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An Empirical Analysis Of Cross-Border Trade Pattern Of Chiang Rai Nathapornpan Uttama Thanapat Boonserm Abstract This study aims to investigate cross-border trade flows between Chiang Rai province of Thailand and major trading partners. The data cover the periods from 2007 to 2012 using the panel data gravity model of trade. The findings reveal that size of population, trade openness and economic corridor development are positive factors influencing cross-border export flows from Chiang Rai to nearby economies. They also indicate that economic size of Chiang Rai, bilateral distance and free trade area agreement show a negative relationship towards Chiang Rai’s crossborder export volume. Keywords: Border trade, Gravity model, Panel data JEL Classifications: F10, F14, C33 1. Introduction Chiang Rai is one of the northern province of Thailand which is as a gateway to neighboring countries such as Myanmar, Lao PDR and South China. Nowadays, the economic context of Chiang Rai province in Thailand has remarkably changed. Chiang Rai experienced the fast economic growth in any major region in the last decade. Its annual average gross provincial products (GPP) growth in the last six-year period was 13 percent, whereas total increase in GPP over the last half decade was 84 percent (DFT, 2013). The primary reason of this high growth rate was due to cross-border export potential of Chiang Rai shown by raising the export annual growth rate by 33 percent during the period 2007-2012 (DFT, 2013). Indeed, the Chiang Rai’s cross-border trade was driven by extensive manufacturing production, retail and wholesale services, and logistics services. Moreover, the infrastructure, transportation and North-South economic corridor development along with the establishment of various regional economic cooperation and integration projects i.e. Ayeyawady-Chao Phraya-Mekong Economic Cooperation Strategy (ACMECS), Greater Mekong Subregion Economic Cooperation (GMS) and ASEAN-China Free Trade Area (ACFTA) have fostered the huge rise in cross-border trade and An Empirical Analysis Of Cross-Border Trade Pattern Of Chiang Rai
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investment activities in this area. This makes Chiang Rai as one of the fastest-growing regions in Thailand even the facilitation in cross-border trade, investment and transportation remain the obstacles to economic development in Chiang Rai. It is therefore interesting to ask whether the important factors influence bilateral cross-border trade between Thailand and neighboring countries. Quite a large number of studies are available on quantifying the determinants of bilateral trade flows using gravity model (Anderson and Wincoop, 2003). Most studies have paid attention to the impacts of economic size, population, income per capita, trade costs, bilateral distance, economic geography and economic integration on bilateral trade flows. For example, Novy (2013), Athukorala (2012), Li et al., (2012), Chen and Novy (2011), Martínez-Zarzosoet al., (2009), Baier and Bergstrand (2009), Wong (2008), Lee and Shin (2006), Carrère (2006), Okubo (2004), Filippini and Molini (2003) and Hassan (2001) showed empirical evidence to support the fact that an increase in economic size, population, common border and economic integration raise bilateral trade flows, whereas higher trade costs and distance discourage bilateral trade flows. Importantly, they found that the results are consistent with economic theory. Moreover, few empirical evidence determined the relationship between technological specialization on bilateral trade flows (Uchida and Cook, 2005) and the demand for transportation development (Ülengin et al., 2013). The former result confirmed the positive relationship between technology and trade flows, whereas the latter result presented the low demand level for road projects. It is wondering to ask why transportation development is not necessary to foster higher trade, economic growth and development. This paper seeks to contribute to the determinants of cross-border trade flows to Chiang Rai. The intuition seems straightforward: an increase in economic size, trade openness and regional economic