have the skills required by industry A and any capital equipment may be an imperfect substitution for the manufacture of potentially very different products e.g. neither coal miners nor coal 5

6 mining equipment is easily transferable into the financial services sector. Not only will expensive and time consuming retraining be required but it is also more likely that, given evidence of geographical industrial clustering, that firms in different industries will be more geographically dispersed than firms in the same industry adding an additional impediment to smooth factor reallocation. Despite its intuitive appeal, empirical evidence of the existence of the SAH, whilst generally supportive, is relatively limited. One reason is that much of the literature to date has simply tested the relationship between changes in IIT (generally known as MIIT) and changes in employment (see e.g. Brülhart and Elliott 2002, Erlat and Erlat 2003, Elliott and Lindley 2006, Brülhart et al and Silva and Crespo 2006). In this paper we argue that while it remains valid to test the SAH by simply comparing changes in MIIT and employment, we argue that by first distinguishing vertical from horizontal IIT a richer analysis and a more accurate test of the SAH is possible as we explain below. In this paper we argue that the adjustment costs associated with changes in matched trade that involve changes in product quality are significantly higher than if changes were horizontal in nature or where the quality of exports and imports does not change. The argument for why this is the case is similar to that described above for the distinction between inter- and intra-industry trade. For example, if matched trade flows have increased, the SAH would suggest that the adjustment costs are minimal. However, if such a change involves an increase in output of high quality varieties at the expense of firms producing low quality goods then it is not evident that workers displaced by the low quality contracting firms will have the required levels of skill or attributes to gain employment in the high quality expanding firms nor whether the capital equipment used to manufacture low quality varieties can be simply and in-expensively re-engineered to produce high quality varieties of the same product. Likewise, if the situation was reversed, whilst high skilled workers may be able to be hired by expanding low quality firms, there will be an underemployed of resources and a subsequent cost to the national economy (related to the over education literature, see e.g. Green et al. 1999). Where there is no or little change in product quality the SAH argument still holds, as resources should be easily reallocated between expanding and contracting firms that are producing goods of a similar quality within an industry. The argument is that such horizontal quality changes in matched trade are only a, potentially small, subset of total matched trade changes which in turn might explain why the empirical evidence for the SAH is only weakly supportive. 6

7 Methodology The empirical analysis in this paper follows the three-stage approach suggested by Azhar and Elliott (2006c) but has not yet been taken to the data. In Stage 1 to identify those industries that have experienced large changes in intra-industry trade we use an index of dynamic IIT, first proposed by Azhar and Elliott (2003) known as the S index. Within a given dataset there may be a significant number of industries that have experienced large changes in inter-industry or net trade where the potential trade induced adjustment costs are clear. For example, if for a given product exports fall dramatically due to a fall in demand overseas, even if imports and domestic demand remain unchanged, it is likely that factors will be laid off or used less extensively. There may also be a second group of products that will have experienced little change in trade volumes with import and export levels remaining relatively stable. Assuming constant domestic demand there will be no adjustment costs and certainly no trade induced adjustment costs which is what we are interested in. However, there may be a third, potentially large, group of industries that may have experienced large increases or decreases in matched trade over the period of analysis. According to the traditional SAH these industries should have incurred relatively few adjustment costs. However, as we discussed earlier, such an assumption may be a heroic one where the actual magnitude of any adjustment cost may be exacerbated by changes in matched trade that involve large changes in relative or absolute product quality. The first stage therefore is to calculate an index of trade-induced adjustment labelled in Azhar and Elliott (2003) as the S index and is given by: S 1 = 2 L ( X M ) = 2 X M ( max{ X, M }) t t for t N, N = { 1,2,3,... n} (1) where L is the largest change in X or M over the period studied (equivalent to the length of one side of the accompanying trade adjustment space (TAS), a geometric tool, that is described in Azhar and Elliott (2003)). The index has a range of 1 S 1 with each change in trade (represented by a Cartesian point in the TAS) having a corresponding 7

