Production Network and Its Impact on International Trade: A Comparison between East Asia and European Union

Transcription

1 Production Network and Its Impact on International Trade: A Comparison between East Asia and European Union Son Thanh Nguyen * UWA Business School The University of Western Australia Abstract: The emergence of production networks has changed the structure of international trade as reflected by a large share of intra-regional trade flows and rising value of intermediate goods trade. This paper aims to examine and compare the impact of production networks on international trade in East Asia and European Union. The results show that at the global level, intermediate goods export is more sensitive to trade barriers than total goods. At regional level, a comparison between East Asia and European Union shows service link costs in East Asia have been successfully reduced. Despite the efforts directed towards export market diversification in East Asia, the region is still more dependent on other regions economic conditions than European Union is. Keywords: Production network; gravity model; European Union; East Asia; interdependence; remoteness; intermediate goods trade. JEL classification: F13, F14, F15 * I would like to thank Professors Yanrui Wu and Anu Rammohan for their continuous support and feedback while developing this paper. i

2 PREFACE Thesis title: Production network in East Asia and opportunities for Vietnam Supervisors: Professors Yanrui Wu and Professor Anu Rammohan The emergence of production networks has changed the structure of international trade as reflected by a large share of intra-regional trade flows and rising value of intermediate goods trade. Understanding the rise of these networks is important because they can present new opportunities as well as challenges for developing countries, particularly in East Asia. In this context, Vietnam is considered as a newcomer, a country on the verge of joining the East Asian production network. The analysis of East Asian production network and Vietnam s position in the network could provide trade policy suggestions for the country to take advantage of the integration opportunity. The thesis aims to address the following key questions: 1. How did the effect of trade determinants change given the increasing importance of trade in intermediate goods? What are the distinct effects on bilateral trade flows of production network in East Asia compared to those in European Union? 2. What is the role of China in the East Asian production network? Does China crowd out the trade opportunities of other developing economies in the region? 3. Given the existence of production networks in East Asia, what is the current position of Vietnam in this network? What are the comparative advantages and opportunities for Vietnam to join the production network? How should Vietnam direct its trade strategy to take advantage of these opportunities? The thesis will be structured as follows: Chapter I: Introduction Chapter II: Theories of trade and production network Chapter III: Review of empirical literature on international trade Chapter IV: Production network and its impact on international trade Chapter V: The rise of China and its impact on developing Asia Chapter VI: Vietnam and its position in East Asian production network Chapter VII: Conclusion This paper is based on the analysis in Chapter IV. ii

3 1. Introduction In recent years, international trade has become increasingly important for the global economy. The world export as a share of global GDP started rising around the 1960s, and has grown steadily since then to reach unprecedented heights. This period of increase in the importance of trade, which Baldwin (2006) called the second great unbundling, has created the opportunity for many countries to achieve rapid growth and conversion with developed economies. This is particularly true for Asia, where trade had an average annual growth rate at 13 per cent over the last 30 years. This rapid growth has helped Asia seize a larger share of world exports as observed in Table 1. From taking only around 14.9 per cent of global merchandise export, the region now exports more than 31 per cent, displacing the share of Europe and North America. East Asia (EA) benefited the most during this period, creating what is usually referred to as the East Asian Miracle. 1 In the 1950s, EA only accounted for 4 per cent of global GDP, but since 1995 it has risen to 25 per cent. Most of this success is attributed to trade, as the region now accounts for over a quarter of world trade (Dent 2008). Table 1: World merchandise export share by region in selected years (%) Region North America Europe Asia Source: WTO (2014) With the second unbundling, there is a transformation in the nature of international trade. This transformation is characterized by high volume of trade and a large share of intraregional trade between countries within the same region. Table 2 shows that European Union (EU) and North America have a high share of intra-regional trade, but the numbers are quite stable over time, whereas the share in Asia and its sub-regions are increasing. The transformation is also shown in the increase of international trade in intermediate goods. Using the Standard International Trade Classification (SITC) specification, the WTO has reported a steady increase in the value of global trade in parts and components over the last three decades. The share of parts and components in world export of manufactured goods increased from 22 per cent in 1980 to 29 per cent in However, between 2000 and 2008, this share declined by roughly 4 percentage points, only to recover somewhat in 2011, at 26 per cent (WTO 2013). At the regional level, EA economies tend to produce and sell 1 East Asia consists of both North East Asia and South East Asia 1

