Chapter VI: FDI, Industrial Upgrading and Economic Corridor in Myanmar
Ni Lar, Chiang Mai University, Thailand
Hiroyuki Taguchi, Saitama University, and Visiting Researcher, ESRI
This chapter addresses the issues on inward foreign direct investment (FDI), industrial upgrading, and economic-corridor development in Myanmar. We first present the economic profile of Myanmar by comparing with other economies in Mekong region as well as its brief history. Second, we discuss the role of inward FDI in
Myanmar. From a short-term perspective, Myanmar needs to accept inward FDI to participate in international production networks and thus to develop a manufacturing sector. This section represents empirical evidence on the linkage between FDI and the growth of GDP and exports, and investigates a specific issue to be addressed for accepting inward FDI in Myanmar manufacturing sector. Third, from a long-term perspective, we discuss the issues on industrial upgrading and geographical linkage in
Myanmar economy. Myanmar now depends heavily on natural resource production in its economy, and also on labor-intensive production in its manufacturing sector. Thus, the industrial reformation should address how to diversify its industries towards a variety of manufacturing sectors and how to upgrade its industries towards upstream and high-valued manufacturing sectors. From the geographical viewpoint, Myanmar also now depends on spot-area development through its SEZ framework. For extending the economic impacts of the SEZ development to nation-wide level, the SEZ development should contribute to an economic corridor approach linked with
102 neighboring countries.
1. Economic Profile of Myanmar
This section first presents the economic profile of Myanmar by comparing with other economies in Mekong region, and then reviews its brief history and the recent economic reforms.1
Economic Profile of Myanmar
Myanmar has just opened up its economy and started its economic reforms under
President U Thein Sein since March 2011. Myanmar, however, still stays at the lowest level in economic performances among ASEAN and Mekong-region economies, and also still depends on agricultural-based and resource-dependent industries.
The basic economic profile of Myanmar in comparison with other Mekong-region economies is shown in Table 1. M yanmar has a larger scale of population of 64.9 million persons, which is roughly the same size as that of Thailand, though the scale of
Myanmar GDP is far smaller than that of Thailand. Then, the GDP per capita of
Myanmar and the Human Development Index are still the lowest among Mekong-region economies. Looking at industrial structure in terms of GDP share in 2012 (Figure 1), the agricultural sector occupies around one-third in Myanmar, while it does one-tenth in
Thailand. Regarding trade structure in Myanmar (Figure 2), the exports depend highly
1 The descriptions of the brief history and the recent economic reforms in Myanmar are mainly based on ERIA (2013) and OECD (2013).
103 on primary-sector products, i.e., on natural gas by 29% and on agricultural products by
24%, while the imports depend much on c apital goods by 44%. Thus, Myanmar industries can be said to be still now agricultural-based and resource-dependent ones.
Brief History of Myanmar
It is the fact that Myanmar’s economy had been slightly larger than that of Thailand before World War II, and expected to achieve rapid industrialization supported by its rich natural resources and highly literate population. However, it seems to be the control-oriented and inward-looking policies taken by the successive governments since its dependence in 1948 that Myanmar has been transforming from being one of Asia’s most prosperous economies into one of the poorest ones through its heavily suppressed industrialization. As an issue peculiar to Myanmar, the continuous conflicts with ethnic minority groups and the imposed economic sanctions due to suppression of human rights have also made its economy stay at a stagnant position.
During the period of the so-called “Burmese Way to Socialism” from 1962 to 1988, the centrally planned and inward-looking strategies, i.e., nationalization
(Burmanization) of all major industries and import-substitution policies had long been pursued. These strategies resulted in economic problems such as inactive industrial production, high inflation, rising living costs, and macroeconomic mismanagement including demonetization in 1987, which eventually led to the collapse of the socialist regime in 1988.2 The economic decline saw the country officially reach Least
2 The nationwide protests to the socialist regime on August 8, 1988, referred to as the “8888 uprising”, dismantled the regime, and were brought to an end when the military retook power.
104
Developed Country status in 1987. U nder the next phase of military-ruled regime so-called SLORC/SPDC3, the government had started to promoted a market-oriented economy in its early stage. It typically promulgated the Foreign Investment Law in
1988 for the intake of private foreign capital, and the Sate-owned Economic Enterprise
Law in 1989 for authorizing private companies to engage in specific industries. After the Asian currency crisis in 1997, however, the government policies had again turned inward and against market-mechanisms by adopting measures to emphasize import-substitutions and to intervene in many economic activities with state controls.
Reforms under President U Thein Sein
The birth of new civilian government led by President U Thein Sein since March
2011 after the dissolved SPDC under a new constitution has turned around the trend again. It has launched the wide-ranging reforms toward an open and market-oriented economy. The inaugural speech of President U Thein Sein embraced “reform and openness”. A year after a series of politically liberalizing measures were introduced,
President announced a “second stage of reforms” in May 2012, focusing on the social and economic transformation of Myanmar. In accordance with his vision and guidelines, the Framework for Economic and Social Reforms (FESR) has been presented for prioritizing policy agenda for 2012-15 towards the long-term goals of the National
Comprehensive Development Plan that the government is drawing up as a 20-year long-term plan. The FESR consists of ten areas of interrelated reforms, namely, 1) fiscal
3 SLORP means the State Law and Order Restoration Council for 1988 – 1997 and SPDC means the State Peace and Development Council for 1997 – 2011.
105 and tax reform, 2) monetary and finance sector reform, 3) trade and investment liberalization, 4) private sector development, 5) improvements in health and education,
6) food security and agricultural growth, 7) governance and transparency, 8) mobile telephony and internet, 9) infrastructure investment, and 10) efficient and effective government.