8 adjustment value. The S index provides a measure from the perspective of the home country so that S is a simple monotonically increasing function of X M that also satisfies consistency and country specificity criterion. See Azhar and Elliott (2003) for a discussion of the desirable characteristics of any MIIT index. A S index of zero means imports and exports are exactly matched. At the extremes imports and exports moved in exact opposite directions either beneficially for the Home country (exports increased and imports decreased) or vice versa with S indices of +1 and 1 respectively. In Stage 2 we take each product from the third group in Stage 1 and split each product s IIT into its constituent horizontal and vertical parts using the PQV index proposed by Azhar and Elliott (2006a). This approach provides an indication of the level of VIIT in total IIT for any given year by taking the ratio of unit values (value divided by quantity). See Azhar and Elliott (2006a) for details. The PQV index is analogous to the Grubel and Lloyd (1975) GL index (that measures the share of IIT in total trade flows). 4 The PQV index measures the dispersion of product quality in IIT flows and is given by; PQV X M UV UV = 1 +, with 0 < PQV < 2 X M ( UV + UV ) (2) Once we have an index value, scaled between zero and two, a decision needs to be made as to what percentage of costs does two-way trade in a product need to share to be considered horizontally differentiated. For example, if the imports and exports of a product share at least 85% of their costs (reflected in the price per unit of output) then it is not unreasonable to consider this as two-way trade in a horizontally differentiated product. Likewise, if the costs of the export country exceed those of the import country by 50% (so they only share 50%) then it would seem reasonable to classify this IIT as VIIT(high). Thus, the PQV index can be interpreted as follows: from a Home country 4 The GL index has been the standard share measure of IIT since its empirical discovery (Grubel and Lloyd 1975). The GL index measures the share of trade in total trade that is intra-industry. For industry i for a given country the GL index is given by; X i M i GL i = 1 ( X i + M i ) where X i and M i are the exports and imports of industry i during a particular time period, usually one year. The index can take any value between 0 and 1 where the upper bound represents all trade being intraindustry in nature. 8

9 perspective IIT is classified as high quality, VIIT(high), if PQV > 1.15, low quality, VIIT(low) if PQV < 0.85 and of a similar quality, HIIT, if PQV By construction, both the PQV index has symmetrical limits and is projected or scaled equally on both lower and upper bounds. The method is simple to use and is able to distinguish between high and low quality IIT from the perspective of either the Home or Foreign country. In other words, it has the desirable characteristic of being country specific. Stage 2 is not crucial but provides useful information on quality differences in IIT at any given point in time that helps us to interpret the values derived in Stage 3. For example, we will be able to state whether China was the importer or exporter of low or high quality varieties of a product in a given year. Finally, in Stage 3 we calculate a dynamic measure of quality changes in MIIT that we call marginal quality (MQ) to capture how changes in product quality in matched trade changes might affect adjustment costs. To do this we utilise the Azhar and Elliott (2006c) MQ index which is given by: MQ t { } X M ( UV UV ) X M UV UV t = = (5) X M 2max UV, UV 2L t t where the MQ index ranges from 1 MQ 1 for t N, N = {1,2,3, n}. The index is scaled by the maximum of the unit values (UV) changes for any given time period or range of products. The index can be visualised by plotting each Cartesian point within a Unit Value Space (UVS) that is described in detail in Azhar and Elliott (2006c). Data In this paper we examine bilateral trade relationships between China and three of its East Asian neighbours: Singapore; Indonesia; and Malaysia between 1994 and For reasons of space certain tables and figures relate to Malaysia only. These three countries were chosen to represent a cross section of the level of industrial development 5 This process is similar to the interpretation of the standard GL index in the sense that we are looking to answer the question: What value of the GL index constitutes high IIT? Although the answer depends, in part, on the level of aggregation, in the same way that certain GL values are seen to reflect high IIT, so we can say that sharing a certain percentage of costs reflects a high share of VIIT. See Azhar et al.azhar et al (2006b) for a detailed discussion of the various possible cut offs and dispersion percentages. 9