4 intermediate goods to each other, with 64 per cent of intermediate exports comprising of intra-regional export flows (Baldwin and Kawai 2013). Table 2: Intra-regional trade share by region in selected years (%) Region Asia ASEAN ASEAN EU North America Note: ASEAN+3 consists of the 10 ASEAN member economies, the People s Republic of China, Japan and the Republic of Korea. Source: Asian Development Bank (ADB) This international trade transformation has been attributed to production fragmentation or creation of production networks. Jones (2000) defined production fragmentation as the splitting up of previously integrated production process into two or more components, called fragments. The unbundling or fragmentation technique itself is not a new concept. In fact, its logic is very simple and commonly used in every industry. But with recent development, when this process broke out of countries border, it became revolutionary and brought great results. Although production networks and supply chains have been formed on a global scale, they are still marked by regional blocks, what could be called Factory Asia, Factory North America and Factory Europe (Baldwin and Lopez- Gonzalez 2014). Of these regions, EA and EU stand out as the most integrated, with large intermediate trade value and high share of intra-regional trade. Yet, production network in EA and EU may have distinct features based on many differences in sociocultural, political, historical and institutional factors between two regions. The first difference between the two regions is that historically EA has been considered as a more diverse region than EU. While EU can be described as a homogeneous region of races and historical experiences, countries in EA vary from religion, ethnicity to political regime. On top of that is the difference between these two regions in terms of economic development asymmetry. While EU mostly consists of high income countries, the EA countries are ranging from low to high income. The second difference is the purpose of regional integration in these two regions. Economic integration is seen as a central tool to achieve stability and security in EU, but the main objective of integration process in EA is purely economic development (Beeson 2005). The third difference lies in regional institution 2

5 that formulates the integration process. The institutional architecture in EU is characterized by supranational institutions and pooling of sovereignty, while there is no equivalent formal institution in EA (Murray 2010). These differences provide motivation for the comparison between the two regions. Based on these observations, this paper investigates and compares the recent changes in the structure of international trade in EA and EU. It aims to answer three main questions. (i) How has the structure of international trade changed recently? (ii) How do these changes alter the impacts of trade determinants? (iii) How do these changes differ at intra-regional trade level, particularly in EA and EU. The empirical analysis is conducted by studying the impact of trade determinants on total goods exports and intermediate goods exports at both global and intra-regional level. The structure of the paper consists of six sections. Section 2 provides the literature review on production network and its impact on the effect of trade determinants. Section 3 analyses the different structure of intra-regional trade in EA and EU regions. Section 4 discusses the methodology and econometric methods. Section 5 provides the analysis of results and some robustness checks. Section 6 concludes the paper. 2. Literature review 2.1. Production network definition The source for the transformation of international trade is mentioned in the literature using different names, such as production sharing, supply chain or production network. According to Athukorala (2011) production sharing is the break-up of the production process into geographically separated stages, allowing a single good to be produced in several countries, with each country specializing in a particular phase or component of the final good. Supply chain is defined as a system of value-added sources and destinations within a globally integrated production network (Koopman et al. 2010). Within a supply chain, from initial to final production stage, each producer purchases inputs and adds value, which then becomes the input for the next stage. A global supply chain is a system that involves many countries, where at different stages in the process, an input must cross an international border. Lastly, production network can be defined as an international division of labour, in which each stage of production is spatially or geographically relocated in the most efficient site, and undertaken by different firms including MNEs and local firms (Dent 2008). Firms fragment their production abroad to minimize production cost based on different factors in the host country, which may include human resources, access to materials, technological capacities, government policies or market-oriented factors. The flow of fragments, parts and 3

6 components or intermediate goods traded between countries within a global production network is called network trade. These concepts are just different ways of expressing the same idea, which consists of three key points. First, the production process can be separated into different stages. Second, these stages can be located to many different countries. And third, because of this separation, countries are trading more in intermediate goods with each other. The use of these terms depends on the emphasis of the author. Supply chain is usually used when describing a narrow production or specific industry. Production sharing stresses on the countries or number of countries involve in the process. Production fragmentation and production network highlight the changing characteristic of production. Some authors also use production network to emphasise regional cooperation and the importance of trade in intermediate goods, or network trade Impact of production network on trade With the rising importance of trade around the world, economists have paid much attention on analysing the factors determining trade flows between nations. Trade flow between countries is considered to be increase in proportion to the economic masses of trading partners and decrease in proportion to the distance between them. With recent development of globalization, the world has come to familiarize with the concept of smaller world, where distance does not have a significant role anymore. The progress in transportation and communication technology has helped us overcome the barriers of physical distance in trade. In business press, journalists and consultants frequently used the terms such as the death of distance, world is flat or borderless world (Cairncross 2001). Yet, literatures on international trade still find the large and continuing importance of distance in determining trade flows. Despite all the reduction in trade costs, the internet revolution, the efforts to remove tariffs and trade barriers, there are no signs of weakening effect of distance (Deardorff 2003). On the contrary, some literatures pointed out that this effect even increased over time. One of the explanations comes from the production fragmentation process and increasing importance of network trade. Rauch (1999) proposed a network view of international trade, where the matching of international buyers and sellers has to go through a search process. This will certainly induce its cost and trade relation will only be established once the best match is found. The search process will be strongly conditioned by proximity or 4