integration result in more cross-border exports to neighboring counties. However, there is one more mechanism underlying this conventional wisdom: economic corridor network is not exogenous itself. Surging Chiang Rai’s cross-border exports may be due to the country’s economic corridor and production logistics development causing the massive rise of crossborder exports through this area. In addition, Chiang Rai has an attempt to become a logistics hub rather than a production base. Hence, ignoring this fact could make estimation results inaccurate. The remainder of the paper is organized as follows. In section 2, the stylized facts of crossborder trade in Chiang Rai province, Thailand are provided. Section 3 discusses the data and An Empirical Analysis Of Cross-Border Trade Pattern Of Chiang Rai
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methods used to derive revealed comparative advantage (RCA) index for cross-border trade of Thailand and the econometric approach taken to estimate the empirical gravity model. Estimated results are exhibited in Section 4. Final section presents the major conclusions and policy implications. 2. Stylized Facts of Border Trade in Chiang Rai Province Chiang Rai province is located at the northern part of Thailand in which it shares common land border with two neighboring countries i.e. Myanmar and Lao PDR and common river border with South China (Figure 1). It currently has three main border checkpoints: Chiang Khlong, Chiang Saen and Mae Sai. Chiang Khlong border area has the friendship bridge crossing Mekong river and road transport (R3A) linkage to Lao PDR and South China, as well as Mae Sai border area which has the friendship bridge crossing Maesai river and road transport (R3B) linkage to Myanmar and South China. The road transport development R3A and R3B are known as the North-South Economic Corridor development under the Greater Mekong Subregion (GMS) Economic Cooperation Program initiated in 1992. Moreover, economic geography of Chiang Khlong and Mae Sai border area are a little bit different with Chiang Saen border area that water transport plays vital role in connecting Lao PDR and South China. Figure 1: Border economic zone and north-south economic corridor
Source: http://www.adb.org/GMS/Economic-Corridors/gms-ec.asp
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Cross-border trade has long become a significant player in the regional economic and social development, especially, being as a source of income and employment. Figure 2 shows total cross-border exports as a share of gross provincial products and economic growth. Prior global financial crisis in 2009, year-on-year growth of cross-border exports to GPP rose slightly to 10 percent in 2009 and rapidly rose to approximately 38 percent and 20 percent in 2010 and 2011, respectively. This share soon declined at around one percent for the year 2012. Figure 2 also presents the GPP growth in Chiang Rai province; it fell dramatically from 15 percent in 2008 to one percent in 2009, the trend reversed again in 2010, with sporadic period of growth. The economic growth has shown higher growth than cross-border exports. Due to an economic insecurity, it resulted in higher volatility in the GPP growth rate since the end of year 2010. Interestingly, the change of the share of cross-border exports to GPP is not parallel with the change of the GPP growth rate. Figure 2: Cross-border exports of Chiang Rai province in Thailand, 2007-2012 0.25
25%
0.20
20%
0.15
15%
0.10
10%
0.05
5%
-
0% 2007
2008
2009 Export/GPP
2010
2011
2012
GPP growth
Source: DFT (2013) and CPO (2013), Authors’ contribution
Moreover, Figure 3 illustrates the compound growth rate of total cross-border exports of Chiang Rai to major trading partners during 2007-2012; it was computed 33 percent per annum. Quite unexpectedly, total cross-border exports in 2012 rose only by 11 percent, compared to 40 percent increase from the year before. It rose by 30 percent in Chiang Khlong and 23 percent in Ching Saen but it declined by two percent in Mae Sai. Surprisingly, Chiang Rai province smoothly passed the 2009 global economic and financial crisis even it marked a considerable slowdown in total cross-border exports in 2012.