10 across the ASEAN region from the relatively developed Singapore; the newly industrialised Malaysia; and the still developing Indonesia. The data are from the COMTRADE database that records imports and exports in quantities and values. The data are classified according to the Standard International Trade Classification (SITC) Revision 3. 6 Before we proceed with the three stages described in the methodology section we present some basic descriptives to highlight the evolution of trade in this region during the last decade or so. Figure 1 shows the growth of Chinese imports and exports in manufactures from Value of Trade ($m) Imports Exports Year Figure 1: The Evolution of China s Imports and Exports As we can see, China has experienced rapid growth in both imports and exports with exports currently four times as high as in Note that it is only recently that the gap between exports and imports has begun to grow significantly with China recording record trade surpluses in Whilst Figure 1 provides an overview of trends in Chinese trade patterns it masks potentially different trends at the individual industry level and between different bilateral trade relationships. In Figure 2 we present the corresponding levels of imports and exports for our sample countries. 6 All data have been deflated to 1994 prices. For a full listing of all 5-digit products within the SITC Revision 3 classification see 7 China s trade surplus reached a record $26.91billion in June 2007 mainly driven by increases in exports. Exports grew by 27% to $ billion whilst imports only grew 14% to $76.4billion (newratings.com). 10

11 Trade Value ($m) Year Singapore Imports Singapore Exports Malaysia Imports Malaysia Exports Indonesia Imports Indonesia Exports Figure 2: The Evolution of Trade for Singapore, Malaysia and Indonesia Observe that all three countries show considerable growth in trade with exports more than doubling for Singapore and Malaysia between 1994 and It is likely that the 1997 Asian currency crisis limited the growth of trade between 1996 and 1998 as it will have simultaneously increased the price of imports (and intermediate inputs) and lowered the price of exports relative to the rest of the world. From the data it is clear that although Indonesia was badly affected its exports still approximately doubled between 1994 and A comparison with Figure 1 reveals that China started in 1994 with import and export values that were 20% higher than Singapore s total trade. By 2004 however China was exporting 20% more than the other three countries put together. Table 1 summarises trade changes between these countries at the aggregate level. Table 1: Summary of China s Trade in Manufactures (SITC 5-8), China IMPORT EXPORT IMPORT EXPORT GROWTH GROWTH IMPORT EXPORT Singapore 1,059,325,416 1,842,304,446 8,721,150,307 8,840,212, % 661% Malaysia 703,976, ,528,483 10,956,837,874 5,567,807, % 683% Indonesia 694,870, ,102,642 2,812,473,593 3,733,508, % 447% World 98,394,424, ,991,472, ,227,758, ,744,699, % 321% Source: COMTRADE and authors own calculations. Values in $US in 1994 prices. The first observation is that China s exports (321% growth) to the World have grown 25% faster than its imports (240% growth). However, for Malaysia and Singapore 11

12 this situation is reversed with even Singapore, as a developed country seeing exports to China grow faster than imports. The remarkable performance of Malaysia is tempered by the poor performance of Indonesia who has seen its imports from China increase faster than its exports. This may be a result of being too close a competitor with China in the low wage sectors and something we investigate further in the remainder of the paper. However, we can only tell so much from these aggregate figures. In Tables 2, 3 and 4 we present summary data for 34 sectors at the 2-digit level to document the extent of the changes in China s imports and exports with Malaysia; Singapore; and Indonesia respectively. We begin in Table 2 with Malaysia. 12