7 pre-existing links, meaning the cost of entering new markets will be higher than traditional ones. The network view argues that the continuing high sensitivity of trade to distance is associated with a shift in the composition of trade towards the goods that require more extensive information exchange and buyers-sellers interaction. Gamberoni, Lanz, and Piermartini (2010) analysed the effect of distance on different type of goods and found that distance matter more for intermediate goods trade than for total goods trade. This finding is consistent with Harrigan and Venables (2006) who introduced the just in time production model. Due to the synchronisation of activities, production cannot be completed until all parts and components have arrived. When production is fragmented, the late arrival of any fragments will induce a cost that is disproportionate to the cost of any single fragment. Under this model, timely delivery is very important and suppliers will tend to locate in the same region as the assembler. This is the same as saying distance or proximity has greater impact on intermediate goods trade. As discussed earlier, the share of intermediate goods trade has increased with production networks, so this may help partially explain why the effect of distance has not decreased over time. Given all this discussion, Antràs and Yeaple (2014) noted one interesting point that although vertical specialization is more difficult with long distances, in reality there are many Asian economies import large amount of intermediate goods despite their distance from the United States. Another trade determinant discussed in the literatures is economic size or stage of development of a country. Economic size affects international trade through two channels: on demand side by showing the market size and on supply side by representing production possibility. But with development of production network, there could also be a third channel which is economic similarities between trading partners. As Baldwin and Lopez-Gonzalez (2014) argued, production fragmentation first started between rich nations, creating a North- North production network. But it only became revolutionary when the developing nations joined the process, creating a North-South production network. The combination of lowwage and high-tech nations led to massive expansion of network trade, most evident in EA. Papers such as Kimura and Ando (2005), Athukorala and Menon (2010) showed that EA production network s great success may be attributed to the distinct feature from other regions, that it took place between countries of different income levels. This feature helped production network take advantage of the difference in effective wages adjusted by labor quality. According to Shiozawa (2007), wage rate differentiations or the disparity in national wage rates is one of the most important determinants of trade relation. He stated that current 5

8 trade flows follow the Ricardian comparative advantage induced by wage differentials, only instead of advantage at the national level, it is now denationalized at firm level. To partially control for wage differential, Kimura, Takahashi, and Hayakawa (2007) used income gap variable, which is the difference in GDP per capita between the importer and the exporter. The authors found that for EA, larger income gap encourage the parts and components trade flow. Orefice and Rocha (2014) modelled the interaction between production networks and deep integration, and concluded that the likelihood of singing deeper agreements is five time higher for agreements between different income countries compared to agreements between countries with similar income levels. The third possible impact of production network on altering the trade determinants is the rising interdependence of bilateral trade flow. Originally, determinants of trade flow were considered to be the bilateral factors between two specific trading partners, whether it is economic size, distance or trade agreement between countries. Later on, the overall or the third country effect was included to capture the interdependence of international trade flows. The argument for this effect is the triangular trade model, with famous example of China, Japan and United States (Baldwin and Lopez-Gonzalez 2014). In this example, Japan exports intermediate goods and sophisticated components to China for assembly into final products. These products are then exported for consumption to the United States. In this situation, the trade relation between Japan and China does not only depend on the determinants of these two partners, but also on the economic condition in the United States. More generally, most of EA trade flow consists of intermediate goods involving many countries, creating a deeply integrated and highly interdependent region. Takeuchi (2011) showed that because of growing fragmentation of production, business cycles in EA countries have become increasingly synchronized. Important role of production network is also responsible for the greater trade contraction in the region compared to overall trade contraction at the global level during the recent financial crisis (Athukorala 2011). When production is fragmented, any given degree of contraction in demand for final goods will affect trade flows from all countries involved in the supply chain to produce those goods. 3. Role of intermediate export and network trade in EA and EU The data used in this research will cover the time period 1998 to 2013, for 143 countries around the world. The data for bilateral trade flows is drawn from UN Comtrade Database. The classification for intermediate goods is based on 3-digits Broad Economic Categories (BEC) classification. GDP, GDP deflator, value added in manufacturing and air 6

