China's economic integration with the Greater Mekong Sub-region: An empirical analysis by a panel dynamic gravity model

Shahriar, Saleh; Qian, Lu; Kea, Sokvibol Working Paper China's economic integration with the Greater Mekong Sub-region: An empirical analysis by a panel dynamic gravity model Economics Discussion Papers, No. 2018-44 Provided in Cooperation with: Kiel Institute for the World Economy – Leibniz Center for Research on Global Economic Challenges Suggested Citation: Shahriar, Saleh; Qian, Lu; Kea, Sokvibol (2018) : China's economic integration with the Greater Mekong Sub-region: An empirical analysis by a panel dynamic gravity model, Economics Discussion Papers, No. 2018-44, Kiel Institute for the World Economy (IfW), Kiel This Version is available at: https://hdl.handle.net/10419/179241 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ Discussion Paper No. 2018-44 | May 29, 2018 | http://www.economics-ejournal.org/economics/discussionpapers/2018-44 China’s economic integration with the Greater Mekong Sub-region: an empirical analysis by a panel dynamic gravity model Saleh Shahriar, Lu Qian, and Sokvibol Kea Abstract The purpose of this study is to fill an existing gap in the literature by addressing the following research question: what are the major determinants of China’s regional economic integration with the Greater Mekong Sub-regional countries (GMS), namely; Cambodia, Laos, Myanmar, Thailand and Vietnam? The author measures the economic integration in terms of bilateral trade and foreign direct investment (FDI). In accordance with the literature, the present study adopts a panel gravity framework method to analyze the significant factors affecting the bilateral aggregate exports flows of China with five economies of the Greater Mekong sub-region. Data were collected from both the Chinese national and the international sources over the period of 23 years, spanning from 1993 to 2016. The time period was chosen on the consideration of data availability. The result shows that the gravity model is econometrically fitted to our dataset. Among other factors GDP, bilateral exchange rate, and population have a positive impact on regional trade integration with the GMS. The author´s second-stage regression analysis confirms that China’s accession to the WTO impacts positively on the bilateral trade. China’s accession to the WTO is a significant factor for facilitation of trade flows. As expected, distance hinders regional trade. Furthermore, the role of historical trade relationship between China and GMS countries is estimated in the dynamic model. The result shows that China’s trade relationship with GMS countries is determined historically. JEL F14, F15 Keywords China; regional economic integration; d ynamic gravity model; Greater Mekong Sub region (GMS); exports; panel data Authors Saleh Shahriar, Northwest A&F University, Yangling, Shaanxi, China Lu Qian, Northwest A&F University, Yangling, Shaanxi, China; [email protected] Sokvibol Kea, Northwest A&F University, Yangling, Shaanxi, China Citation Saleh Shahriar, Lu Qian, and Sokvibol Kea (2018). China’s economic integration with the Greater Mekong Sub-region: an empirical analysis by a panel dynamic gravity model. Economics Discussion Papers, No 2018-44, Kiel Institute for the World Economy. http://www.economics-ejournal.org/economics/ discussionpapers/2018-44 Received April 12, 2018 Accepted as Economics Discussion Paper May, 22, 2018 Published May 29, 2018 © Author(s) 2018. Licensed under the Creative Commons License - Attribution 4.0 International (CC BY 4.0) 2 1. Introduction International trade is an important driver of economic development. It is one of the most important catalysts of growth and productivity (Singh, 2010). It plays a substantial role in the economic development of China. R. C. Feenstra and Wei (2009) have introduced China’s growing role in global trade with regional levels integration. As they observed, “In less than three decades, China has grown from having a negligible role in world trade to being one of the world’s largest exporters, as well as a substantial importer of raw materials, intermediate inputs, and other goods” (R. C. Feenstra & Wei, 2009). China has already demonstrated extraordinary performances in export trade (Liang, 2008; Maddison, 2007; Rodrik, 2006). The country has achieved a double-digit growth for three decades. Some studies are available to support the assumption that to date regional economic integration is on the increase (Atkinson, 1998; Hossain, 2009; Lin & Wang, 2012; Plummer, Morgan, & Wignaraja, 2016; Pogoretskyy & Beketov, 2012; Roberts & Moshes, 2016; Rodríguez-Delgado, 2007; Teeramungcalanon, 2016; Yang & Martinez-Zarzoso, 2014). Recently, there has been a decline in China’s export-led economic growth performance. Since 2007-2008 global economic crisis, the