13 Table 2: China-Malaysia Manufacturing Trade at the 2-Digit Level (SITC 5-8) SITC IMPORT EXPORT IMPORT EXPORT GROWTH GROWTH GL Index GL Index S Rev3 COMMODITY IMPORT EXPORT index 51 ORGANIC CHEMICALS 13,300,370 10,870, ,022,030 59,217, % 445% INORGANIC CHEMICALS 571,145 44,664,645 19,122,057 79,029, % 77% DYES,COLOUR MATERIALS 3,179,326 1,888,297 24,672,876 19,687, % 943% MEDICINAL,PHARM.PRODUCT 127,185 15,318, ,916 30,754,043 22% 101% ESSENTL.OILS,PERFUME,ETC 2,998,919 7,221,705 26,158,379 19,543, % 171% FERTILIZER,EXCEPT GRP272 6,811,758 63,381 39,700, % PLASTICS IN PRIMARY FORM 31,871,381 7,099, ,820,347 19,674, % 177% PLASTIC,NON-PRIMARY FORM 5,203,085 1,698,399 27,176,050 15,698, % 824% CHEMICAL MATERIALS NES 7,690,116 10,100,504 65,713,634 48,674, % 382% LEATHER, LEATHER GOODS 1,233, , ,716 3,625,951-39% 1059% RUBBER MANUFACTURES, NE 10,535,100 2,288,114 83,038,420 24,643, % 977% CORK, WOOD MANUFACTURE 358,825,726 2,619, ,180,369 11,825,548-67% 351% PAPER,PAPERBOARD,ETC. 6,430,608 16,281,829 24,370,356 32,181, % 98% TEXTILE YARN,FABRIC,ETC. 58,096,903 59,680,357 69,680, ,657,560 20% 340% NON-METAL.MINER MANFCT 2,954,371 18,576,381 59,977, ,142, % 552% IRON AND STEEL 21,110,236 39,884, ,549, ,658, % 538% NON-FERROUS METALS 10,894,529 7,554,520 97,704,691 81,435, % 978% METALS MANUFACTURES,NES 6,901,846 44,288,033 35,753, ,771, % 236% POWER GENERAT.MACHINES 11,227,666 8,535,339 40,393,696 83,221, % 875% SPECIAL.INDUST.MACHINERY 19,926,859 21,333,606 56,534,586 68,208, % 220% METALWORKING MACHINE 2,899,941 16,619,096 15,918,172 22,055, % 33% GENERAL INDUSTL.MACH.NE 20,878,283 39,666, ,106, ,660, % 446% OFFICE MACHINES,ADP MAC 8,899,509 11,525,206 1,205,964,185 1,391,076, % 11970% TELECOMM.SOUND EQUIP ET 23,492,937 51,551, ,834, ,049, % 1306% ELEC MCH APPAR,PARTS,NES 61,719,769 66,824,933 6,861,247, ,273, % 1174% ROAD VEHICLES 1,239,028 12,146,821 20,555, ,057, % 823% OTHR.TRANSPORT EQUIP. 2,782, , ,975 11,411,356-88% 4299% PREFAB BUILDGS,FTTNG ETC 654,168 4,034,052 2,639,762 21,949, % 444% FURNITURE,BEDDING,ETC. 1,387,799 1,552,841 3,932,256 34,968, % 2152% TRAVEL GOODS,HANDBGS E 53,016 6,859, ,276 20,602, % 200% CLOTHING & ACCESSORIES 107, ,377,592 12,732, ,819, % 58% FOOTWEAR 189,684 10,773, , ,564, % 1000% SCIENTIFIC EQUIPMENT NES 1,441,637 3,450, ,309, ,250, % 5356% PHOTO.APPARAT.NES;CLOCK 831,347 13,375,834 24,437,608 36,447, % 172% Source: COMTRADE and authors own calculations. Values in $US in 1994 prices. 13

14 The values that stand out are the enormous growth rates that many sectors experienced in those ten years. If we look at those sectors where China has seen the largest growth in imports they tend to be in the high tech sectors such as SITC 87 (Scientific Equipment NES), SITC 75 (Office machinery), SITC 77 (Electrical Machinery Apparatus). The exception and surprise, that also reported a growth rate of over 10,000%, was SITC (84) Clothing and Accessories. However, this growth was from a low base and the 2004 import value is still nearly twenty times lower than the level of Chinese exports back to Malaysia (with a correspondingly low GL index). What Table 2 serves to show is the sheer dynamism of the Chinese economy. The growth of Chinese imports from Malaysia provides strong evidence that China presents more of an opportunity to its East Asian neighbours than a threat. However, an example where the Malaysian government might be concerned is SITC 61 (Leather, Leather goods) that saw a fall in exports coupled with a 1000% growth in imports from China with China switching from a net exporter to a net importer. The high S index of 0.57 reveals that this is the 2-digit sector where Malaysia has suffered the most damaging change in matched trade in the sense that the changes in trade are unmatched (imports falling and exports rising). In nearly all other 2-digit sectors both imports and exports have risen. Finally, the GL indices reveal that there are significant levels of IIT at the 2- digit level so, in all by one sector, China and Malaysia import and export goods from the same sector. The follow-up question that we address later is what has happened to the quality of China s imports and exports to Malaysia. In Tables 3 and 4 we present similar results for Singapore and Indonesia respectively. There are some obvious differences between China-Malaysia trade and China s trade with Singapore and Indonesia. In the first instance, the trade growth rates vary significantly for the more developed Singapore with imports from Singapore growing at close to 700% and exports growing around 600% between 1994 and The overall values for Indonesia from Table 1 were lower but these figures do mask some quite remarkable changes in trade patterns for example, SITC 75 (Office machinery) grew over 125,000% in ten years. 14

