impact of myanmar’s trade liberalization on the …

Applied Econometrics and International Development Vol. 16-2 (2016)

IMPACT OF MYANMAR’S TRADE LIBERALIZATION ON THE COUNTRY’S INTERNATIONAL TRADE ENVIRONMENT: A GRAVITY

APPROACH Jinhwan OH1

Kyi Cin THANT2 Abstract Using a gravity model with a panel dataset encompassing ASEAN countries and their 85 trading partners for a 15-year time period (1994-2008), this study empirically examines ASEAN members’ trade pattern. Then, using this empirical result, we simulate Myanmar’s gravity-based predicted trade flows. This simulation reveals that Myanmar’s trade has been distorted due to political factors, including economic sanctions. However, the degree of distortion has been varying over countries; distortion with Japan has declined while distortion with the U.S. has increased. As a result of these political factors, economic contact with Myanmar’s neighboring countries, particularly with Thailand has skyrocketed. JEL Code: F10, O10 1. Introduction

It may come as a surprise to many that until the early 1940s, Myanmar was the leading economy in the Southeast Asia region and the world’s top rice exporter (Than 1992). With well-educated elite and rich and diverse natural resources such as timber, oil and precious stones, Myanmar was once regarded as the most promising economy in the region after its independence in 1948; its per capita income at the time was higher than that of Hong Kong, Korea, Malaysia and Thailand (Wong 1997). Ever since 1962, however, when the military government took power under the motto, “the Burmese way to socialism,” Myanmar has isolated itself from the world by pursuing self-reliant and import-substitution policies. Consequently, it was categorized in 1987 as one of the least developed countries in Asia.

Student-organized pro-democracy movements and articulation of the market-oriented economic system soon followed in 1988 with soaring trade-GDP ratio (See Table 1 in Chapter 2). This trend, which could have been a critical turning point to the country’s political and economic environment, did not last long; movements for domestic political changes were suppressed by government and Myanmar had to face hostile pressure from the international community, such as a series of economic sanctions since 1996.

This study argues that economic sanctions created distortion of Myanmar’s trade. International trade plays a vital role in a country’s economic development, and

1 Corresponding author. Assistant Professor, Graduate School International Studies, Ewha Womans University, Seoul, Korea. E-mail: [email protected] 2 Lecturer, Department of Business Economics, Assumption University, Bangkok, Thailand [email protected] Acknowledgement: This work has been supported by Asia Research Grant funded by POSCO TJ Park Foundation (2-2015-0549-001-1)


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all of the countries that experienced the “take-off” actively pursued export-oriented policies. Without having normal relationship with trading partners, Myanmar’s development would not be easy to be achieved.

The question is, how much distorted Myanmar’s trade is. In order to measure the degree of its trade distortion, this study first examines trade patterns of seven or eight ASEAN countries based on the gravity model. It is seven or eight because Myanmar is included and excluded in each regression, and Brunei and Singapore were excluded because their high income levels are too high to produce unbiased results. A dataset of these ASEAN countries is analyzed by Tobit regressions to effectively deal with zero values, and, using the coefficients produced by those regressions, this study derives theoretically predicted export and import flows of Myanmar based on the gravity model. Then, those flows are compared with Myanmar’s actual ones. To say the conclusion up front, there are a substantially large gap between these two, indicating a large degree of trade distortion. To improve the predictions, the timeframe has been divided into three periods: 1994-1998, 1999-2003, and 2004-2008.

This study is presented in six parts. Chapter 2 expounds on Myanmar’s major economic and social indicators for selected years, trade compositions and types and status of sanctions. Chapter 3 describes model, data, and methodologies used in this study. In Chapter 4, the estimation results are discussed, followed by a simulation test for Myanmar’s predicted trade and actual trade levels, using ASEAN trade patterns estimated by the model for the three time periods. Chapter 5 concludes this paper.

2. Myanmar’s economy, external trade compositions, and economic sanctions

In spite of the introduction of an open market economy and the Foreign Investment Law in 1988, no considerable changes were made in Myanmar in terms of economic structure for more than two decades. Living standard of average people did not change significantly and the export earnings could not contribute to the improvement of the whole economy. A compound of the economic mismanagement and sanctions imposed by western countries has encumbered Myanmar’s population to catch up with other booming economies.

According to the statistics, growth rate of GDP was increasing along the 1990s and the early 2000s. However, these rates have slowed down in recent years. As shown in Table 1, Myanmar’s economy has stagnated for the past three decades in terms of per capita GDP that has increased only two times; $479 in 2008 is only 2.5% of that of South Korea and 12% of that of Thailand. Current account deficits have been dissolved by natural gas revenue since 2002; however, due to the government’s monopoly system on gas exploration exacerbated its economy. Furthermore, growing fiscal deficits were financed by printing money, which resulted in chronic inflation (Kubo 2007). Foreign Direct Investment (FDI) has been fluctuating due to the instability and incredibility of foreign exchange market. For example, numerous regulations on foreign exchange caused the big gap between official exchange rate and an informally developed market based rate. The previous rate had been set up at around six kyat per U.S. dollar for more than two decades, while the latter was 1,290 kyat per dollar (in

Oh, J.;Thant, K.C. Impact Of Myanmar’s Trade Liberalization: A Gravity Approach

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2008).3 Additionally, basic social needs are yet to be satisfied. As shown in the same table, the infant mortality rate is still high, which reduces life expectancy at birth and the Human Development Index (HDI) ranking, which stood at 132 out of 169 countries in 2008.