Chinese economy is facing a slow pace of economic development. The Chinese economists prefer to call the slow trend as a ‘new’ normal growth. To address the problem of slow economic growth, the Chinese government has already introduced an interconnected chain of supply-side structural reforms. Moreover, in the newly formulated the “13th Five-Year Plan 2016-2020” (CCCPC, 2016), the government has promised to provide support to export-intensive industries. The Chinese government also began to implement anew and large-scale project, i.e. the “Belt & Road” Initiative, to facilitate the flows of trade and infrastructural development. However, there is definitional debate on the notion of regional economic integration (Schneider, 2017). Usually, economic integration is facilitated by means of regional trade, investment and connectivity (Dur, Baccini, & Elsig, 2014; Bassem Kahouli, 2016; Liu, 2016). Economic integration has both deepening and widening dimensions. It is a process of mutual agreement among countries in a geographic region that reduce trade barriers to the free flows of goods and services and factors of production among each other. It reduces trade costs and 3 increases economic activities or gross domestic product (GDP) and welfare among members of integrated nations. Economic integration essentially ‘involves the removal of barriers at and behind the border with the aim of increasing welfare from increased trade, investment and economic activity that comes from the ability to specialize, develop the economy and take advantage of mutually beneficial exchange with other countries’ (Armstrong, 2015). It is essentially a market building effort in the sense that ‘it creates a new market with new rules’ (Orcalli, 2017). There are five forms of integration such as free trade zone, custom union, common market, economic union, and full economic integration. China is making gigantic efforts to open up its economy to the outside world since 1978 economic reforms. After China’s accession to the WTO in 2001, the country has gone through a substantial process of trade liberalization and expansion of international trade (P. Lai, Du, Wang, & Chen, 2016). There are interesting questions among the academic circles with regard to the Chinese economy and implications of the its trading systems (Naughton, 2017; Schweickart, 2015). In that context, this paper aims to examine the determinants of China’s regional economic integration in the light of the gravity model of trade. The empirical evidence presented in the paper lends support to the hypothesis that China is regionally integrated with its five neighboring economies of the Greater Mekong Sub-region (GMS); namely, Cambodia, Laos, Myanmar, Thailand, Vietnam. The paper is organized as follows. Section 1 introduces the research objective along with a brief background of China-GMS trade relations within the larger global contexts. Section 2 provides a brief profile and overview of GMS and some emerging trends of global economic integration. A description of sample size, dataset, methodology, and the gravity model are given in the Section 3, followed by the results and discussion in Section 4. Finally, Section 5 makes the concluding observations. 4 2. Greater Mekong Sub-region (GMS) and Global Economic Integration 2.1. Brief Socioeconomic Profile of GMS The Greater Mekong Sub-region (GMS) comprising the Kingdom of Cambodia, the People’s Republic of China (Yunnan province), the Lao People’s Democratic Republic, and the Socialist Republic of Vietnam (see Figure 1), is home to some 250 million people who have had social, cultural and economic linkages dating back many centuries. With impressive GDP growth rates ranging between 5-10 percent per annum during the 1990s and early 2000s, the GMS region has recorded equally high rates of urbanization and economic development. To put the analysis into the proper perspective, some background information is presented in the Table 1 and Table 2.China’s commodity exports flows to these countries are shown in Appendix 2 to Appendix 5. Figure 1: Geographical Location(Google) 5 Table 1: Selected socioeconomic indicators of China Sl. Indicators 2012 2013 2014 2015 2016 1. Social Total Population (1,000 persons) 1,350,695 1,357,380 1,364,270 1,371,220 1,378,665 Population growth rate (annual %) 0.5 0.5 0.5 0.5 0.5 Urban population growth (annual %) 3.1 2.9 2.8 2.7 2.6 Poverty head count ratio at $1.9 a day (2011 PPP) (of population) 6.5 1.9 - - - 2. Economic GDP (growth rate) 7.9 7.8 7.3 6.9 6.7 Consumption Contribution to GDP growth (%) 4.3 3.7 3.7 4.6 4.7 Industrial Output (growth rate %) 8.1 7.7 7.0 6.0 6.0 Fixed Assets Investment Growth Rate 20.3 19.3 15.3 9.8 7.9 Share of Manufacturer sector in GDP 47.1 47.2 47.2 46.9 40.7 Share of Service Sector in GDP 44.1 44.4 44.6 45.1 50.7 Electricity production (growth rate %) 4.7 8.9 4.2 2.8 5.2 Electricity Consumption (growth rate %) 5.9 8.9 4.2 2.9 - Total energy consumption (growth rate) 3.9 3.7 2.1 1.0 - Total freight volumes (growth rates %) -0.7 1.6 -3.9 -11.9 -0.8 Railway freights volumes (growth rate) 12.1 9.1 -2.7 4.2 -3.7 CPI (%) 2.6 2.6 2.0 1.4 2.0 PPI (output price, %) -1.7 -1.9 -1.9 -5.2 -1.3 PPI (Input price, %) -1.8 -2.0 -2.2 -6.1 -1.9 3. Monetary and Income Indicators 6 Foreign exchange reserve (growth rate) 4.1 15.4 0.6 -13.3 -9.6 Real disposable Income per capita (Yuan) 18,310.8 20,167.1 21,966.2 23,821.0 Real disposable income per capita (growth rate%) - - 8.0 7.4 6.3 Nominal medium of disposable income per capita (Yuan) - 25,632.1 17,569.8 19,281.1 20,883.0 Nominal medium of disposable income per capita (growth rate %) - - 12.4 9.7 8.3 Real disposable wage per capita (Yuan) - 10,410.8 11,420.6 12,459.0 13,455.0 Real disposable wage per capita (growth rate) - - 9.7 9.1 8.0 4. Trade Share of Chinese exports in world exports 9.5 10.0 10.6 11.4 10.6 Share of Chinese imports in world imports 8.7 9.3 9.7 9.8 9.6 Exports (% of GDP) 25.4 24.5 24.1 22.0 19.6 Imports (% of GDP) 22.7 22.1 21.6 18.5 17.4 Total trade growth rate 6.2 7.5 3.4 -8.1 -6.8 Exports growth rate 4.3 7.2 0.5 -14.3 -5.5 Import growth rate 4.3 7.2 0.5 -14.3 -5.5 Trade surplus growth rate 48.7 12.5 47.9 55.0 -14.1 Share of service exports in total exports 9.3 8.8 8.9 9.2 9.5 Share of service imports in total imports 14.5 15.6 19.3 21.8 23.2 Share of processing trade 42.1 39.0 37.7 35.1 34.1 Inflow FDI growth rate (actual) -3.7 5.3 1.7 5.6 -0.2 Outflow FDI growth rate 17.6 22.8 14.2 18.3 - Source: Compiled by the authors from the World Bank Database (World_Bank, 2018); and Zhang (2017). 7 Table 2: Selected indicators of the Mekong Sub region Countries, 2016 Indicator Cambodia Lao PDR Myanmar Thailand Vietnam Total land area (km2) 181,035 236,800 676,576 513,120 331,231 Total population (1,000 persons) 15,158 6,621 52,917 67,455 92,695 Annual population growth (%) 1.2 2.0 0.9 0.3 1.1 GDP at current prices (US$ million) 19,194 15,903 68,636 407,048 198,196 GDP per capita at current prices (US$) 1,266 2,402 1,297 6,034 2,138 GDP per capita at current prices (US$ PPP) 3,848 7,123 5,959 17,273 6,325 International merchandise Export (US$ million) 10,073 3,124 11,509 215,327 176,575 International merchandise Import (US$ million) 12,371 4,107 15,696 194,668 174,463 International merchandise Trade, Total (US$ million) 22,444 7,231 27,205 409,994 351,038 Foreign direct investments inflow 2,280 1,076 2,989 2,553 12,600 Source: Compiled by authors from the ASEAN Statistics Database, http://www.aseanstats.org/ 2.2. The Emerging Trends of Global Economic Integration Regional cooperation and integration (RCI) is a well-known economic strategy. The strategy is especially adopted by the ADB. The RCI Strategy is anchored on four pillars: 1. Regional and Sub Regional Economic Cooperation Programs on Cross-border Infrastructure and Related Software - physical connectivity through regional and subregional infrastructure complemented by harmonized regulations, procedures, and standards that will facilitate cross-border trade. 2. Trade and Investment Cooperation and Integration - elimination of trade and investment barriers through improvement of the transparency, efficiency, and procedural uniformity of cross-border transportation of goods and services. 8 3. Monetary and Financial Cooperation and Integration - ensuring economic and financial stability through establishment of regional financial mechanisms 4. Cooperation in Regional Public Goods - promotion of regional public goods (RPG) through coordinated actions to supply RPGs, such as clean air, control of communicable diseases, and management of natural disasters. The effort to integrate the Asian economies is getting new impetus in the twenty first century. Regional trade and cooperation across the international borders have been used as a strategy to promote the growth and development of the peripheral areas in different parts of the worlds. There are many instances of horizontal and vertical economic integration initiative in Asia. Trade cooperation is one of the key issues in almost all economic integration and partnership agreements. Some notable regional arrangements and mechanisms are mentioned below: 1. Greater Mekong Sub-Region (GMS) involving Yunnan province of China, Vietnam, Laos, Cambodia, Thailand and Myanmar. 2. Indonesia, Malaysia and Thailand Growth Triangle (IMT-GT) involving Sumatra in Indonesia, peninsular Malaysia and southern Thailand. 