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Figure 3: Growth of cross-border exports of Chiang Rai in Thailand, 2007-2012 25,000
Unit: Baht
20,000 15,000 CAGR = 30.96%
10,000 5,000 0 2007
2008
Chiang Khlong
2009 Chiang Saen
2010
2011
Mae Sai
2012 Total
Source: DFT (2013), Authors’ contribution
Summing up, it is commonly believed that cross-border economic zone development encourage trade flows to Thailand; and then it leads to foster regional economic growth and development, improve the well-being, and reduce an income inequality in Thailand and neighbouring countries. However, the contradiction between the first cases shown above entails further investigation of the determinants influencing cross-border trade between Thailand and neighbours. 3. Empirical Approach This section presents an empirical approach for analyzing empirically cross-border trade pattern. It starts with revealed comparative advantage index, empirical model construction, econometric tool selection and data collection. 3.1 Revealed comparative advantage index In fact, a competitiveness of cross-border trade measures a degree of cross-border trade in border zones in terms of an improvement of a size of its cross-border exports to a certain market. Based on aforementioned perspective, changes in a competitiveness of cross-border trade are able to assess through a revealed comparative advantage (RCA) index. This paper applies the standard RCA measure by Balassa (1965) to calculate competitive performance of An Empirical Analysis Of Cross-Border Trade Pattern Of Chiang Rai
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cross-border trade in Chiang Rai province of Thailand and neighboring countries. The RCA index is measured as the percentage share of a given product in country’s cross-border exports over the share of a given product in total exports of Thailand: ∑ ∑
∑ ∑
,
where Xij are cross-border exports of product j from border zone i. When RCA is above unity, border zone i’s competitiveness in product j is greater than the average competitiveness or border zone i has comparative advantage. 3.2 Model specification: Gravity model Based on the gravity model of trade Anderson and Wincoop (2003) and Shepherd (2013), the model specification to capture the determinants influencing cross-border export and trade flows in Chiang Rai province is shown in the following threshold specification: (1)
where =1,2,…,n is border zone index, =1,2,…,n is trading partner index, and t=1,2,…,T is the time index. Dependent variable, EXPORT is cross-border export and trade flows. Independent variable, GPP stands for gross provincial products, POP denotes population and OPEN denotes trade openness measured as the share of cross-border export and import in GPP. DIST is bilateral distance between trading partners. ACFTA and NSEC are dummy variables. ACFTA is ASEAN-China free trade area as proxy for regional economic integration, whereas NSEC is NorthSouth economic corridor development. All variables are in natural logarithms. This analysis has an attempt to estimate Chiang Rai’s cross-border trade flows. To obtain the concrete empirical results, the diagnostic tests such as Breusch-Pagan test, Jarque-Bera test and variance inflation factor are performed to explain the model qualification. The Breusch-Pagan test is to identify the heteroskedasticity; the Jarque-Bera test is to indicate the normality; and the variance inflation factor is to check the multicollinearity. Moreover, the Hausman specification test is used to select the most appropriate estimator between fixed effect model
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(FEM) and random effect model (REM) for a panel data model. All estimated results are computed on mathematical program. 3.3 Data source Data on GPP and population exploited to measure GPP per capita are obtained from the statistics of the Chiang Rai Provincial Office of Comptroller General of Thailand (CPO), whereas export and import at border checkpoints are used to measure cross-border-trade openness are extracted from the statistics of Department of Foreign Trade (DFT), Ministry of Commerce of Thailand. All data are available electronically. The dataset covers three cross-border zones (Chiang Khlong, Chiang Saen, Mae Sai) of Chiang Rai province in Thailand with three neighboring countries (Myanmar, Lao PDR, South China) during the period 2007 to 2012. All results are provided in Table 1. Table 1: Descriptive Statistics Bilateral Variables EXPORT TRADE GPP POP OPEN GPPCAP DIST ACFTA NSEC
Mean 8.096 8.205 11.228 14.003 -3.022 11.040 6.170 0.500 0.611
Standard Deviation 6.751 0.609 0.217 0.006 0.459 0.210 0.616 0.514 0.501
Minimum
Maximum
0.706 7.292 10.938 13.993 -3.645 10.760 5.579 0 0
9.175 9.187 11.550 14.012 -2.240 11.353 6.991 1 1
Note: The values are in log form. The number of observations is 18 followed by three border zones of Chiang Rai in Thailand and three neighboring countries over six time periods (2007-2012).