Table 1. Myanmar’s major economic and social indicators for selected years

1960 1970 1980 1990 2000 2005 2008 GDP growth rate (%) 7.8 5.0 7.9 2.8 13.7 13.6 3.6 PGDP ($) n.a 70 186 68 178 216 479 Exports ($ millions) 224 132 415 409 1980 3707 6629 Imports ($ millions) 223 165 785 668 3039 3577 6952 Trade balance ($ millions) 1 -33 -370 -259 -1059 130 -322 Trade/GDP ratio n.a n.a 0.19 0.39 0.56 0.61 0.48 Current Account Balance ($ millions) -53 -66 -671 -612 -71 444 -697

Inflations (%) -16.9 27.9 -0.1 21.9 -1.7 10.7 22.5 Foreign Direct Investment ($ millions) n.a n.a n.a 280.57 58.15 158.28 205.72 Population (million) 22.2 27.6 33.6 40.8 50.1 55.4 58.8 Life expectancy at birth (years) 44 53 44 51 57 59 60 Adult literacy (%) 60 71 70 78.6 89.8 89.8 91.9 Infant mortality rate (per 1000) 129 59.8 101 120 107 101 98

HDI (Human Development Index) n.a n.a n.a 0.58 n.a 0.406 0.438

Sources: Statistical Year Books 2004 and 2008, WEO April 2010, DOTS, Myat Thein, Economic Development of Myanmar (2001), UNDP, WDI, and Statistics on the Burmese economy: the 19th and 20th centuries.

As shown in Table 2, since 1940, Myanmar’s export structure remained the same until the early 1980s. In particular, the share of rice and rice-based products dominated its export. Since the late 1990s, however, there was a slight change in the export structure such as the surge of garment exports and natural gas exports. , The garment industry, however, have started to decline since the 2000s due to the 2003 sanctions imposed by the United States.

3 Regional Outlook, Southeast Asia, 2010-2011.


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Table 2. Structure of Exports by Commodity: 1941-2008 (%)

Commodity 1941

1961

1971

1981

1991

2001

2005

2006

2007

2008

Agricultural products 53.9 82.4 62.5 54.6 28.2 16.1 9.7 10.6 11.7 11.4

Animal and marine products 0.5 0.3 0.1 3.0 4.1 6.7 5.6 4.9 4.0 4.1

Forest products 11.3 9.8 23.7 24.7 36.7 5.6 11.9 11.5 8.6 7.2 Minerals and gems 11.2 4.3 8.9 14.5 1.3 4.8 6.1 8.4 8.4 9.8

Gas n.a n.a n.a n.a n.a 7.7 30.7 26.2 34.1 34.0 Garment n.a n.a n.a n.a 8 26.3 6.5 6.7 4.7 3.8 Others 23.1 3.3 1.4 3.2 29.7 21.3 17.8 18.3 16.3 15.7 Total 100 100 100 100 100 100 100 100 100 100 Sources: Statistical Year Books 2004 and 2008, Mya Than, Myanmar’s External Trade (1992)

Myanmar’s import structure has gone through major shifts since 1940 with the industrialization policies of the governments. Up until 1962, the largest share of total imports was consumer goods. Since the Revolutionary Council regime took office in 1962, import substitution policies were widely implemented and preferences were put on importing raw materials and intermediate goods. Since the free-market system was launched in 1988, the demand for imported consumer goods was increased substantially. Table 3. Structure of Imports by Commodity: 1941-2008 (%)

Commodity 1941

1961

1971

1981

1991

2001

2005

2006

2007

2008

Capital goods 10.6 16.0 40.8 53.9 31.6 26.9 29.9 27.6 23.9 29.6 Raw materials, spares for inter-industry use

19.5 16.7 44.1 40.2 30.2 30.4 29.3 32.1 40.0 28.7

Consumer goods 69.9 67.3 15.1 5.4 38.2 42.7 40.8 40.3 36.2 41.7 Total 100 100 100 100 100 100 100 100 100 100 Sources: Statistical Year Books 2004 and 2008, Mya Than, Myanmar’s External Trade (1992)

3. Model, Data, and Methodology Using the gravity model, this study analyzes a substantially comprehensive panel dataset on the bilateral trade flows covering eight ASEAN (or seven if Myanmar is excluded) countries and their 85 trading partners between 1994 and 2008. Using Berthelemy and Tichit’s (2004) terms, this is a “three dimensional panel dataset”. Keep in mind, however, that analyzing this dataset and providing regression results are not the ultimate goal of this study; these are stepping stones toward the next stage where degree of Myanmar’s trade distortion is measured. This study basically follows the approach suggested by Montenegro and Soto (1996). As a first stage, they conducted regression analyses for 65 developing countries with their 101 trading partners, and, in their second stage, the focus was on Cuba’s 13 neighbors in the Caribbean area. This study is similar to their second stage, and it uses a dataset of eight ASEAN countries, including Myanmar, (or seven excluding Myanmar) with their 85


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trading partners. Based on this result, a simulation is made in the next stage and theoretically predicted trade flows of Myanmar are compared with its actual ones.