3. Greater Tumen Initiative (GTI) involving four provinces of China, four eastern port cities of South Korea, three provinces of Mongolia and Russia’s Far East. The GTI is an intergovernmental cooperation framework supported by the United Nations Development Programs (UNDP). This GTI is originally known as the Tumen River Area Development Program. GTI’s one of the key priority areas is agricultural trade and investment 4. Southern Growth Triangle (SIJORI) involving Singapore, Johor state of Malaysia and Indonesian island of Batam in the province of Riau. The Singapore-Johor-Riau (SIJORI) Growth Triangle was first mooted in December 1989.The main areas for cross-border integration are the agriculture, fisheries and trade. 15 Figure 6: The conceptual and theoretical foundation of the gravity model Source: Yotov et al. (2016), p.12 Additionally, the next section will review some empirical works to detect the key factors of Chinese regional trade integration. There is indeed a huge volume of literature on the empirical studies of the trade gravity model. However, studies which investigate the determinants of Chinese trade integration with the Greater Mekong Sub region are much fewer in number. The related literature can be divided into two strands of research: i) investigation of trade patterns of China with other nations or a group of nations, and ii) using the gravity model to explore the issues of trade integration of several economic blocs. Poncet (2006) studied the process of economic integration between the Chinese border province of Yunnan and its riparian areas of the Greater Mekong Sub-region by using the gravity model for the period from 1988 to 1999. According to the study, Greater Mekong Sub-region cooperation efforts have positive effects on Yunnan’s trade. Hemkamon (2007)’s doctoral dissertation employed the gravity model to analyze the determinants of bilateral trade flows and the foreign direct investment of ASEAN countries. The work concludes that ASEAN 10 countries are strongly influenced by regional and global market forces. Edmonds, La Croix, and Li (2008) reviewed the public policies that shaped China’s manufactured export explosion and examined long trend statistics on the evolution of China’s trading 16 partners and the goods it traded in the post-reform period. The paper shows that China’s patters of trade changed dramatically to reflect its increasing market orientation and its evolving comparative advantage. The researchers applied the gravity model to explain the ‘export booms’ of China with 157 countries over the period 1985-2002. Ge, He, Jiang, and Yin (2014) used the gravity model to investigate the determinants of China’s cross-border trade with its 14 neighboring economies. They used disaggregated firm-level trade transaction data from the Chinese Customs for the period from 2000 to 2006. The findings showed that “income and the GDP growth rate of the destination countries are positively correlated”, whereas the low levels of “institutional quality of the importing country is negatively correlated with border exports from China”. Xinjiang is a Uygur autonomous region connecting China with Central Asia and South Asia. It is an important province for regional integration and trade facilitation of china under the belt and road initiative framework (Fan, Zhang, Liu, & Pan, 2016; Herrero & Xu, 2017; Huang, 2016; Yiwei, 2016). The result shows Xinjiang has trade integration with Central Asia, Central and Eastern Europe, Western Europe, East Asia and South Asia. But its foreign trade with West Asia is much lower for the existence of trade barriers. Caporale, Sova, and Sova (2015) analyzed the Chinese trade flows with its 190 countries for the period 1992-2012. They estimated the gravity model along with panel data with the fixed effects vector decomposition (FEVD) techniques. They model bilateral exports as a function of GDP, the difference in per capita income, geographical distance, FDI inflows and other the dummy variables. The findings confirm the significant change in China’s trading structure associated with the fast growth of foreign trade. In particular, there has been a shift from resourceand labor-intensive to capitaland technology-intensive exports. The results show that trade on the whole is fostering the Chinese economic growth. Arvis, Duvan, Shepherd, and Raj (2016) used a dataset of 167 developing countries to estimate the trade costs for the period, 1996-2010. The gravity analysis shows that among developing countries 17 African and low-income countries have high level of trade costs. The study reports that distance, regional trade agreements, maritime transport connectivity and trade facilitation performance are important determinants of trade costs. Studies reveal that there are positive economic effects of cultural institute on trade and investment. For instance, the gravity analysis of Rauch and Trindade (2002) reports that ethnic Chinese network have an important impact on bilateral trade. Also, Gao (2003), and Ghosh, Lien, and Yamarik (2017) shows that the presence of Confucius Institute in the source