4. Empirical Results This section presents empirical evidence on comparative advantage of cross-border trade in Chiang Rai province, Thailand and determinants influencing cross-border export and trade in this area. An Empirical Analysis Of Cross-Border Trade Pattern Of Chiang Rai
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4.1 Revealed comparative advantage index Table 1 and 2 compare the comparative advantage in cross-border exports from Chiang Rai in Thailand to Lao PDR (and South China) and Myanmar, respectively, during 2007-2013 (6 months). During 2009-2013, Chiang Rai has strong comparative advantage in beverages and automotive tires on the Myanmar market, whereas it has comparative advantage in chicken meat, meat and edible parts of animal, and frozen and chilled pork on the Lao PDR market. Surprisingly, Chiang Rai has the comparative disadvantage in diesel and gasoline products on the Myanmar and Laotian market despite these products are primary exports to neighboring countries. Also, Chiang Rai has the comparative disadvantage in automobile parts and accessories on the Lao PDR market. In other words, Chiang Rai at Mae Sai border area has crossborder trade competitiveness in consumer goods and energy products, whereas Chiang Khlong and Chiang Saen have border trade competitiveness in food products and energy products. Table 1: RCA from Chiang Rai province, Thailand to Myanmar Items 2007 2008 2009 2010 Alcoholic beverages Diesel Non-alcoholic beverages Iron and Steel Gasoline Automotive tire Automobile parts and accessories
2011
2012
2013 (Jan-Jun)
1.78
3.11
3.15
2.64
2.33
2.16
2.25
0.96
0.61
0.40
0.40
0.43
0.66
1.00
3.25
3.06
2.75
1.94
1.41
1.59
1.70
0.78 1.62 1.67
0.52 1.60 1.78
0.98 1.29 2.09
0.86 1.00 2.03
1.40 0.85 1.83
2.19 0.82 1.41
1.54 1.03 1.99
0.14
0.34
0.18
1.04
1.11
1.32
1.24
Table 2: RCA from Chiang Rai province, Thailand to Laos Items 2007 2008 2009 2010
2011
2012
Source: DFT, (2013) and Author’s contribution
Chicken meat Meat and edible parts of an
0.00
0.00
0.00
18.35
11.88
11.93
2013 (Jan-Jun) 10.21
0.00
0.00
12.92
18.03
11.84
11.93
10.27
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Items animal Other livestock products Diesel Gasoline Automobile parts and accessories Frozen and chilled pork
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2007
2008
2009
2010
2011
2012
2013 (Jan-Jun)
0.00
0.72
0.67
0.33
4.04
6.59
7.05
0.59 0.97
1.89 1.76
1.84 2.11
1.09 1.05
0.80 0.72
0.76 1.08
0.67 0.98
0.43
0.52
0.54
0.61
0.25
0.37
0.29
0.00
0.00
5.16
7.89
11.28
8.55
8.27
Source: DFT, (2013) and Author’s contribution
4.2 Estimation results of cross-border trade flows Based on Equation (1), the estimation results of cross-border trade flows from Chiang Rai, Thailand to neighboring countries are shown in Table 3. There are two models (Model 1 and 2) that estimate the determinants of cross-border export and trade, respectively. Table 3: Fixed effect model and random Effect model of cross-border export and trade Model 1: Cross-border export Model 2: Cross-border trade FEM (1) REM (2) FEM (3) REM (4) GPP -1.506* 0.878* (-3.03) (3.22) POP 11.396** 8.873 3.795 26.275* (2.41) (1.09) (1.38) (6.48) OPEN 1.012* 0.816* 1.063* 1.039* (16.05) (13.95) (30.68) (17.05) DIST/GPPCAP -48.709* -3.714* -17.234* 0.221 (-5.39) (-8.50) (-9.49) (0.48) ACFTA -0.001 0.041 -0.009 0.062 (-0.03) (0.76) (-0.50) (1.15) NSEC 0.096** 0.202* -0.050* -0.027 (2.23) (4.32) (-2.25) (-0.57) Constant -104.33 -121.624 -32.056 -356.74* An Empirical Analysis Of Cross-Border Trade Pattern Of Chiang Rai
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Goodness of fit: Observations Adj.R2 Diagnostic tests: Jarque-Bera VIF Breusch-Pagan LM test Hausman test
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Model 1: Cross-border export FEM (1) REM (2) (-1.63) (-1.09)
Model 2: Cross-border trade FEM (3) REM (4) (-0.81) (-6.28)
18 0.377
18 0.475
18 0.996
0.634 10.54 4.290 1.740
18 0.994
2.004 4.41 1.280 1.720 26.41*
103.56*
Note: The subscripts * and ** denote the 1 and 5 percent significance levels respectively. The t-statistics and z-statistics are shown in parentheses.