First, regressions will be conducted using the entire dataset for all countries and all years (Part 1). Next, the dataset will be broken down into each ASEAN country and the regression results for each country will be provided (Part 2). Thirdly, the dataset is divided into three time periods (1994-1998, 1999-2003, and 2004-2008: five years each) and regressions for each period will be conducted to check if there are any structural breaks over time (Part 3). Part 3 (excluding Myanmar) is particularly important, as this result is applied to the next simulation stage when Myanmar’s predicted trade flows are compared with its actual ones. As is common in the literature, exports and imports will be estimated separately. Furthermore, in order to put emphasis on Myanmar, this study compares regressions when Myanmar is included with these when Myanmar is discarded. In total, 32 regression analyses will be conducted in this study [2 (export and import) * 2 (with and without Myanmar) * (Part 1 + 3 periods in Part 2) + 2 (export and import) * 8 ASEAN countries = 32]. Tobit will be the main tool for this research. In fact, it is very common to find several zero values in bilateral trade data. In this case, OLS is inappropriate due to the left-censoring problems and inconsistent parameters. Some researchers have attempted alternative procedures, such as simply eliminating zeros in the dependent variable (e.g. Brada and Mendez, 1985) or replacing them with arbitrary small values (Anderson and Wincoop,2003, Butt, 2008), which also tends to bias the results. Using fixed effects may drop some important variables, and random effects have the same problems as OLS. For this reason, Montenegro and Soto (1996) used Tobit in their cross-sectional data. Tobit’s model is frequently used in the three dimensional panel data as well (see Allesina and Dollar 2000 and Berthelemy and Tichit 2004). Regarding regression equations, the present study uses the following, which is typical in gravity literature.

where i denotes ASEAN countries, j denotes their partner countries, t denotes time, and the variables are defined as:

GDPiGDPJ is the product of ASEAN countries’ and their partners’ GDPs, PCGDPiPCGDPj is the product of ASEAN countries’ and their partners’ per

capita GDPs, Distanceij is distance between ASEAN countries and their trading partners, Linderijt is the Linder variable, which is the absolute difference of per capita

GDP between ASEAN countries and their trading partners, ASEANj is a dummy variable, which is 1 if a partner country is a member of

ASEAN, and 0 otherwise. LANDLOCKEDj is a dummy variable, which is 1 if a partner country is

landlocked, and 0 otherwise. Borderj is a dummy variable, which is 1 if a partner country shares a border

with ASEAN members, and 0 otherwise.


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The product of GDPs measures ASEAN countries and their partners’ economic sizes. Strictly speaking, when the original gravity equation is transformed into a natural logarithm, we have summation of GDPs instead of their product. However, if summation is used, coefficient for ASEAN countries’ GDP would be insignificant due to insufficient observations. In fact, the product of GDP (or per capita GDP) was used in several gravity papers, including Rose (2004) and Sohn (2005). We expect to be positive, as the export or import tends to increase proportionately with the increase of economic size. In order to avoid endogeneity problem stemming from reverse causality, we use one year lagged GDP, which is common in gravity literature.The per capita GDP measures income levels. This is a complement to GDP, which focuses more on economic size rather than income level. The expected sign for per capita GDP is ambiguous; Bergstrand (1989) argued that the exporter’s per capita GDP is expected to have a positive (negative) effect if the trade composition is capital (labor) intensive in production. An importer’s per capita GDP is supposed to be positive (negative) if the trade composition is on luxury (necessity) goods in consumption.

Distanceij is the distance between an ASEAN member and its partner countries, which will work as a proxy of transport cost. A greater circle distance is used in this study. A negative sign is expected for this coefficient, for a country tends to trade less with a far away country due to higher transportation costs.

Linderij is the absolute difference of per capita GDPs in U.S. dollars between an ASEAN country and its partners. This variable, originally used by Montenegro and Soto (1996), will reveal information on the structure of trade between two countries. If countries trade more when their economies differ, as was predicted by traditional trade theory based on comparative advantage, the expected sign is positive (Linder 1961). However, if countries trade more when their economies are similar, as was predicted by new trade theory based on increasing returns and product differentiation, a negative sign is expected. ASEAN, Landlocked, and Border are dummy variables taking value 1 if the partner country belongs to them and 0 otherwise.

Regarding data sources, export and import data come from the Direction of Trade Statistics (DOTS) of IMF. GDP and per capita GDP have been obtained from the World Economic Outlook Database 2010 of IMF. The greater-circle distances are obtained from www.timeanddate.com. Since most trades are made through sea ports, distances to major sea ports are sometimes considered if capital cities are located far away from the port cities. In this case, average between distance to the capital and distance to the port cities are considered4 4. Results 4.1 Regression Results. In Part 1, regression results for the entire dataset are provided. As previously mentioned, export and import values are analyzed separately, and Myanmar is excluded first and then included. The results are provided in Table 5. No matter which dependent variable (export or import) is used, and regardless of Myanmar’s inclusion

4 For example, in Vietnam, both Ho Chi Minh City and Hanoi are used because distance between these cities is over 600 miles. Therefore, Ho Chi Minh City is used as a starting point for the southern regions of Vietnam and Hanoi for its northern regions.


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in the analyses, the results are all similar to each other: positive coefficient for GDP, per capita GDP and all dummies, and negative coefficient for distance and Linder.