9 transport freight are taken from World Bank s World Development Indicators (WDI). Variables accounting for trade barriers, such as distance, common language, common border and colonial links are taken from Mayer and Zignago (2011) CEPII Geodist database. Data for membership in regional trade agreements (RTA) are collected based on Mayer and Zignago (2011) and WTO list of all RTA for period after Data for GATT/WTO membership is constructed from Rose (2013) and WTO website. Appendix A1 provides description of variables used in the analysis. The concept of region has a broad definition, from groups of countries sharing common identities to a supranational subsystem of the international system (Hettne 2005). This paper uses region as reference to group of countries with geographic proximity. The analysis focuses mainly on economic integration of two regions, namely EA and EU. As shown in Table 3, EU countries are EU-28 countries and EA has 15 economies. Region EU EA Table 3: Regional country groupings Countries Austria, Belgium, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, United Kingdom Brunei Darussalam, Cambodia, China, Hong Kong (China), Macao (China), Indonesia, Japan, Malaysia, Mongolia, Philippines, Republic of Korea, Singapore, Taiwan, Thailand, Viet Nam As production network is characterized by high value of intra-regional trade, this section studies the intra-regional trade in EA and EU with more details. The data shows great importance of intermediate goods in intra-regional export flows in both regions. During the period of analysis, the average share of intermediate goods in intra-regional exports for EU was 51 per cent and for EA was 64 per cent. Even though in absolute terms, intermediate export in EA is smaller than in EU, it takes up a much larger proportion of intra-regional export flow in EA than in EU. Figure 1 illustrates the intra-regional intermediate goods export value in sub-regions for both EA and EU. Intra-regional intermediate goods exports in both regions show similar pattern, where the export values started to take off after the 2000s. The trade reversal effect is evident in 2008, as the export value dropped for both regions. Since 2009, intermediate export value has recovered and continued its growing trend, where EA showed faster growth and caught up with EU in terms of absolute value. For sub-regions, when reducing the number of countries in EU from 28 to 15, the intra-regional export value reduces only 7

10 slightly. 2 This shows that most of trade activities in the region took place between the countries with EU membership before The enlargement after 2004 increased the number of member states, but had little effect in promoting trade relation between old and new members. On the contrary, when looking at sub-regions in EA, there is a dramatic fall in the value intra-regional intermediate exports. This suggests that there are extensive trade links between North East Asian (JCK) and South East Asian (ASEAN) economies. This is an indication that in the network of trade in EU, there is a center, a trading hub that connects other economies in the region. While in EA, the trading flows are intertwined and all countries trade with each other without a clear center of trade. Figure 1: Intra-regional intermediate goods export value in EA and EU (billions $) 2,000 2,000 1,500 1,500 1,000 1, intra EU28 intra EU15 Intra EA intra Asean intra JCK Note: JCK consists of Japan, the People s Republic of China and the Republic of Korea Source: Calculated based on RIETI (2013) Figures 2 and 3 illustrate the network connection in intermediate goods trade between countries in EA and EU. It is drawn using Stata code developed by Corten (2011) and Grund for intra-regional trade flows (at constant 2005 US dollar value) from 1998 to The graphs represent the network of trade flows during this period between countries. A connection between two nodes represents the existence of a positive bilateral trade value in intermediate goods. Without a cut off value, each country will have a connection with all other countries in the region. As the purpose of the graphs is to identify the key players of the production network, it should highlight the connections that represent large volume of intermediate goods flow. For this purpose, the graphs only demonstrate the connections that show the value of intermediate goods trade above $5 billion (grey lines) and $10 billion (red lines) in constant 2005 prices. These values are derived from real average trade values in EA 2 EU15 includes United Kingdom, France, Germany, Italy, Austria, Belgium, Luxembourg, Denmark, Finland, Greece, Ireland, Netherlands, Portugal, Spain, Sweden 3 Network analysis using Stata: 8

11 and EU during the period. The countries with most connection with other countries will be located in the center of the network, whereas countries with fewer or no connection will be on the verge of the graph. For countries with no connection with another country, it does not mean that they do not trade with others, but rather their intermediate trade value is smaller than the critical values to be illustrated in the graph. The first observation in Figure 2 is that the number of countries in EU not involved in the network is high. The numbers of countries with at least one connection with other countries in respective years are: 16/27 in 1998, 17/28 in 2003, 21/28 in 2008 and 20/28 in 2013, leaving close to a half of countries outside the network. The countries which do not have any connection with other countries are mostly new members of EU but there is also a long-time member namely Greece. While Finland joined EU in 1995, its intermediate trade flow only became significant in 2008, with Germany and Sweden. Slovakia and Romania made use of the accession in 2004 and 2007, to quickly integrate to the network. The role of Germany is clearly noticed, as it is always the center of the network and has the most connection with other countries. For countries like Romania and Hungary, trade relation with Germany is the connection showing their involvement in the network. Within the countries in the network, visually we can separate into two groups. First group consists of seven countries (Germany, France, Netherlands, United Kingdom, Italy, Belgium and Spain) in the center of the network, with many red line connections with other countries. This is no surprise, as these countries are expected to have large volume of trade with each other based on their economic size, proximity, historical and cultural links. The second group is the countries that have mostly grey intermediate trade connections with others. If we look at the dynamics of the network through time, there is not much change in these two groups. The core network trade was still happening between countries in the first group and countries in the second group are linked to the network by connection to one country, mostly Germany. The only countries that showed significant changes in terms of moving closer to the center of the network in this period are Poland and Sweden. This confirms that trade flows in Europe follow the hub-and-spoke pattern (Johnson and Noguera 2012). Figure 3 shows there were many changes in the network of intra-regional intermediate goods trade in EA during period. The number of countries in the network is: 10/15 in 1998, 10/15 in 2003, 11/15 in 2008 and 11/15 in There were four countries that did not have any links with other countries. These are Macao (China), Mongolia, Brunei 9