The diagnostic test results in Model 1 and 2 are quite similar. Jarque-Bera test statistic indicates that error term is not normally distributed; Breusch-Pagan test statistic shows that hypothesis of homoskedasticity is rejected; and VIF index presents the absence of multicollinearity. Table 3 compares the estimated results with FEM and REM estimator for two models. According to the Hausman test, it confirms the presence of a correlation between individual effects and explanatory variables. Therefore, the FEM estimator is an appropriate tool for explaining the estimated results. In Model 1, the estimated results show that the coefficients of population, trade openness and economic corridor development are significantly positive. They are consistent with theoretical contexts and empirical findings in the trade literature. Moreover, the coefficients of GPP and the distance-GPP-per-capita ratio are significantly negative while the coefficient of ASEAN-China free trade area (proxy for economic integration) is negative but insignificant. This implies that a high amount of population, a higher degree of trade openness and a stronger North-South economic corridor network between the GMS economies are positively related to an increase in crossborder exports from Chiang Rai to Myanmar, Lao PDR and South China. On the contrary, an increase in GPP and regional economic integration are not related to an increase in cross-border exports at the border zones of Chiang Rai anymore. Similarly, the estimated results in Model 2 are quite similar to Model 1. The coefficient of population is positive but insignificant, whereas the coefficient of trade openness is significantly positive. Furthermore, the coefficients of An Empirical Analysis Of Cross-Border Trade Pattern Of Chiang Rai
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distance-GPP-per-capita ratio and economic corridor development are significantly negative while the coefficient of ASEAN-China free trade area is negative but insignificant. This implies that that an increase in trade openness and population are positively related to an increase in cross-border trade in Chiang Rai province. Conversely, an economic corridor network and regional economic integration are not main factors influencing change in cross-border trade in Chiang Rai. 5. Conclusion and Policy Implication This paper focuses on the determinants of cross-border export and trade flows from Chiang Rai in Thailand to three neighbouring countries: Myanmar, Lao PRD and South China. Data for three border zones in Chiang Rai over the period of 2007-2012 are used. The primary estimated results reveal that trade openness and North-South economic corridor network have positive relationship with cross-border export flows. On the contrary, GPP and FTA agreement have negative relation to cross-border export flows. Specifically, the determinants influencing crossborder trade flows are quite similar to cross-border export flows. The findings from this study have important policy implication. In particular, strengthening the North-South economic corridor network is aimed at boosting cross-border exports from Chiang Rai province, Thailand to nearby countries. It is possible that cross-border trade are extensive to other border zones in Thailand. Therefore, domestic and international policies and regional agreements on cross-border trade and investment liberalization and facilitation, financial security, and transportation stability should be tailored. These are beneficial to boost regional economic growth and sustainable development in Thailand. 