Table 5. Part 1: Regression Results for the Entire Period (1994-2008) Without Myanmar With Myanmar Variables Export Import export import Ln (product of GDPs) 1.984*** 2.175*** 1.940*** 2.220*** (0.033) (0.035) (0.032) (0.035) Ln (product of per capita GDPs) 0.783*** 0.929*** 0.834*** 0.995*** (0.060) (0.064) (0.056) (0.060) Ln (Distance) -1.302*** -2.402*** -1.616*** -2.607*** (0.119) (0.126) (0.117) (0.126) Ln (Linder) -0.382*** -0.348*** -0.316*** -0.357*** (0.053) (0.056) (0.050) (0.054) ASEAN 2.453*** 2.069*** 2.214*** 2.186*** (0.294) (0.311) (0.281) (0.300) Landlocked 0.836*** 0.561*** 0.476* 0.551* (0.303) (0.322) (0.293) (0.315) Border 1.858*** 1.215*** 1.370*** 0.763* (0.408) (0.430) (0.381) (0.406) Constant -55.536*** -56.756*** -52.695*** -57.756*** (1.370) (1.463) (1.333) (1.447) Observations 8743 8739 9973 9899

Table 6.1. Part 2: Regression Results for Each ASEAN Countries: Exports Variables Cam

bodia Indo nesia

Laos Mala ysia

Myan mar

Philip pines

Thail and

Viet nam

X1 2.513 ***

0.71 ***

2.603 ***

0.747 ***

1.448 ***

1.010 ***

0.685 ***

1.277 ***

(0.169) (0.055) (0.210) (0.046) (0.134) (0.777) (0.052) (0.117) X2 3.401

*** -0.289

*** 2.390 ***

0.061 -0.575 0.254* -0.135* -0.231

(0.473) (0.097) (0.453) (0.064) (0.414) (0.142) (0.080) (0.249) X3 -3.292

*** -1.474

*** -1.088

* -1.32 ***

-4.613 ***

-1.195 ***

-0.508 ***

-1.741 ***

(0.486) (0.190) (0.604) (0.147) (0.443) (0.223) (0.154) (0.348) X4 -1.104

*** 0.221 ***

-0.004 0.050 2.157 ***

0.483 ***

0.257 ***

0.409 **

(0.383) (0.084) (0.359) (0.071) (0.406) (0.121) (0.073) (0.195) X5 3.140

** -0.474 2.893

** -0.004 -0.144 2.387

*** 2.092 ***

1.278

(1.248) (0.420) (1.350) (0.416) (0.903) (0.524) (0.380) (0.900) X6 2.110

* -1.235

*** 1.222 -0.81 -2.631

*** -1.319

** 0.625 3.014

*** (1.182) (0.403) (1.529) (0.344) (1.003) (0.568) (0.388) (0.922) X7 3.899

** 0.501 10.152

*** 0.472 -0.578 -1.419

*** 1.789

(1.724) (0.585) (1.717) (0.492) (1.191) (0.503) (1.304) X0=Constant -93.278

*** 6.523 ***

-110.091 ***

0.491 -12.623 **

-19.536 ***

-2.647 -17.526 ***

(7.504) (2.441) (8.743) (1.871) (5.532) (3.293) (2.077) (5.097) T 1232 1256 1238 1255 1230 1254 1254 1254

Notes: See table 6.2.


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In the second part of the regressions, regression analyses for each ASEAN country were conducted to compare each country’s pattern (see Tables 6.1 and 6.2 in the Annex). This approach is similar to that of Allesina and Dollar (2000) and Berthelemy and Tichit (2004) when they examined aid disbursement patterns of multiple donor countries; after conducting analyses for the multiple donors as a whole, they split the dataset based on each donor to see whether there were any country-specific characteristics. Generally speaking, the positive signs of GDP and negative coefficient of distance, a basic prediction of the gravity model, are common across countries. Regarding per capita GDP, Indonesia, Thailand, and Myanmar show negative signs with statistical significance; this finding implies that they are mostly trading with countries of similar economic size. In case of Linder, coefficients of most countries are positive, showing that their trades are based on Hecksher-Ohlin type comparative advantage. Distance variables are estimated with relatively large coefficients for Myanmar, Cambodia and Laos reflecting that their trading costs are higher than other ASEAN countries’ costs (trade concentrates on nearer partners).

Table 6.2. Part 2: Regression Results for Each ASEAN Countries: Imports Variables Cam

bodia Indo nesia

Laos Mala ysia

Myan mar

Philip pines

Thail and

Viet nam

X1 2.997 ***

0.822 ***

3.620*** 0.920 ***

2.921 ***

1.353 ***

0.760 ***

1.600 ***

(0.187) (0.067) (0.245) (0.056) (0.178) (0.999) (0.056) (0.131) X2 1.498

*** -0.065 2.377

*** 0.376 -1.646

*** 0.309

* 0.072 -0.168

(0.526) (0.119) (0.516) (0.079) (0.523) (0.182) (0.085) (0.275) X3 -6.310

*** -1.743

*** -6.343

*** -1.333

*** -5.798

*** -1.332

*** -1.004

*** -3.491

*** (0.536) (0.234) (0.692) (0.182) (0.576) (0.288) (0.163) (0.384) X4 0.521 0.204** -0.153 0.049 2.657

*** 0.532 ***

0.324 ***

0.681 **

(0.443) (0.104) (0.415) (0.088) (0.516) (0.155) (0.078) (0.216) X5 2.044 -0.602 3.789

*** 0.989

* 1.591 1.895 1.562

*** 0.156

(1.330) (0.516) (1.423) (0.514) (1.105) (0.675) (0.403) (0.988) X6 2.532

** -0.832* 2.647 -0.681

* 0.930 -3.118

*** 0.181 4.407

*** (1.272) (0.496) (1.752) (0.425) (1.203) (0.736) (0.411) (1.009) X7 -0.910 1.338* 3.693

** -0.427 -3.024

** -1.180

** 0.656

(1.843) (0.718) (1.815) (0.607) (1.457) (0.533) (1.430) X0=Constant -72.041

*** -1.654 -102.614

*** -12.053

*** -46.433

*** -32.385

*** -5.863

*** -24.166

*** (8.161) (3.000) (9.883) (2.312) (7.052) (4.245) (2.202) (5.648) T 1232 1256 1237 1255 1160 1254 1252 1253 Notes: *, **, *** indicate significance at the 10%, 5%, and 1% level respectively. Numbers in ( ) are standard errors. X1= Ln (product of GDPs). X2=Ln (product of per capita GDPs). X3=Ln (Distance). X4=Ln (Linder). X5=ASEAN, X6=Landlocked. X7=Border. X0=Constant. T = number of observations.