15 Table 3: China-Singapore Manufacturing Trade at the 2-Digit Level (SITC 5-8) SITC IMPORT EXPORT IMPORT EXPORT GROWTH GROWTH GL Index GL Index S Rev3 COMMODITY IMPORT EXPORT index 51 ORGANIC CHEMICALS 33,307,928 23,602, ,793,565 93,515, % 296% INORGANIC CHEMICALS 3,277,820 18,037,961 7,706,952 30,690, % 70% DYES,COLOUR MATERIALS 45,515,155 12,385,624 76,386,915 18,443,351 68% 49% MEDICINAL,PHARM.PRODUCT 6,038,210 21,802,049 5,814,683 38,259,886-4% 75% ESSENTL.OILS,PERFUME,ETC 9,728,708 12,282,212 24,450,710 18,079, % 47% FERTILIZER,EXCEPT GRP272 47,361 1,547,772 14, ,694-69% -81% PLASTICS IN PRIMARY FORM 121,737,977 8,534,739 1,014,088,890 34,565, % 305% PLASTIC,NON-PRIMARY FORM 6,371,114 2,711,005 45,077,793 16,977, % 526% CHEMICAL MATERIALS NES 43,835,317 16,748, ,003,246 57,669, % 244% LEATHER, LEATHER GOODS 2,579, ,722 32,496,817 9,495, % 4777% RUBBER MANUFACTURES, NE 2,098,693 14,363,009 19,308,502 32,974, % 130% CORK, WOOD MANUFACTURE 21,415,625 8,787,062 1,896,431 34,353,337-91% 291% PAPER,PAPERBOARD,ETC. 11,580,433 40,377,094 19,190,518 22,154,659 66% -45% TEXTILE YARN,FABRIC,ETC. 9,437, ,465,153 39,037, ,451, % -15% NON-METAL.MINER MANFCT 10,777,213 63,396,182 23,805, ,259, % 71% IRON AND STEEL 36,457,715 39,961,286 20,841, ,204,801-43% 546% NON-FERROUS METALS 26,977,637 99,685,995 83,930, ,641, % 138% METALS MANUFACTURES,NES 22,465, ,230, ,074, ,577, % 25% POWER GENERAT.MACHINES 48,852,596 30,540, ,812, ,214, % 231% SPECIAL.INDUST.MACHINERY 103,885,733 28,672, ,641,326 82,818,347 51% 189% METALWORKING MACHINE 20,737,498 18,619, ,244,750 79,694, % 328% GENERAL INDUSTL.MACH.NE 67,727,774 55,657, ,706, ,658, % 320% OFFICE MACHINES,ADP MAC 78,775, ,581,903 1,368,838,280 1,492,925, % 1214% TELECOMM.SOUND EQUIP ET 129,980, ,264, ,384,778 1,915,431, % 1183% ELEC MCH APPAR,PARTS,NES 97,403, ,059,040 3,003,558,383 1,964,694, % 1446% ROAD VEHICLES 19,975,175 37,837,281 19,399, ,612,366-3% 227% OTHR.TRANSPORT EQUIP. 27,954,732 40,116,289 12,019, ,520,970-57% 328% PREFAB BUILDGS,FTTNG ETC 3,459,915 9,744,503 2,435,890 14,664,397-30% 50% FURNITURE,BEDDING,ETC. 4,683,171 26,170,220 1,355,623 37,098,388-71% 42% TRAVEL GOODS,HANDBGS E 48,531 19,776, ,094 40,170, % 103% CLOTHING & ACCESSORIES 614, ,476,719 1,515, ,070, % 530% FOOTWEAR 229,110 25,302, ,750 46,399,026-8% 83% SCIENTIFIC EQUIPMENT NES 19,351,723 13,855, ,208, ,078, % 702% PHOTO.APPARAT.NES;CLOCK 6,572,767 23,053,158 65,792,894 54,464, % 136% Source: COMTRADE and authors own calculations. Values in $US in 1994 prices. 15