6. Reference Anderson, J. E., & Wincoop, E. W. (2003). Gravity with Gravitas: A Solution to the Border Puzzle. American Economic Review, 93(1), 170–192. Athukorala, P. (2012). Asian trade flows: Trends, patterns and prospects. Japan and the World Economy, 24(2), 150–162. doi:10.1016/j.japwor.2012.01.003 Baier, S. L., & Bergstrand, J. H. (2009). Bonus vetus OLS: A simple method for approximating international trade-cost effects using the gravity equation. Journal of International Economics, 77(1), 77–85. doi:10.1016/j.jinteco.2008.10.004 Balassa, B. A. (1965). Trade Liberalisation and Revealed Comparative Advantage. The Manchester School, 33, 99–123. An Empirical Analysis Of Cross-Border Trade Pattern Of Chiang Rai
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Carrère, C. (2006). Revisiting the effects of regional trade agreements on trade flows with proper specification of the gravity model. European Economic Review, 50(2), 223–247. doi:10.1016/j.euroecorev.2004.06.001 Chen, N., & Novy, D. (2011). Gravity, trade integration, and heterogeneity across industries. Journal of International Economics, 85(2), 206–221. doi:10.1016/j.jinteco.2011.07.005 CPO. (2013). Statistics of gross provincial products. The Chiangrai Provincial Office of the Comptroller General. Retrieved from http://www.klangcri.com/ DFT. (2013). Statistics of border trade in Thailand. Department of Foreign Trade, Ministry of Commence, Thailand. Retrieved from http://www.dft.go.th/Default.aspx?tabid=164 Filippini, C., & Molini, V. (2003). The determinants of East Asian trade flows: a gravity equation approach. Journal of Asian Economics, 14(5), 695–711. doi:10.1016/j.asieco.2003.10.001 Hassan, M. K. (2001). Is SAARC a viable economic block? evidence from gravity model. Journal of Asian Economics, 12(2), 263–290. doi:10.1016/S1049-0078(01)00086-0 Lee, J.-W., & Shin, K. (2006). Does regionalism lead to more global trade integration in East Asia? The North American Journal of Economics and Finance, 17(3), 283–301. doi:10.1016/j.najef.2006.06.007 Li, L., Dunford, M., & Yeung, G. (2012). International trade and industrial dynamics: Geographical and structural dimensions of Chinese and Sino-EU merchandise trade. Applied Geography, 32(1), 130–142. doi:10.1016/j.apgeog.2010.10.017 Martínez-Zarzoso, I., Felicitas, N.-L. D., & Horsewood, N. (2009). Are regional trading agreements beneficial?: Static and dynamic panel gravity models. The North American Journal of Economics and Finance, 20(1), 46–65. doi:10.1016/j.najef.2008.10.001 Novy, D. (2013). International trade without CES: Estimating translog gravity. Journal of International Economics, 89(2), 271–282. doi:10.1016/j.jinteco.2012.08.010 Okubo, T. (2004). The border effect in the Japanese market: A Gravity Model analysis. Journal of the Japanese and International Economies, 18(1), 1–11. doi:10.1016/S08891583(03)00047-9 Shepherd, B. (2013). The Gravity Model of International Trade: A User Guide. ARTNeT Gravity Modeling Initiative, United Nations publication. Uchida, Y., & Cook, P. (2005). The Transformation of Competitive Advantage in East Asia: An Analysis of Technological and Trade Specialization. World Development, 33(5), 701–728. doi:10.1016/j.worlddev.2005.01.005 Ülengin, F., Özaydın, Ö., Ülengin, B., Kopp, A., Önsel, Ş., Kabak, Ö., & Aktaş, E. (2013). Are road transportation investments in line with demand projections? A gravity-based analysis for Turkey. Transport Policy, 29, 227–235. doi:10.1016/j.tranpol.2013.07.002 An Empirical Analysis Of Cross-Border Trade Pattern Of Chiang Rai
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Wong, W.-K. (2008). Comparing the fit of the gravity model for different cross-border flows. Economics Letters, 99(3), 474–477. doi:10.1016/j.econlet.2007.09.018
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