Lastly, Part 3 provides results for a dataset that is divided into three parts based on five year periods (1994-98, 1999-2003, and 2004-08) (see Tables 7.1 and 7.2 in the Annex). Basically, the results in this part are similar to the previous parts, and each


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period does not show distinctive characteristics. Even though the result provided in Table 7 may not be very interesting, it is of great importance in simulation part, which will be introduced in the next section. 5.2. Simulation Results

As mentioned several times in this paper, Myanmar’s trade has been distorted due to external political factors. In order to measure the degree of distortion, this section uses the regression results from the previous section. In particular, the simulation depends on results provided in Part 3 where three time periods are considered in order to better estimate predicted values by reducing time horizons to five years instead of the entire 15 years. Also, this study uses regression results based on the dataset of seven ASEAN countries excluding Myanmar. The reason for excluding Myanmar is simple. Suppose I am interested in whether my kid did all right in an exam. The best way to check it is to get exam scores of other kids who have similar characteristics to mine and compare with mine theirs, which are neighboring ASEAN countries whose distances to their trading partners are similar to Myanmar’s. Also, ASEAN countries’ income levels (except Brunei and Singapore, which are excluded from this study) are not too different from Myanmar5. With all these similar characteristics on hand, the only difference is that, unlike its neighbors, Myanmar is facing economic sanctions and its trade has been distorted.

How to get the predicted values is as follows. We first calculate five-year averages of the actual values of explanatory variables of Myanmar’s trading partners in log form. Then, they are plugged into equations derived from the estimated results in Part 3 in which Myanmar is excluded. For example, the predicted export flow of Myanmar in the first period (1994-1998) is:

Ln (export) = -52.58+1.801 Ln (product of GDPs) + 1.396 Ln (product of per capita GDPs) – 2.033 Ln (distance) + 1.054 ASEAN +0.051 Landlocked + 1.953 Border (2)

where coefficients are from Table 7 (first column). When five-year averages of explanatory variables are plugged into this equation, the predicted export flow is derived in natural log form. Finally, by transforming this flow into exponential values, Myanmar’s gravity-based predicted export flow is derived. The same logic is applied to import flows.

The results are provided in Table 8 (in the Annex). As expected, predicted flows of Myanmar’s export and import are quite different from its actual flows. The most striking result is Myanmar’s trade volumes with the United States. The portion of Myanmar’s predicted export with the U.S. is 29.57%, while the actual portion is only 6.33%. Likewise, the portion of Myanmar’s import with the U.S. is 16.86%, whereas the actual portion is only 0.49%. When we divide the time periods into three, it is revealed that the degree of distortion has increased; between 2004-2008 period, actual

5 Of course, whether Malaysia is a similar income group to Myanmar may be controversial. However, this problem is negligible when it comes to simulation part.


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export and import with the U.S. are even reduced to 0% and 0.21%, which are far behind the predicted values.

Trade with Japan is also distorted, but the degree has declined over the years; the gaps between predicted and actual flows are almost 50% in the first period but reduced to less than 10% in the third period. In fact, Myanmar’s relationship with Japan is all right these days with increasing trade flows, as well as increasing ODA disbursements.

Regarding China, predicted flows in the first and the second period are similar to the actual ones, but quite different in the third flows, particularly in the export part (predicted portion is 46.11% whereas the actual one is only 7.64%), even though Myanmar is not imposed economic sanctions by China. This large gap is resulted from China’s recent rapid economic development; it seems that Myanmar-China trade, particularly Myanmar’s export to China, has not caught up with China’s booming economy yet. Additionally, the predicted value and the actual value in EU’s case do not differ significantly; this finding confirms the fact that their sanctions against Myanmar are targeted only toward the military government such as visa bans on senior military officials and bans on purchase of military equipment, minimizing the effect on trade.

On the other hand, actual export and import flows with India and Thailand are higher than the predicted one, meaning that Myanmar’s economic contact with these countries has abnormally increased due to embargoes from other countries. In particular, Myanmar’s export to Thailand has skyrocketed recently; Myanmar’s predicted export to Thailand is 8.03%, but its actual export portion is as large as 50.63%.

The distortion is clearly understood when this simulation result is compared with that of Bangladesh whose income level is similar to that of Myanmar ($522 in Bangladesh vs. $479 in Myanmar in year 20086) and their industrial structures are mostly based on labor intensive sectors. Moreover, Bangladesh is located next to Myanmar, and therefore, their distance data to their trading partners are similar to each other. Given all these, the gravity-based simulation should provide similar results.