16 Table 4: China-Indonesia Manufacturing Trade at the 2-Digit Level (SITC 5-8) SITC IMPORT EXPORT IMPORT EXPORT GROWTH GROWTH GL Index GL Index S Rev3 COMMODITY IMPORT EXPORT index 51 ORGANIC CHEMICALS 11,439,339 28,899, ,127, ,629, % 276% INORGANIC CHEMICALS 377,278 45,898,400 4,704, ,623, % 163% DYES,COLOUR MATERIALS 2,036,910 12,897,884 17,526,429 58,480, % 353% MEDICINAL,PHARM.PRODUCT 364,604 8,092, ,561 22,593,113-16% 179% ESSENTL.OILS,PERFUME,ETC 3,317,310 2,127,960 15,947,731 12,113, % 469% FERTILIZER,EXCEPT GRP272 9,987,429 1,647,178 37,116 37,893, % 2201% PLASTICS IN PRIMARY FORM 8,142,064 14,326,102 74,585,536 22,749, % 59% PLASTIC,NON-PRIMARY FORM 5,379,537 1,317,266 17,650,469 24,709, % 1776% CHEMICAL MATERIALS NES 1,755,034 5,381,678 31,624,133 69,448, % 1190% LEATHER, LEATHER GOODS 10,292, ,147 11,536,498 5,412,121 12% 1009% RUBBER MANUFACTURES, NE 808,320 1,573,712 12,665,305 20,302, % 1190% CORK, WOOD MANUFACTURE 447,389,100 3,424, ,607,772 10,174,586-50% 197% PAPER,PAPERBOARD,ETC. 63,175,671 5,967, ,903,500 27,753, % 365% TEXTILE YARN,FABRIC,ETC. 52,883,541 43,284, ,869, ,358, % 774% NON-METAL.MINER MANFCT 7,101,052 25,627,770 47,633,714 90,753, % 254% IRON AND STEEL 30,844,012 30,754,961 71,732, ,936, % 1038% NON-FERROUS METALS 7,078,743 27,664, ,497,727 93,620, % 238% METALS MANUFACTURES,NES 1,217,045 28,508,329 6,482, ,410, % 585% POWER GENERAT.MACHINES 479,199 59,022,307 25,139, ,709, % 174% SPECIAL.INDUST.MACHINERY 4,351,472 69,895,155 10,142, ,466, % 138% METALWORKING MACHINE 185,304 33,697, ,146 18,289,389 65% -46% GENERAL INDUSTL.MACH.NE 8,279,980 31,816,676 54,859, ,297, % 712% OFFICE MACHINES,ADP MAC 438,688 2,735, ,192, ,034, % 5312% TELECOMM.SOUND EQUIP ET 729,185 35,420,316 91,708, ,502, % 878% ELEC MCH APPAR,PARTS,NES 1,701,112 46,665, ,712, ,990, % 609% ROAD VEHICLES 8,003,362 13,394,453 19,332, ,271, % 1933% OTHR.TRANSPORT EQUIP. 481,560 2,361 21,401,139 0% 4344% PREFAB BUILDGS,FTTNG ETC 990,760 8,284, ,417 22,301,419-40% 169% FURNITURE,BEDDING,ETC. 634,929 2,029,780 1,838,945 12,637, % 523% TRAVEL GOODS,HANDBGS E 43, ,595 1,761,973 10,431, % 1967% CLOTHING & ACCESSORIES 132,661 7,115,842 3,227,003 69,128, % 871% FOOTWEAR 185,906 3,165,245 9,127,513 29,927, % 846% SCIENTIFIC EQUIPMENT NES 172,606 3,275,497 11,397,050 31,135, % 851% PHOTO.APPARAT.NES;CLOCK 159,706 4,533,218 3,448,126 26,907, % 494% Source: COMTRADE and authors own calculations. Values in $US in 1994 prices. 16