12 Figure 2: Network of intermediate goods trade in EU (trade flows above 5 billion and 10 billion in constant 2005 USD) Source: Authors own estimates 10

13 Figure 3: Network of intermediate goods trade in EA (trade flows above 5 billion and 10 billion in constant 2005 USD) Source: Authors own estimates 11

14 and Cambodia. The only country joined the network during the period is Vietnam since The role of Japan in EA network in 1998 was similar to Germany in EU, where it was a hub linking all other countries in the region. But in later years, this was not the case anymore as countries like China, Singapore and Korea moved closer to the center of the network. By 2013, China, Korea and Japan were all acting as center of the network, with red line connections with most of other economies in the region. Another striking difference between EA and EU is that countries involved in the EA network are trading with everyone in the network, dominating by red line connections (above 10 billion USD). Nine economies, naming Japan, China, Hong Kong (China), South Korea, Indonesia, Thailand, Malaysia, Singapore and Taiwan are all connected with large flow of intermediate trade, especially since Vietnam and Philippines are the newcomers to the network, and showed rapid increase in intermediate trade value in recent years. Although the number of countries in the network did not change much during the period, the number of connection changed dramatically for some countries. Most notable are Indonesia and Thailand, starting with only one significant trade flow with Japan in 1998 to 6 flows in The similar change was observed for Korea, Taiwan and Malaysia. This is a lot different from EU, where number of connection for countries was relatively stable. This shows that during the period, intermediate goods trade flow in EU was between traditional partners, while in EA there was a diversification of partners. All of this support the argument by Baldwin and Lopez-Gonzalez (2014) and Kimura, Takahashi, and Hayakawa (2007) that trade flows in EA follows more like a network rather than hub-and-spoke pattern. The final observation is the comparison of core groups in the network between EA and EU. In EU, it is the first group of seven countries and in EA, it is the nine countries mentioned above. The first difference, as noted earlier, is the vast changes in number of connection for East Asian countries while European countries stayed stable in the period. The second difference is while large trade volume is expected between seven economies in EU, it is more of a surprise given the level of diversity between countries for nine economies in EA. As Fort and Webber (2006) and Dent (2008) pointed out, EA is the most diverse region in the world. Although sharing geographic proximity and cultural links, these countries are different in terms of economic size, level of income, geographic area, population There is much tension between particular countries both historically and present. Unlike EU, there is no regional institution to regulate trade flows, only preferential trade agreements and regional 12

15 policy coordination. Yet, despite all this, EA network trade is growing and continuing to expand. 4. The gravity model and econometric method The econometric approach in this paper is based on the gravity trade model by Anderson and van Wincoop (2003). The gravity model has been the most successful in the empirical analysis of determinants of bilateral trade. In this paper, the log linear form of the pooled cross-section gravity equation of bilateral trade between exporter i and importer j is: ln ( ) = + ln( ) + ln + ln + + ln (1) + + where is the value of exports from country i to country j; and are economic mass of the countries i and j; is the distance between country i and country j, representing larger shipping expenses, cost of insurance or freight charges when countries are physically further away from each other. is remoteness of country pair i and j, to account for the multilateral resistance terms. is a set of seven dummy variables namely common border, colonial relationship, common colonizer, WTO membership, common language, same origin and common RTA to account for the subjective bilateral resistance. The global export flow analysis will be performed using both total goods and intermediate goods as the dependent variable and include all above bilateral resistance variables. The regional analysis for EA and EU will concentrate only on intermediate goods export flow and bilateral resistance variables will exclude WTO membership and common RTA. A set of year-specific fixed effect ( ) are included in these estimations to control for unexpected global variation during the period, such as the value of the US dollar, global business cycle or oil shocks. Most of gravity models so far, used country s GDP as the standard proxy for the economic mass variables ( and ). Baldwin and Taglioni (2011) argued that GDP is valueadded measurement and only takes into account the final goods and services, while trade value is measured in a gross basis. GDP is a good proxy for demand of destination country as well as proxy for supply of origin country if consumer trade dominates. As trade in parts and components is becoming more important, GDP of trading partners loses its explanatory power, and bilateral trade should be increasingly well explained by demand in third countries. With this idea, the authors proposed a new way of accounting for economic mass of trading partners. If the destination country of bilateral trade flow is in the production 13