However, the result for Bangladesh is quite different from that of Myanmar. According to Oh and Sardar (2011)’s similar simulation study7 using Bangladesh’s trade data between 1980 and 2009 (Table 9 in the Annex), actual export flows from Bangladesh to the U.S. is about 25% more than the predicted ones, which is

6 WEO April 2010. 7 This simulation is based on their so-called basic gravity model where GDP, per capita GDP, distance are considered as independent variables. Like Myanmar’s case, this study is based on Tobit regression. However, trading partner groups are a bit different from Myanmar’s. Bangladesh’s trade is more diversified than Myanmar’s and portion of ‘the rest of the world’ is far larger than Myanmar’s. Another difference is that, unlike Myanmar’s case where the regression is based on neighboring seven ASEAN countries’ dataset, Bangladesh’s study is based on the country’s dataset only instead of considering its neighbors. See Oh and Sardar (2011) for more explanation on methodologies and results.


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substantially different from Myanmar’s result. Unlike Bangladesh that depends a large portion of its export on the U.S. market, Myanmar’s export to the U.S. has reduced to zero. From this result, we can predict that, upon the sanctions are lifted, Myanmar’s export channels will be largely transferred to the U.S. to take advantage of the market of World’s largest GDP and its contact with Thailand will be reduced. 5. Conclusion

After the collapse of the socialist system in 1988, the new military government introduced the market oriented economic system and enacted the Foreign Investment Law. However, the vulnerable political conditions between the opposition group and the military government triggered additional sanctions from the United States and other western countries. In addition, hostile international pressures have created obstacles for Myanmar in attracting American and European investors. Myanmar has had to rely on only a few neighbors, which has resulted in trade pattern distortions.

The standard gravity model, with panel data from the 1994 to 2008 period, and econometric tools are employed to explore the trade structure of ASEAN countries with an emphasis on Myanmar. Upon the lifting of the trade and investment sanctions, a large portion of trade could be channeled to other countries, particularly to the U.S.. Bangladesh’s is a good example. Then current biased export and import flows to only a few countries, such as Thailand, will be more diversified. More active economic contact with China and overcoming current low trade ratio for China’s booming economy is another thing that Myanmar government needs keep in mind.

The experiences of Asian countries for achieving rapid economic development can be applied as role models so that Myanmar can escape from deep stagnation. After a military coup in 1961 by Park Chung Hee, South Korea was able to attain tremendous economic performance. Likewise, although Lee Kuan Yew, the elected Prime Minister from Singapore, has been regarded as an authoritarian, Singapore is well known for its successful economic growth. It is worth noting that national economic performance is not solely dependent on political structure. No matter how South Korea and Singapore developed under generous American economic aid in the early phases of industrialization, the mainspring for the successes of these authoritarian regimes is the creation of a general consensus of economic development among their own people and interest groups. Although more easily said than done, Myanmar must have “political commitment to development” along with a quality of leadership that is able to create the essential social cohesion and political stability for achieving sustained economic growth. References Alesina, A., & Dollar, D. (2000). Who gives foreign aid to whom and why? Journal of Economic Growth, 5, 33–63. Bergstrand, J. H. (1985). The gravity equation in international trade: Some microeconomic foundations and empirical evidence. The Review of Economics and Statistics, 20, 474-81.


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Bergstrand, J. H. (1989). The generalized gravity equation, monopolistic competition, and the factor proportion theory in international trade. Review of Economics and Statistics, 71(1), 143-53. Berthelemy, J., and Tichit, A. (2004). Bilateral donors’ aid allocation decisions----a three-dimensional panel analysis. International Review of Economics and Finance, 13(2004) 253-274. Howse, R.L., and Genser, J.M. (2008). Are EU Trade Sanctions on Burma Compatible with WTO Law? Michigan Journal of International Law, Vol.29:165. Kubo, K. (2007). Determinants of Parallel Exchange Rate in Myanmar. ASEAN Economic Bulletin, Vol. 24, No. 3. Linder, S. (1961). An Essay on Trade and Transformation. New York: John Wiley. Lwin, N.N. (2009). Analysis on International Trade of CLM Countries. IDE Discussion Paper No. 215. Montenegro, C.E., and Soto, R. (1996). How distorted is Cuba’s trade? Evidence and predictions from the gravity model. The Journal of International Trade & Economic Development. Vol. 5, No. 1. Oh, Jinhwan and Sardar, Rashedur Rahman (2011). Gravity Matters: International Trade of Bangladesh. International University of Japan Working Paper. Than, M. (1992). Myanmar’s External Trade: an Overview in the Southeast Asian Context. Institute of Southeast Asian Studies. Wong, J. (1997). Why has Myanmar not developed like East Asia? ASEAN Economic Bulletin, Vol. 13. No.3.

Appendix A: Sample countries used in this paper ASEAN countries used in this study Cambodia, Indonesia, Laos, Malaysia, Philippines, Thailand, and Vietnam, (Myanmar) ASEAN countries’ 85 trading partners Algeria, Angola, Argentina, Australia, Austria, Bahrain, Bangladesh, Belgium, Benin, Brazil, Brunei, Canada, Chile, China, Colombia, Costa Rica, Côte d'Ivoire, Denmark, Dominican, Ecuador, Egypt, Finland, France, Germany, Ghana, Greece, Hong Kong, Hungary, India, Iran, Ireland, Israel, Italy, Japan, Jordan, Kenya, Korea, Kuwait, Lebanon, Libya, Lithuania, Mexico, Morocco, Netherlands, New Zealand, Nigeria, Norway, Oman, Pakistan, Panama, Papua, Peru, Poland, Portugal, Qatar, Romania, Russia, Saudi Arabia, Senegal, Singapore, Slovenia, Spain, Sri Lanka, Sudan, Sweden, Switzerland, Syrian Arab, Tanzania, Togo, Tunisia, Turkey, UAE, UK, Ukraine, USA, Venezuela, and Yemen.