17 For Singapore, in Table 3, the sector that appears to have suffered the largest trade induced adjustment pressures is SITC 63 (Cork and Wood Manufactures) where exports to China fell 91% at the same time that imports from China grew nearly 300%. The high S index of 0.88 is indicative of a problem from Singapore s perspective (remember that China is the Home country in this analysis). The other sectors where it is apparent that Singapore has suffered large trade induced adjustment pressures are in SITC 67 (Iron and Steel), SITC 79 (Other Transport Equipment), SITC 81 (Prefab Buildings, Fittings etc.) and SITC 82 (Furniture, Bedding etc.). In all these cases exports from Singapore fell as imports from China grew. The first of these sectors SITC 67 (Iron and Steel) is often considered a strategic industry and can be a large employer. Between 1994 and 2004 we can observe that exports to China fell from $36,457,715 down to $20,841,151 whilst exports grew from an almost similar starting level of $39,961,286 to a huge $258,204,801 by However, even for Singapore we know that over the 10-year period overall exports to China grew at a faster rate than imports from China (see Table 1). We now consider China s trade with Indonesia. Indonesian exports to China grew faster than imports in half of our 34 2-digit sectors although the absolute levels of growth have been exceedingly large in many cases non more so that SITC 75 (Office Machinery), SITC 77 (Electrical Machinery and Apparatus) and SITC 76 (Telecommunications Equipment) where imports from Indonesia grew 126,000%, 19,988% and 12,477% respectively. From an Indonesian perspective, those sectors that may have suffered the trade induced adjustment costs associated with a contraction are SITC 54 (Medicinal, Pharm. Products) where exports to China fell 16% while at the same time imports from China grew 179%, SITC 56 (Fertilizer, except GRP 272) where exports fell 100% while imports from China grew from $2,127,960 to $12,113,856 a growth rate of 2201% and finally, SITC 63 (Cork, Wood Manufacture) which saw a 50% fall in exports to China and an 197% increase in imports. All three of these industries are likely to have experienced some adverse employment assuming of course that domestic demand or exports to third countries does not compensate. Having examined some of the trends at the aggregate level we now consider China s bilateral trade with its ASEAN neighbours using the three-stage approach suggested by Azhar and Elliott (2006c). Such an approach will allow us to account for changes in the quality of matched trade. The results are presented in the next section. 17

18 Results STAGE 1: Measuring changes in IIT In Stage 1 we calculated S indices for all SITC-5 digit products within the manufacturing sector (SITC 5-8) for trade between China and three countries: Malaysia; Indonesia; and Singapore. By plotting the changes in exports against the changes in imports within a trade adjustment space we were able to identify those products that have high levels of matched trade. In this paper we compare trade flows between 1994 and For reasons of space most of the commentary is restricted to Malaysia although tables and graphs are also included for the other two countries where appropriate. 8 China - Malaysia For Chinese-Malaysian trade our SITC (Revision 3) dataset for the manufacturing sector (SITC 5-8) includes 2008 commodity codes where there has been at least some trade. Thus, in Stage 1 we calculated S indices for all 2008 products. The majority of these had relatively little bilateral trade and a large number of products had either no imports or exports in either 1994 or Of the 2008 products, approximately 10% of them had significant changes in matched trade represented by an S index of between 0.4 and We experimented with different S index values to act as a cut off point (see Azhar et al for more discussion). Of the digit products examined just over 30% recorded a S index of either 0.5 or +0.5 with 21% being A value of 0.5 (+0.5) will be achieved if either, there are no exports (imports) and imports (exports) have been increasing, or that there are no imports (exports) and exports (imports) have been falling. A large majority of the +0.5 values are as a result of increases in exports for products where there are no imports into China from Malaysia. The other excluded products are those with S indices between 0.5 and 1 or 0.5 and 1 where there is no matched trade and imports and exports moving in opposite directions. Clearly, in terms of adjustment, the costs to Malaysia are limited if they never produced the good in the first place. This is why it is important to examine those products where there is some two-way matched trade and to examine how this trade has 8 We also look at and separately. Results are available from the authors upon request. 18