16 network or supply chain, its import is more often a function of its export rather than its GDP. So to proxy for demand of importing country, the authors propose: = +, (2) The gross expenditure of the importing country is the demand shifter of the bilateral trade flow, which consist of both expenditure for final goods and intermediate goods. To account for demand of imported goods when trade in intermediate goods is becoming increasingly important, Baldwin and Taglioni (2011) added the sum of imported intermediates to destination country j across all its partners k,, to traditional GDP of destination country j. This implies that a destination country which imports more intermediate goods from different sources should have a larger economic mass. To avoid putting the bilateral trade flow from i to j on both sides of the estimation, the sum of imported intermediates to j will exclude the flow from i. The total cost in source country of bilateral trade flow will capture the gross output that will be produced. This cost is separated into cost of primary inputs and cost of intermediate inputs. The intermediate purchase can be from both local suppliers as well as imports, but accounting for the cost of whole nation, local intermediate purchases will cancel out, leaving only payments to local factors of production and import of intermediates: = + (3) where is the source country s value added in manufacturing and is its import of intermediate inputs from all partners k, except from itself. The sum of foreign intermediate inputs and domestic value added in manufacturing will represent the gross production possibility of source country. To compare the efficiency of their new economic mass variables, the authors included the ratio of intermediates in total bilateral trade, both on its own and interacted with the mass variables. They suggested that the use of new proxy gives better result in picking up demand and supply for intermediates than traditional use of GDP. Anderson and van Wincoop (2003) suggested that trade between two countries depends on bilateral barriers between them relative to average trade barriers that they face with all other trading partners. These average trade barriers are called multilateral resistances. The authors proposed the most efficient way to account for these terms is to use a custom 14

17 non-linear least-squares (NLS) estimation technique, but this technique proved to be too complicated for broader application in gravity model. A much more applicable method to account for multilateral resistance that has been used widely (Rose and van Wincoop 2001, Baldwin and Taglioni 2006) is to use importer and exporter dummies or country fixed effect. By using this technique, the fixed effect estimation will account for any country-specific factors that affect trade, including multilateral resistance and country s economic mass. The major drawback of fixed effects estimation is the demolition of structure, as most of variables of interests by economists or policy makers are country specific and will be omitted. To get over this problem, economists have suggested the use of different proxies or approximation for the multilateral resistance terms. The most popular way of accounting for third country effect on bilateral trade flows was the use of remoteness. This paper uses three specification of remoteness, denoted as Rem1, Rem2 and Rem3 respectively. These different specifications of remoteness all capture the average distance of country pair ij to other trading partners k but using different output weights. The intuition behind this is the measurement of weighted economic distance, with the inclusion of economic mass or output as the weight. The idea is that a larger trading partner will have a bigger impact on the value of remoteness of a country. The first measure of remoteness (Rem1) follows Melitz (2007): =, where is remoteness of exporting country, is economic mass of partner k (different from bilateral partner j), is total economic mass of all other trading partners k, is the distance from country i to partner k. The author used GDP as a proxy for economic mass. But this paper will use new economic mass proxy to account for the rising importance of intermediate goods trade. Thus, and will be derived based on equations (2) and (3). Similar formula will be used to calculate remoteness of importing country. Finally, remoteness of country pair is the product of remoteness of exporting country i and importing country j: (4) 1 = (5),, 15

18 The second measure of remoteness (Rem2) is called the output-weighted measure of remoteness by Baier and Bergstrand (2002) and Carrère (2006). From the same formula for multilateral resistance as Anderson and van Wincoop (2003) defined: = [ (1 + )(1 + )] where multilateral resistance of exporting country is determined by the number of varieties of goods produced in country k ( ), the price, transport cost factor, gross tariff rate and elasticity of substitution. Baier and Bergstrand (2002) proposed to proxy the multilateral resistance terms by normalizing prices and gross tariffs to unity, using GDP as a proxy for the number of varieties of goods, and bilateral distance ( ) as a proxy for transport cost. For this research, economic mass based on equations (2) and (3) will be used to proxy for number of varieties of goods instead of traditional GDP as Carrère (2006) did, and the elasticity of substitution =4. The multilateral resistance term for exporting country (and analogously for importing country) will now be calculated based on observable data: (6) =, (7) The remoteness of country pair is: 2 =,, (8) Rem2 still resembles the remoteness variable, as it is calculated based on gross output and distance of all trading partners. The advantage of Rem2 over Rem1 is it was derived from a theoretical model using some simplification techniques rather than purely an intuitive measure as Rem1 is. The final measure of remoteness (Rem3) follows Baier and Bergstrand (2009) by using Taylor s series approximation. To avoid non-linear procedure, multilateral resistance will be calculated by linear approximation using information of all bilateral resistance: 16