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Appendix B: Major types and status of sanctions against Myanmar No. Dates Types and Status 1 August 31,

1988 West German and Japan suspend disbarment of aid by reason of Myanmar government basic human rights violations.

2 September 23, 1988

All arms sales are suspended by the US along with bans on foreign assistance except humanitarian aid to Myanmar.


Development aid is suspended by the European Community.

4 July 1994 Levi-Strauss (US company) pulls out from Myanmar. 5 February

28, 1995 Companies that deal with the regime in Myanmar are banned city contracts by the town of Berkeley, and CA.

6 June 25, 1996

Commonwealth of Massachusetts prohibits companies that do business in Myanmar from purchasing or leasing state-owned property.

7 July 7, 1996

Plan of investment by Carlsberg of Denmark is abandoned, and Heineken (Dutch beer multinational) withdrawals from Myanmar under fire from its unions.


The FY 1997 Foreign Operations Appropriations bill is signed by President Clinton, a provision in which US assistance to Myanmar, except for relief aid and anti-drug purposes, is barred.

9 October 3, 1996

Myanmar government leaders are barred from entry into the US.

10 October 25, 1996

EU imposes a ban on officials of Myanmar’s military junta visas and high-level bilateral contacts is placed a moratorium.

11 November 25, 1996

234 firms involved in Myanmar are affected by the selective purchasing law by the State of Massachusetts. The Massachusetts initiative has been followed by eight cities by adopting similar restrictions.

12 January 28, 1997

Withdrawal from Myanmar by Kodak, Apple and Walt Disney, and PepsiCo are announced.

13 February 6, 1997

Motorola also withdrawals from Myanmar.

14 May 20, 1997

Bars on new investments are issued by President Clinton, while existing contracts are allowed but not to be modified or expanded.

15 June 20, 1997

Trade promotion activities in Myanmar are suspended by the United Kingdom.

16 October 5, 1997

Another six months bans on non-humanitarian aid, visas for military leaders, and the sale of military equipment are extended by EU.

17 October 1998

EU expends sanctions of travel for Myanmar military officers including transit and tourist visas.

18 April 10, 2000

Visa ban on Myanmar officials is extended by EU.

19 April 14, 2003

Existing sanctions for another year is extended by EU.

20 June 6, 2003

Visa ban to members of the Union Solidarity Development Association, an organization associated with the military junta, are expended by Bush administration.

21 June 24, 2003

Japan announces to suspend all economic assistance.

22 July 28, The Burmese Freedom and Democracy Act are signed by President Bush.


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2003 The US imports from Myanmar are banned, Myanmar government and senior officials’ assets are frozen, US firms are prohibited from providing financial services to any Myanmar entity, current visa ban are expanded, and the existing policy of opposition to international loans and technical assistance to Myanmar are codified.

23 November 7, 2003

The tobacco firm of UK multinational, BAT, pulls out.

24 July 7, 2004

The import ban on for another year is extended by the US Congress.

25 April 28, 2006

Restrictive measures against Myanmar are extended by the EU.

26 August 1, 2006

The 2003 Burmese Freedom and Democracy Act are extended three more years.

Source: Peterson Institute for International Economic

Table 7. 1. Part 3: Regression Results for Three Periods, without Myanmar

Period 1 (1994-1998) Period 2 (1999-2003) Period 3 (2004-2008) Variables Export import export import Export Import Ln (product of GDPs) 1.801*** 1.917*** 2.221*** 2.423*** 2.165*** 2.389***

(0.063) (0.066) (0.050) (0.054) (0.054) (0.059) Ln (product of pc GDPs)* 1.396*** 1.667*** 0.448*** 0.618*** 0.433*** 0.641***

(0.121) (0.124) (0.092) (0.099) (0.102) (0.111) Ln (Distance) -2.033*** -2.948*** -0.919*** -2.027*** -1.091*** -2.411*** (0.237) (0.245) (0.178) (0.191) (0.190) (0.206) Ln (Linder) -0.462*** -0.470*** -0.436*** -0.506*** -0.230*** -0.118 (0.110) (0.114) (0.075) (0.081) (0.085) (0.092) ASEAN 1.054* 0.844 3.124*** 2.513*** 2.609*** 2.364*** (0.607) (0.623) (0.438) (0.468) (0.468) (0.504) Llocked 0.051 -0.497 1.134*** 0.889* 1.299*** 1.124** (0.631) (0.661) (0.440) (0.470) (0.469) (0.506) Border 1.953** 1.530* 1.538*** 0.786 2.125*** 1.410** (0.851) (0.874) (0.592) (0.631) (0.630) (0.679) Constant -52.58*** -53.86*** -60.16*** -61.07*** -60.01*** -62.77*** (2.693) (2.795) (2.001) (2.228) (2.229) (2.43) Observations 2865 2864 2938 2935 2940 2940 Note: See table 7.2. * pc GDP = per capita GDP