19 changed over time. Thus, the 636 products that represented perfect inter-industry trade were removed as well as the large number of products with very little IIT change (with a S index value >0.4 or <-0.4). Our final sample that we take forward to Stage 2 is therefore restricted to 194 or close to 10% of products across SITC (5-8). If we were to undertake such an analysis for intra-eu trade for example, it is envisaged that the number of products taken on to Stage 2 would be significantly higher. Before we plot the S indices for our 194 products remember that we are examining China s trade with Malaysia from a Chinese perspective. That is to say, which products have China done well in and which has it done badly. As a first indicator of China s trade performance with Malaysia it is therefore useful to compare the number of positive and negative S index values. Figure 3 plots S indices for the 194 products that have experienced significant changes in IIT between 1994 and SITC 5 SITC 6 SITC 7 SITC 8 S index Manufacturing Products (SITC 5-8) Figure 3: S indices for China-Malaysia bilateral trade SITC (5-8) The first observation is that there are a large number of positive and negative values that appear to be fairly evenly spread across 1-digit sectors. In total there are 102 negative and 92 positive S index values with average index values of 0.24 and for the negative and positive values respectively. What Stage 1 has allowed us to identify are those products in China (negative values) or Malaysia (positive values) that have suffered potentially negative adjustment costs as a result of the changes in bilateral trade between these two countries. The relatively even split between negative and positive values is the 19

20 first indication that Malaysia has not faired too badly in bilateral trade with China in those products where there has been a significant degree of matched trade change. China - Singapore For Chinese Singaporean trade there were 2083 SITC 5-digit commodity codes with some recorded trade. Of those, 260 or 12.5% recorded an S index between 0.4 and It is these products that we take onto Stage 2. Figure 4 plots S indices for the 260 products that have experienced significant changes in matched trade between 1994 and SIT C 5 SIT C 6 SIT C 7 SIT C S index Manufacturing Products (SITC 5-8) Figure 4: S indices for China-Singapore bilateral trade SITC (5-8) Again we observe a large number of negative (150) and positive (110) S index values. As a percentage of products bilaterally traded, Singapore has suffered potentially adverse adjustment pressures in far less than Malaysia (42% and 47% respectively). The average positive and negative S index scores are and 0.25 respectively. There appears to be no evidence of systematic differences across 1-digit sectors. Again, Singapore does not appear to have been adversely affected in its bilateral trade in those products that experienced matched trade changes with China. China - Indonesia As we might have expected, for Chinese-Indonesian trade there were only 1943 products with some recorded trade and of them only 99 or 5% had significant levels of 20

21 changes in matched trade (-0.4<S<0.4) as demonstrated by the S index results. Figure 5 plots S indices for the 99 products that have experienced significant changes in matched trade between 1994 and SITC 5 SITC 6 SITC 7 SITC S index Manufactured Products (SITC 5-8) Figure 5: S indices for China-Singapore bilateral trade SITC (5-8) For Chinese bilateral trade with Indonesia there are 42 negative and 45 positive S indices with average negative and positive S indices of and Indonesia is the only country where the number of positives is greater than the negatives. What this suggests is that of all our three countries Indonesia has suffered potentially the largest adjustment costs from China s emergence into the global economy. Beforehand one might have expected Singapore to the most adversely affected but our results suggest that it has been one of the largest beneficiaries. Once we have identified those products that appear to have suffered some sort of adjustment cost we examine these more carefully in Stage 2 where we are able to examine the quality differences in matched trade. STAGE 2: Measuring Product Quality In Stage 2 we take the products from Stage 1 and disentangle each product s IIT into that which is vertical and that which is horizontal in nature using the PQV methodology from Azhar and Elliott (2006a). This allows us to examine those products that China and its trading partners both import and export (hence have high IIT levels) 21

22 and to examine whether China tends to export high or low quality varieties of the product. To do this we calculate PQV indices and then plot them in rank order so that we can simply visualise the breakdown between China s exports that are high quality (VIIT high), those that are low quality (VIIT low) and those that are horizontally differentiated (HIIT). China-Malaysia In Figures 6 and 7 we present the results for 1994 and 2004 respectively for Chinese-Malaysian trade. The cut off between vertical and horizontal that we have chosen for illustration is where 0.85>HIIT<1.15. PQV index VIIT (High) HIIT VIIT (Low ) Manufacturing Products (SITC5-8) Figure 6: Product Quality for China-Malaysia Manufacturing Trade in 1994 PQV index VIIT (High) HIIT VIIT (Low ) Manufacturing Products (SITC5-8) Figure 7: Product Quality for China-Malaysia Manufacturing Trade in