countries positively affect the trade and FDI flows. Similarly, the effects of the British Council on trade and investment are positive (Lien & Lo, 2017). Table 5 provides some summary information of existing literatures implementing gravity model to investigate international trade. Table 5: Summary of existing literature Authors Region / Economic Block Methodology Main results and conclusion Irshad, Xin, Shahriar, and Arshad (2018) OPEC & China OLS, Fixed effect model The researchers applied the panel data gravity model to analyze China’s trade patters with 14 OPEC member countries for the period, 19902016.The study confirms that China’s bilateral trade with OPEC member countries positively impacts on GDP, GDP per capita income, trade openness in China and WTO membership. As usual, the distance has a negative impact on trade. Rasoulinezhad and Wei (2017) OPEC & China Fixed effects (FE), Random effects (RE), and the The study analyzes the trade patterns between China and 13 OPEC member countries over the period 1998-2014 by using the panel-gravity model. It confirms the existence of long-term relationships between the bilateral trade flows and 18 FMOLS approaches the main components of gravity model-GDP, income (GDP per capita), the difference in income, exchange rate, the openness level, distance, and WTO membership. Gashi, Hisarciklilar, and Pugh (2016) EU & Kosovo Dynamic Panel Poisson Gravity model Higher transaction costs are barriers to Kosovo’s market integration into the EU. Need for personal and community networks for trade facilitations and integration. Narayan and Nguyen (2016) Vietnam and her 54 partners Unit roots model, Cointegration method The authors examine the trade issues of Vietnam with its 54 partners. The conclusion is that the influence of trade gravity variables is dependent on trading partners. For instance, trade with rich nations is more sensitive to distance, economic size, and trading partners, openness of trading partners, and exchange rate, than trade with low income nations. B. Kahouli and Maktouf (2015) Mediterranean area GMM estimation, panel model The study examines cross-section and panel of 27 countries for 1980-2011. The gravity results show the existence of a strong relationship between the factors of free trade agreements and trade flows. Geda and Seid (2015) Africa PPML, Tobit The authors have examined the nature and potential for trade and regional integration in Africa. They found a low level of integration due to the absence of infrastructure. The results from the gravity model and revealed comparative advantage demonstrate the problems of trade 19 competitiveness and integration of the African countries. Moinuddin (2013) South Asia Panel least square, Random & Fixed effect The gravity analysis shows that compared to other regions such as East Asia, Latin America and North America, South Asian region is lagging behind in terms of regional integration Kabir and Salim (2010) BIMSTEC Panel estimation The researchers find the gravity results are meaningful to explain the trade patterns of the 7 South East and South Asian Countries. They found that both GDP and good governance positively influence the bilateral trade and create an environment for regional trade integration Gu (2008) OECD & China OLS The thesis on the extended gravity analysis reports that GDP per capita and population have strong effects on China’s exports. Trade cooperation positively effects on the export trade. Sharma and Chua (2000) South East Asia Gravity model The study focuses on the economic integration of the 5 ASEAN economies namely Indonesia, Malaysia, Philippines, Thailand and Singapore. The estimated gravity model reveals that trade in ASEAN countries increases with the size of the economy. But there was gap in intra-ASEAN trade. Source: Organized by authors 20 Prior studies on China’s trade integration show that there is a deficiency in the literature with regard to the empirical analyses of China’s economic integration with the Greater Mekong Sub-region. To the best of the author’s knowledge, this paper is the first attempt to study the export dynamics between China and the Greater Mekong Sub region. The paper is timely and important for a number of reasons. First, the study for the first time will apply the gravity model to explain the nature of China’s trade integration with the MSR economies. Second, despite China remains a ‘global trade power’ (B. Naughton, 2007), our knowledge on the country’s trade relations with its GMS neighbors is limited. Third, the economic rise of China is a recent global phenomenon. It is projected that China would become the single largest economic power by 2050 with a reasonable economic size of 58.499 trillion US dollar (Appendix 1). Fourth, there is a section titled New Export Strength in recently formulated “13th Five Year Plan (2016-2020)” of China (CCCPC, 2016). According to the plan: “We will move faster to make our export-intensive industries more internationally competitive in terms of their technology, standards, brand names, quality, and services; encourage the export of high-end equipment; and increase the use of high technology and the value-added of our exports. We will expand the export of services, improve after-sale maintenance and repair services, and coordinate the development of onshore and offshore outsourcing. We will increase support to the exports of micro, small, and medium enterprises.” It is, therefore, crucial to find out the influencing factors of bilateral exports from the policy perspective of China. Finally, since 2013 the Chinese government is implementing the Belt and Road Initiative. The initiative is aimed to integrate China with the rest of the world. The facilitation of the free flows of trade is a fundamental economic goal of the “Belt & Road” Initiative (Lemoine & Unal, 2017). Therefore, there should not be any question that studying such a topic is of great importance from both academic and policy implications. 21 3.2. Sample, Data, Methodology, and Empirical Model Specification For author(s)’s knowledge, the studies on the trade relations between China and GMS economies tend to remain in a few number, which allowing this study to focuses on bilateral trade integration between China and the GMS economies. Our dataset covers panel data over time period from 1993 to 2016. The dependent variable used in this paper is the total export flows of China. Several researchers like Baltagi, Egger, and Pfaffermayr (2014) and Egger (2002) suggested to employ panel data methodology in the estimation of the gravity model to overcome the biasness and problems of the time series and cross-sectional data, and panel data econometrics has some basic modelspooled OLS, fixed and random effects models. The gravity model explains international trade flows as a log-liner function of income and distance between countries. It predicts that bilateral trade depends positively on income and negatively impacted by distance. This basic concept came from Newton’s Theory of Gravity discovered in 1687. According to the Law of universal gravitation, the standard gravity model simply describes that the trade between two countries is determined positively by each country’s GDP, and negatively by the distance between them. This formulation can be generalized as follows: 𝑋  = 𝛽  𝑌    𝑌    𝐷    (1) where 𝑋 is the flow of exports into country 𝑗 from country 𝑖 , 𝑌 and 𝑌  are country 𝑖’s and country 𝑗’s GDPs and 𝐷 is the geographical distance between the countries’ capitals. The linear form of the model is as follows: log  𝑋   = 𝛼 + 𝛽  log ( 𝑌  ) + 𝛽  log  𝑌   + 𝛽  log  𝐷   (2) The generalized gravity model of trade states that the volume of exports between pairs of countries, 𝑋 , is a function of their incomes (GDPs), their populations, their distance (proxy of transportation costs) and a set of dummy variables either facilitating or restricting trade between pairs of countries. That is, 22 𝑋  = 𝛽  𝑌    𝑌    𝐿    𝐿    𝐷    𝐴    𝜀  (3) ln  𝑋   = 𝛼 + 𝛽  ln ( 𝑌  ) + 𝛽  ln  𝑌   + 𝛽  ln ( 𝐿  ) + 𝛽  ln  𝐿   + 𝛽  ln  𝐷   + 𝛽  ln  𝐴   + 𝜀  (4) where 𝑌 (𝑌  ) indicates the GDP of the country 𝑖 (𝑗), 𝐿 ( 𝐿 ) are populations of the country 𝑖 (𝑗), 𝐷 measures the distance between the two countries’ capitals (or economic centers), 𝐴 represents other factors that might affect export flow (mostly, dummy variables), 𝜀 is the error term and 𝛽 are parameters of the model. This study follows the frameworks of the most updated development of gravity model, which introduced in the research works of Narayan and Nguyen (2016), Rasoulinezhad and Wei (2017) and Irshad et al. (2018). These researches included bilateral exchange rate, openness and population variable into the gravity model and proved helpful in explaining trade variations between trading partners. Therefore, by inclusion of these variables, our empirical gravity models can be expressed as follows: ln 𝐸𝑥𝑝𝑜𝑟𝑡  = 𝛽  + 𝛽  ln 𝐺𝐷𝑃  + 𝛽  ln 𝐺𝐷𝑃  + 𝛽  ln 𝑃𝐶𝐺𝐷𝑃  + 𝛽  ln 𝑃𝐶𝐺𝐷𝑃  + 𝛽ln𝐷𝑃𝐶𝐺𝐷𝑃 + 𝛽ln𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 + 𝛽ln𝐵𝐸𝑋𝐶𝐻 + 𝛽  ln 𝑂𝑝𝑒𝑛𝑛𝑒𝑠𝑠  + 𝛽  ln 𝑃𝑂𝑃  + 𝜀  (5) where, subscript terms 𝑖, 𝑗, and 𝑡 denote exporting country (i.e. China for the this study), importing country (i.e. GMS economies), and time period respectively. 𝛽 and 𝛽 correspondingly indicates the country-specific intercept term and estimated coefficients. 𝐸𝑥𝑝𝑜𝑟𝑡 is the total bilateral export value between country 𝑖 and 𝑗. 𝐺𝐷𝑃 (𝐺𝐷𝑃 ) is Gross Domestic Product (GDP) in millions of US dollars of country 𝑖 (𝑗), and 𝑃𝐶𝐺𝐷𝑃 (𝑃𝐶𝐺𝐷𝑃) represent Per Capita GDP of country 𝑖 (𝑗), while 𝐷𝑃𝐶𝐺𝐷𝑃 is the absolute difference value between GDP per capita of country 𝑖 and 𝑗. 