19 = ln( ) + ln ln( ),, + +,, (9) = + where the subscripts denotes countries, is the world multilateral resistance of bilateral trading partners i and j, is distance, is the set of bilateral trade resistance and the share of country s GDP in world GDP. The approximation procedure will be applied to all of observable factors of bilateral trade resistance, and the approximation for each factor is similar to the expression in square bracket in equation (9). The approximation closely resembles the use of remoteness with the inclusion of distance to other trading partners and share of output as the weight. The difference of this approach from previous use of remoteness is that previous research only accounted for multilateral distance and ignored other multilateral effects (Baier and Bergstrand 2009). In fact the first square bracket in equation (9), which the authors called the multilateral distance, can be defined as the remoteness variable, which is called Rem3 in this analysis. 3 = = ln( ) + ln ln( ) (10),, The detailed estimation forms and techniques are described in Appendix A2. Model (a) is the traditional panel data model, without accounting for the effect of multilateral resistance. The other specifications control for multilateral resistance using different approaches. Model (b) uses fixed exporter-time and fixed importer-time effect; models (c) and (d) use Rem2; models (e) and (f) use Rem1; and model (g) uses Rem3. The random effect is used instead of the pair-fixed effect, because the purpose of the analysis is to compare the impact of bilateral trade determinants, which would be omitted under fixed effect. The GLS random effects model requires that we treat the terms as random variables and assume that there is no correlation between and other explanatory variables. By using these models, some weaknesses must be mentioned. First, by using random effect estimation, there is a risk that some explanatory variables may be correlated with the bilateral random effect. Second, as Anderson and van Wincoop (2003) stated, remoteness indexes have only low correlation with multilateral resistance and are not in conformity with the theory. Thus, they 17

20 can introduce a bias. Even so, remoteness still captures some of the multilateral resistance effect and provides an intuitive explanation. 5. Analysis of the results 5.1. Determinants of total goods and intermediate goods exports: global analysis Table 4 reports the estimation results for both total and intermediate goods export flows by using the whole sample with Rem1 and Rem3. However, the separate estimation of total goods and intermediate goods may be accompanied by correlated estimation errors. That is unobservable factors, such as non-tariff barriers may simultaneously affect both final goods and intermediate goods export. Therefore random effect estimation is more preferable to pooled OLS and is generally more efficient as argued by Wooldridge (2009). For complete results of all specifications, refer to Appendix A3. Each model specification shows the result of a system of equations for bilateral exports in total goods and in intermediate goods. To test whether total goods and intermediate products have different export patterns, the Wald test of the null hypothesis that all coefficients are identical in both equations is performed. The tests are based on seemingly-unrelated regressions (SUR) and show that most of the estimated coefficients are significantly different between total goods export and intermediate goods export. Although the estimated coefficients differ in size between specifications, the significance and expected signs are consistent. Overall, distance between trading partners reduces export value, representing the impact of trade costs. Other trade determinants all have a positive impact on export flow as suggested by the theory. Neighbouring countries would likely to trade more with each other. Historical and social links such as colonial relationship, similar spoken language and being one same country at some point in time lead to higher export value for both total goods and intermediate goods. Different specifications of remoteness also have significant and positive effects on trade flows. This shows that as country pair becomes further away from the rest of their trading partners, the export value between the pair increases. 18

21 Table 4: Impact of trade determinants on total goods and intermediate goods export flow (1) (2) Total Intermediate Difference Total Intermediate Difference 1.193*** 1.226*** 0.033*** 1.325*** 1.369*** 0.045*** (0.009) (0.009) (0.001) (0.008) (0.009) (0.004) 0.945*** 0.965*** 0.020*** 0.991*** 1.045*** 0.054*** (0.008) (0.009) (0.001) (0.008) (0.009) (0.004) *** *** *** *** *** *** (0.025) (0.028) (0.001) (0.023) (0.026) (0.010) 1.010*** 1.186*** 0.176*** 0.820*** 0.963*** 0.143*** (0.127) (0.138) (0.005) (0.128) (0.141) (0.033) 1.256*** 1.301*** 0.045*** 0.825*** 0.887*** (0.120) (0.127) (0.005) (0.111) (0.123) (0..038) 0.473*** 0.448*** *** 0.774*** 0.836*** 0.062* (0.073) (0.080) (0.003) (0.075) (0.083) (0.032) _ h 0.151*** 0.227*** 0.076*** 1.034*** 1.243*** 0.208*** (0.024) (0.027) (0.002) (0.106) (0.120) (0.047) 0.622*** 0.575*** *** 0.750*** 0.705*** * (0.051) (0.057) (0.002) (0.052) (0.059) (0.023) 0.659*** 0.716*** 0.057*** 0.651*** 0.753*** 0.102*** (0.181) (0.187) (0.006) (0.178) (0.192) (0.039) 0.280*** 0.337*** 0.057** 0.653*** 0.613*** * (0.024) (0.029) (0.002) (0.051) (0.057) (0.022) 0.711*** 0.821*** 0.110*** 0.223*** 0.263*** 0.039*** (0.056) (0.063) (0.003) (0.013) (0.015) (0.006) Cons *** *** *** *** (1.035) (1.166) (0.381) (0.428) N R Notes: Standard errors in brackets. *, **, *** indicate significance at the level of 10%, 5% and 1%. Models (1) and (2) correspond to models (f) and (g) in Appendix A3. Source: Authors own estimates The first comparison of the impact of trade determinants on total export value and intermediate export value is the change of the coefficient for new economic mass variable. There is a small and statistically significant increase of the estimates moving from total goods export to intermediate goods export. This suggests that the market size effect is more important for intermediate goods than that for total goods. Previous studies, such as Miroudot and Ragoussis (2009), found that the impact of economic size on intermediate goods export is smaller than that on final goods export, but the measure of economic size was traditional GDP. This is not surprising, as GDP only shows consumer preference and expenditure for final goods. Intermediate goods and final goods are traded differently, as the buyers of intermediate input are not final consumers, but producers of a later stage. The demand 19