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Table 7. 2. Part 3: Regression Results for Three Periods, with Myanmar Period 1 (1994-1998) Period 2 (1999-2003) Period 3 (2004-2008) Variables export Import export import export Import Ln (product of GDPs) 1.721*** 1.890*** 2.208*** 2.511*** 2.135*** 2.470*** (0.061) (0.066) (0.050) (0.055) (0.054) (0.060) Ln (product of pc GDPs) 1.305*** 1.604*** 0.542*** 0.738*** 0.597*** 0.809*** (0.108) (0.115) (0.087) (0.093) (0.095) (0.104) Ln (Distance) -2.137*** -3.022*** -1.256*** -2.340*** -1.573*** -2.613*** (0.231) (0.243) (0.177) (0.190) (0.188) (0.205) Ln (Linder) -0.250** -0.305*** -0.424*** -0.561*** -0.251*** -0.248*** (0.102) (0.108) (0.072) (0.078) (0.081) (0.089) ASEAN 1.147** 1.092* 2.819*** 2.370*** 2.249*** 2.766*** (0.577) (0.602) (0.421) (0.450) (0.447) (0.485) Llocked -0.432 -0.542 0.946** 0.920** 0.912** 1.147** (0.608) (0.643) (0.431) (0.461) (0.458) (0.496) Border 1.677** 1.092 1.068* 0.425 1.400** 0.854 (0.791) (0.825) (0.558) (0.595) (0.592) (0.641) Constant -49.02*** -52.57*** -58.41*** -63.06*** -57.42*** -65.88*** (2.590) (2.745) (2.032) (2.207) (2.191) (2.418) Observations 3265 3235 3353 3329 3355 3335

Table 8. Actual vs. Predicted Trade Share of Myanmar by Regions and Countries (%) Predicted Actual Predicted Actual Trade Partners Exports (1994-2008) Imports (1994-2008)

EU 7.43 10.98 4.57 4.39 CHINA 28.18 7.59 26.91 28.58 INDIA 2.56 13.75 1.90 2.76 JAPAN 16.45 5.81 24.85 5.42 THAILAND 12.49 37.37 22.17 14.68 USA 29.57 6.33 16.86 0.49 Rest of the World 3.33 18.17 2.74 43.67 Total 100 100 100 100

Predicted Actual Predicted Actual Trade Partner Exports (1994-1998) Imports (1994-1998) EU 7.55 10.98 3.94 7.42 CHINA 2.60 11.04 1.17 25.13 INDIA 0.50 15.65 0.20 1.65 JAPAN 58.23 9.02 67.74 8.44 THAILAND 13.61 1.40 18.19 0.00 USA 15.13 11.21 6.69 1.07 Rest of the World 2.39 40.69 2.08 56.28 Total 100 100 100 100 Exports (1999-2003) Imports (1999-2003) EU 5.48 16.56 3.72 3.65 CHINA 14.66 5.95 16.45 24.76 INDIA 1.97 11.47 1.96 2.38 JAPAN 19.38 5.11 34.28 6.59 THAILAND 5.47 26.77 8.88 16.47 USA 50.92 17.02 33.12 0.47 Rest of the World 2.13 17.11 1.60 45.67 Total 100 100 100 100 Exports (2004-2008) Imports (2004-2008) EU 5.16 8.18 3.40 3.26 CHINA 46.11 7.64 43.31 32.59

INDIA 3.47 14.46 2.05 3.55 JAPAN 7.87 5.44 13.96 3.21 THAILAND 8.03 50.63 19.24 21.00 USA 26.99 0.00 16.02 0.21 Rest of the World 2.37 13.66 2.00 36.17 Total 100 100 100 100


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Source: Authors’ calculation Table 9. Actual vs. Predicted Trade Share of Bangladesh by Regions and Countries (%)

Predicted Actual Predicted Actual Trade Partners Exports (1980-2009) Imports (1980-2009) EU 30.15 49.82 1.94 12.34 ASEAN 10.47 2.10 14.20 15.34 MENA 11.32 3.08 3.72 10.15 CHINA 4.56 0.79 1.76 12.18 JAPAN 8.05 1.78 3.80 7.90 INDIA 3.54 1.57 1.95 13.62 USA 3.92 28.59 0.05 4.24 REST OF THE WORLD 27.98 12.26 72.59 24.21 Trade Partners Predicted Actual Predicted Actual

Exports (1980-1989) Imports (1980-1989) EU 17.80 25.94 6.02 19.94 ASEAN 13.46 6.44 15.50 12.73 MENA 8.44 11.18 18.75 15.30 CHINA 11.31 2.38 0.44 4.23 JAPAN 4.44 5.89 8.61 14.40 INDIA 11.79 1.61 1.95 2.95 USA 3.68 20.05 0.23 10.49 REST OF THE WORLD 29.08 26.51 48.49 19.96 Exports (1990-1999) Imports (1990-1999) EU 34.62 44.21 3.10 15.32 ASEAN 8.95 2.43 25.49 12.92 MENA 11.61 3.62 1.60 4.80 CHINA 1.89 0.76 2.85 8.68 JAPAN 7.66 2.60 20.88 10.74 INDIA 1.65 1.02 4.38 14.34 USA 3.76 33.77 0.13 5.79 REST OF THE WORLD 29.85 11.60 41.57 27.41 Exports (2000-2009) Imports (2000-2009) EU 30.42 54.70 0.26 10.17 ASEAN 10.94 1.47 4.61 17.11 MENA 12.81 1.92 0.87 11.43 CHINA 4.42 0.61 0.61 15.24 JAPAN 8.53 1.00 0.24 5.89 INDIA 2.52 1.77 0.63 15.67 USA 2.99 17.88 0.01 2.65 REST OF THE WORLD 27.36 10.64 92.77 21.83 Source: Oh and Sardar (2011), MENA= Middle East and North Africa Journal published by the EAAEDS: http://www.usc.es/economet/eaat.htm

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