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 indicate the geographical distance (in kilometer) between country 𝑖’s capital (Beijing, China) and 𝑗’s capital. 𝐵𝐸𝑋𝐶𝐻 is bilateral exchange rate of country 𝑗’s currency against country 𝑖’s 23 currency (Chinese Yuan) at time 𝑡. 𝑂𝑝𝑒𝑛𝑛𝑒𝑠𝑠 is trade openness level of country 𝑗 within time 𝑡. 𝑃𝑂𝑃  represent the population of the GMS countries at time 𝑡, 𝜀 is an error terms. Furthermore, Gashi et al. (2016) expressed that countries with a history of trading with one another continue to do so either for political, economic, policy, or other related reasons, thus, changes in trade flows can produce effects with significant persistence. Moreover, the authors also argue that the omission of historical factors is likely to bias estimated trade effects. Specifically, such kind of results may produce omitted variable bias. Our empirical gravity model, therefore, will also include dynamic factor variable to investigate the effect of historical bilateral trade relationship on current trade flow. Thus, empirical gravity models in Equation 5 can be transformed into Equation 6 by including variable 𝐸𝑥𝑝𝑜𝑟𝑡, as follows: ln 𝐸𝑥𝑝𝑜𝑟𝑡  = 𝛽  + 𝛽  ln 𝐸𝑥𝑝𝑜𝑟𝑡  ,    + 𝛽  ln 𝐺 𝐷 𝑃  + 𝛽  ln 𝐺𝐷𝑃  + 𝛽  ln 𝑃𝐶𝐺𝐷𝑃  + 𝛽ln𝑃𝐶𝐺𝐷𝑃 + 𝛽ln𝐷𝑃𝐶𝐺𝐷𝑃 + 𝛽ln𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 + 𝛽ln𝐵𝐸𝑋𝐶𝐻 + 𝛽  ln 𝑂𝑝𝑒𝑛𝑛𝑒𝑠𝑠  + 𝛽  ln 𝑃𝑂𝑃  + 𝜀  (6) where 𝐸𝑥𝑝𝑜𝑟𝑡, is the lagged variable (or dynamic / historical trade) of export flow between country 𝑖 (China) and country 𝑗 (GMS countries), and other variables’ notation are same as in Equation 5 above. More importantly, according to Narayan and Nguyen (2016), for avoiding inaccurate estimation of the parameters, the model of Equation 6 should be sub-divided into three different models, i.e. Model I, Model II, Model III as showing in Equation 7, 8, 9 follows, in which the income variables (GDP, GDP per capita, and the absolute difference value between GDP per capita) appear separately in each and the remaining factors are consistent. 24 Thus, this study’s Empirical Gravity Models are expressed as follows: Model I ln 𝐸𝑥𝑝𝑜𝑟𝑡  = 𝛽  + 𝛽  ln 𝐸𝑥𝑝𝑜𝑟𝑡  ,    + 𝛽  ln  𝐺𝐷𝑃  . 𝐺𝐷𝑃   + 𝛽ln𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 + 𝛽ln𝐵𝐸𝑋𝐶𝐻 + 𝛽ln𝑂𝑝𝑒𝑛𝑛𝑒𝑠𝑠 + 𝛽  ln 𝑃𝑂𝑃  + 𝜀  (7) Model II ln 𝐸𝑥𝑝𝑜𝑟𝑡  = 𝛽  + 𝛽  ln 𝐸𝑥𝑝𝑜𝑟𝑡  ,    + 𝛽  ln  𝑃𝐶𝐺𝐷𝑃  . 𝑃𝐶𝐺𝐷𝑃   + 𝛽ln𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 + 𝛽ln𝐵𝐸𝑋𝐶𝐻 + 𝛽ln𝑂𝑝𝑒𝑛𝑛𝑒𝑠𝑠 + 𝛽  ln 𝑃𝑂𝑃  + 𝜀  (8) Model III ln 𝐸𝑥𝑝𝑜𝑟𝑡  = 𝛽  + 𝛽  ln 𝐸𝑥𝑝𝑜𝑟𝑡  ,    + 𝛽  ln  𝐷𝑃𝐶 𝐺𝐷𝑃   + 𝛽ln𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 + 𝛽ln𝐵𝐸𝑋𝐶𝐻 + 𝛽ln𝑂𝑝𝑒𝑛𝑛𝑒𝑠𝑠 + 𝛽  ln 𝑃𝑂𝑃  + 𝜀  (9) In accordance with the theoretical structure of the gravity model, it is anticipated that economy size and income (GDP per capita) would have positive impacts on trade flow and promote trade between China and GMS economies. The effect of the third income measure is ambiguous. The coefficient can have a positive sign, if countries have the Heckscher–Ohlin (H-O) bilateral trade pattern, while the negative sign of this variable can appear under the Linder hypothesis. The coefficient for the bilateral exchange rate of 𝑗’s currency against Chinese yuan is expected to be negative (for instance, any increase in the GMS’s currency compare to Chinese yuan leads to decrease in trade flows between China and GMS economies). The more open the country economy the more it will trade. So, we are excepting the positive sign for economy openness. For population, MartinezZarzoso and Nowak-Lehmann (2003) point out that the coefficient of population can be negative or positive signed, depending on whether the country exports less when it is big (absorption effect) or whether a big country exports more than a small country (economies of scale). 31 Table 9: Secondstage regression: Fixed effects regressed on dummy (China’s WTO membership in December 2001) ln𝐸𝑥𝑝𝑜𝑟𝑡 Model I Model II Model III Fixed Effect Fixed Effect Fixed Effect Coefficient Prob. Coefficient Prob. Coefficient Prob. ln 𝐸𝑥𝑝𝑜𝑟𝑡  ,    ∗ 0.14 0.23 0.14 0.23 0.14 0.22 ln 𝐺𝐷𝑃  1.43*** 0.00 ln 𝐺𝐷𝑃  ∗ 0.00 1.00 ln 𝑃𝐶𝐺𝐷𝑃  1.43*** 0.00 ln 𝑃𝐶𝐺𝐷𝑃  ∗ 0.00 1.00 ln 𝐷𝑃𝐶𝐺𝐷𝑃  1.43*** 0.00 ln 𝐵𝐸𝑋𝐶𝐻  -0.38*** 0.00 -0.38*** 0.00 -0.38*** 0.00 ln 𝑂𝑝𝑒𝑛𝑛𝑒𝑠𝑠   ∗ 0.33 0.20 0.33 0.20 0.33 0.18 ln 𝑃𝑂𝑃  ∗ -21.06 0.11 -21.06 0.11 -21.10 0.11 𝑊𝑇𝑂  0.28** 0.01 0.28** 0.01 0.28** 0.01 𝐶𝑜𝑛𝑠𝑡𝑎𝑛𝑡 1.87** 0.01 1.87** 0.01 1.87** 0.01 No. of groups 5 5 5 No. of observations 115 115 115 𝑅  : within 0.9665 0.9665 0.9665 between 0.2236 0.2236 0.2236 overall 0.5736 0.5736 0.5736 Note: The (*) on variable name represent the first-different value. (*, **, ***) on the coefficient represent the significant level 10%, 5% and 1%, respectively. Prob. = Probability. 