22 depends more on the suitability and applicability to the production process than on the preference and habits of final consumers. The use of new economic mass variables as in equations (2) and (3), will provide a more comprehensive estimate of the effect of market size. This is because the new economic mass variable captures both the impact on final goods through the value added measurement, and the impact on intermediate goods through the gross flow of intermediate goods. In both estimations for total goods and intermediate goods, we observe the same pattern that the estimate of exporting country is larger than that of importing country. This has been addressed as the home market effect or home bias effect in previous literature. The home market effect implies a positive link between a country s market size and its exports. Industries tend to concentrate in large economies, transforming these economies into net exporters (Hanson and Xiang 2004). Another implication from this pattern is that the export flow will be larger from big economy to small economy, or when the gap in economic size is larger, given the same combined size of any two trading partners. The second notable comparison is the change in the estimated effect of distance. There is a statistically significant increase in the elasticity of distance on intermediate exports compared to total exports. This increase ranges from 0.02 to 0.08 across different specifications. Although this is just a small increase, it is in line with the discussion of the increasing role of distance as a result of production fragmentation. The fragmentation theory suggests that the decision to open a new plant by firms comes at the trade-off between increased service link cost and decreased marginal cost from that new plant (Jones and Kierzkowski 2005). The service link cost positively correlates with distance, the further away the plant is. The higher the cost of transportation, communication and coordination will be. With longer distance between two countries, the firms will be less willing to create a production network and thus reducing the amount of intermediate goods traded. The just in time production model (Harrigan and Venables 2006) also points out that distance has a more pronounced impact on intermediate exports than that on total exports. Production networks are submitted to geographic and time constraints, where timely delivery of any fragments affects the outcome of the whole production chain. With long distance, the risk of failing to deliver in any stage of production also increases, making it less desirable to break up production process or trade in intermediate goods. Countries that share the border or same country origin have stronger effects on intermediate exports than those on total exports. As suggested by network view of trade by Rauch (1999), in case of intermediate goods export, the search is not only between the 20

23 producers and consumers, but also the search for suitable foreign input suppliers. For neighbours or countries sharing historical origin, firms will likely have better information about doing business, potential partners and even contacts who know the market. This could reduce the cost of setting up affiliates or managerial cost and speed up the production sharing process. Colonial links between trading partners do not seem to affect intermediate goods and total goods export differently. Although the estimate for colonial relationship increased slightly for intermediate goods, it is mostly statistically insignificant. Surprisingly, the estimate for common language decreases from total goods to intermediate goods, even though language also promotes existing link between partner economies. Melitz (2008) suggested that common language can promote trade by either direct communication or through translation. In the production process, direct communication or linguistic skills will be much more important than translation. But for trade flows, translation can act as a separate channel distinct from direct communication to also promote trade. Moreover, for some intermediate goods, such as primary products, the relevant information could be so small that there are no language hurdles at all. The results in Table 4 show that trade policy helps promote trade, with a significant and positive effect across all specifications, for both total and intermediate goods. WTO membership of both trading partners is more sensitive for intermediate goods trade, observed through significantly larger value of the estimated coefficient. When countries trade in intermediate goods, involving customized inputs or incomplete contracts, the effect of crossborder policy will be much more important in facilitating trade relation. For partners that are both in WTO system, it is more likely that they operate in a harmonized legal system, engage in tariff cutting, and create more incentives for international production fragmentation. Orefice and Rocha (2014) showed similar results, where there is a positive relationship between network trade and deeper integration. This integration includes provisions under current mandate of the WTO and beyond. The coefficient of other trade policy variable, regional trade agreement is also positive and significant in all specifications, suggesting that trade agreements are positively correlated with trade relation. However, the different impact of RTA on intermediate goods export and total goods export is inconsistent with that of WTO membership. RTA shows significantly a larger effect for intermediate goods in some models and a larger effect for total goods in others. It is worth mentioning that the analysis of policy variables can draw implication for the correlation between the variables and export value, but it indicates no 21