32 5. Conclusions In this study, an attempt is made to explore the major determinants influencing the china’s export to the MSR. We have filled a void in the current literature with regard to China’s economic integration with the MSR economies. Depending on the nature and availability of data, we have chosen a period of 23 years ranging from 1993 to 2016. Three has been a rising trend of global and regional economic integration. Since the 2007 global economic crisis, China has been encountering the slow pace of economic growth. It is according to the Chinese economists a new normal economic growth. The government of China in its new Five-Year Plan has emphasized to accelerate the exports flows of China. The government also initiated a large-scale project-the Belt and Road-to boost up its economic development. One of the main goals of the Belt and Road Initiative is to facilitate the free flows of trade. The trade pattern between China and MSR economies are mutually interdependent having a strong historical tie of economic cooperation. The basic premise of the cooperation could be highlighted by the principle of comparative advantage. China has dominated consumer and electronic goods in these markets. In this study we applied panel gravity model to identify the major factors of regional economic integration. The results demonstrated that GDP, GDP per capita, openness, bilateral exchange rate and population have positive influence on bilateral export, whereas distance is an impediment to trade. China’s membership has significant impact on bilateral export between China and GMS economies. It is revealed that China is integrated with the GMS economies. We encountered the problem of data limitations. There are some unobserved factors such as border conflict, tariffs, pricing, import substitution policy, language and policy variables that could have significant impact on trade relations between China and GM. Further research is needed to explore new factors with larger dataset. 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World Economy, 40, 2491-2499. doi:10.1111/twec.12569 39 Appendix 1: Projection of the most powerful economies in the world, by 2050 Rank Country Size of Economy (Trillion USD) Rank Country Size of Economy (Trillion USD) 1 China 58.499 17 Iran 3.900 2 India 44.128 18 South Korea 3.539 3 USA 34.102 18 Philippines 3.334 4 Indonesia 10.502 20 Vietnam 3.176 5 Brazil 7.540 21 Italy 3.115 6 Russia 7.131 22 Canada 3.100 7 Mexico 6.863 23 Bangladesh 3.064 8 Japan 6.779 24 Malaysia 2.815 9 Germany 6.138 25 Thailand 2.782 10 UK 5.369 26 Spain 2.732 11 Turkey 5.184 27 South Africa 2.570 12 France 4.705 28 Australia 2.564 13 Saudi Arabia 4. 694 29 Argentina 2.365 14 Nigeria 4.348 30 Poland 2.103 15 Egypt 4.333 31 Columbia 2.074 16 Pakistan 4.236 32 Netherlands 1.496 Source: Compiled by the authors from Martin (2017). 1.50 2.07 2.10 2.37 2.56 2.57 2.73 2.78 2.82 3.06 3.10 3.12 3.18 3.33 3.54 3.90 4.24 4.33 4.35 4.69 4.71 5.18 5.37 6.14 6.78 6.86 7.13 7.54 10.50 34.10 44.13 58.50 0.00 2.50 5.00 7.50 10.00 12.50 15.00 17.50 20.00 22.50 25.00 27.50 30.00 32.50 35.00 37.50 40.00 42.50 45.00 47.50 50.00 52.50 55.00 57.50 60.00 62.50 Netherlands Columbia Poland Argentina Australia South Africa Spain Thailand Malaysia Bangladesh Canada Italy Vietnam Philippines South Korea Iran Pakistan Egypt Nigeria Saudi Arabia France Turkey UK Germany Japan Mexico Russia Brazil Indonesia USA India China Size of economy (Trillion US$) 40 Appendix 2: Products exports by China to Laos, 2016 Reporter Partner Year Trade Flow Product Group Export (1,000 US$) China Lao PDR 2016 Export Capital goods 558,985.81 China Lao PDR 2016 Export Consumer goods 121,601.12 China Lao PDR 2016 Export Intermediate goods 240,272.47 China Lao PDR 2016 Export R aw materials 10,097.30 China Lao PDR 2016 Export Animal 3.20 China Lao PDR 2016 Export Chemicals 28,966.67 China Lao PDR 2016 Export Food Products 23,330.64 China Lao PDR 2016 Export Footwear 1,389.60 China Lao PDR 2016 Export Fuels 12,252.31 China L ao PDR 2016 Export Hides and Skins 258.29 China Lao PDR 2016 Export Mach and Elec 443,813.37 China Lao PDR 2016 Export Metals 187,057.92 China Lao PDR 2016 Export Minerals 1,710.53 China Lao PDR 2016 Export Miscellaneous 83,430.90 China Lao PDR 2016 E xport Plastic or Rubber 20,274.90 China Lao PDR 2016 Export Stone and Glass 10,036.14 China Lao PDR 2016 Export Textiles and Clothing 22,325.82 China Lao PDR 2016 Export Transportation 127,838.91 China Lao PDR 2016 Export Vegetable 1,672.07 China Lao PDR 2016 Export Wood 22,605.34 Source: Calculated by authors from World Integrated Trade Solution (WITS, 2018) Capital goods 29.15% Mach and Elec 23.14% Intermediate goods 12.53% Metals 9.75% Transportation 6.67% Consumer goods 6.34% Miscellaneous 4.35% Chemicals 1.51% Food Products 1.22% Wood 1.18% Textiles and Clothing 1.16% Plastic or Rubber 1.06% Fuels 0.64% Raw materials 0.53% Stone and Glass 0.52% Minerals 0.09% Vegetable 0.09% Footwear 0.07% Hides and Skins 0.01% Animal 0.00% Other 4.17%