We herein emphasize the following three points related with the economic reforms above. First, one of the most notable reforms that have been carried out to the present is the improvements in exchange rate system in Myanmar. Since October 2011, private banks have been allowed to trade foreign currency in the usual market. What is more important is that a managed floating exchange rate regime has been established by the
Central Bank of Myanmar since April 2012 b y abolishing the multiple exchange rate system. This currency reformation has been and will be the basis for the development of private sectors in Myanmar. Second, the international community has responded positively to the reforms, by easing sanctions and by increasing development assistance.
The government of Japan has decided to provide a total of more than 100 billion
Japanese yen of ODA assistances as well as the debt-relief measure for Myanmar.4 For another instance, EU has adapted the LDC (least developed countries) framework of
Generalized System of Preferences (GSP) for Myanmar since July 2013. Third, the conflicts with ethnic minority groups as an issue peculiar to Myanmar have also been in the ending process. Ceasefires have been negotiated with 10 out of the 11 armed ethnic groups, and hundreds of political prisoners have been released, including Daw Aung
4 See http://www.mofa.go.jp/region/page23e_000032.html and http://www.mofa.go.jp/press/release/press6e_000096.html.
106
San Suu Kyi, who became a Member of Parliament following the 2012 by-elections.
2. Role of Inward FDI in Myanmar
This section discusses the role of inward FDI in Myanmar. As the ESRI report on the “potentials of the Asian economic zones” in 2013 emphasized, one of the driving forces for Asian economic growth has been and will be their economic integration through forming and deepening international networks in manufacturing sectors. The creation of the production networks usually involve the prevailing FDI undertaken by transnational corporations. UNCTAD (2013) identified the statistical relationship between FDI stock in countries and their participation in global value chains (GVCs).
Figure 3 indicates that their correlation is strongly positive, implying that FDI may be an important avenue for an economy to gain access to GVCs and increase their participation. Under this context, we first examine whether the FDI has really led to the growth of GDP and exports in ASEAN economies. Then we focus on the case of
Myanmar and investigate the prerequisites for Myanmar to accept inward FDI especially in manufacturing sector.
2.1 Impacts of FDI on GDP and Exports in ASEAN
Under the endogenous growth theory developed in the 1980s, FDI has been considered to have permanent growth effect in the host country through technology transfer and spillover. Most of empirical studies have found positive effects of FDI on
107 transitional and long-run economic growth through capital accumulation and technical and knowledge transfers. Some of them, however, identified opposite causality from growth to FDI, suggesting that FDI inflows have been attracted to the growing economies and markets.
Several studies of an individual economy have identified the causality between FDI and growth / exports: bidirectional causality between each pair of FDI, GDP and exports for China (Liu et al., 2002); unidirectional causality from FDI to GDP for Thailand
(Kohpaiboon, 2003), for Pakistan (Ahmad et al. 2004) and for Mexico and Argentina
(Cuadros et al., 2004); and bidirectional causality between FDI and GDP for Malaysia and Thailand (Chowdhury and Mavrotas, 2006). The studies targeting a group of economies have also verified their causality: unidirectional causality from FDI to GDP for 24 developing countries (Nair-Reichert and Weinhold, 2000); bidirectional causality between FDI and GDP for 31 countries (Hansen and Rand, 2006); and unidirectional causality from FDI to exports for 9 economies (Cho, 2005). It is Hsiao and Hsiao (2006) that investigated the most comprehensive causalities among all three variables of FDI,
GDP and exports together, through such a sophisticated method as panel-data-VA R causality analysis for eight East and Southeast Asian economies with the stationarity of each variable being examined. They also found unidirectional effects of FDI on GDP directly and also indirectly through exports in the panel-data analysis, although the analysis of individual economies represented different causality relations among sample economies.
Our study basically follows Hsiao and Hsiao (2006) in our analytical methodology with a focus on the impact of FDI on GDP as well as the one of FDI on exports. Our
108 contributions are to extend the sample period of Hsiao and Hsiao (2006), i.e. from 1986 to 2004 towards the period from 1984 to 2012, and to target all of ASEAN economies including such latecomers as Cambodia, Lao PDR, Myanmar and V ietnam whereas
Hsiao and Hsiao (2006) focus rather on forerunning economies.
Methodology and data
We examine the bilateral Granger causalities between FDI and GDP and between
FDI and exports by their time-series data of individual economies and also by their panel data of all sample economies.
The sample economies are ten ASEAN economies, and the sample period is from
1984 to 2012. T he data for FDI (inward), GDP and exports are retrieved from
UNCTADSTAT5 and expressed in real and natural logarithm terms: fdi, gdp and ext6.
We then construct a vector autoregression model with p-l ag, VA R (p), for fdi and gdp as well as for fdi and ext to test the bilateral Granger causalities as follows.
= + + + +
𝑡𝑡 1 𝑡𝑡−1 𝑝𝑝 𝑡𝑡−𝑝𝑝 𝑡𝑡 𝑦𝑦 𝜇𝜇 𝑉𝑉 𝑦𝑦 ⋯ 𝑉𝑉 𝑦𝑦 𝜀𝜀 where is a (2 × 1) column vector of the endogenous variables: = ( ) ′ 𝑡𝑡 𝑡𝑡 𝑡𝑡 𝑡𝑡 or =𝑦𝑦 ( ) , is a (2 × 1) constant vector, each of and𝑦𝑦 𝑓𝑓𝑓𝑓𝑓𝑓is a (2𝑔𝑔 𝑔𝑔 ×𝑔𝑔 2) ′ 𝑡𝑡 𝑡𝑡 𝑡𝑡 1 2 coefficient𝑦𝑦 𝑓𝑓𝑓𝑓𝑓𝑓 matrix,𝑒𝑒𝑒𝑒𝑒𝑒 each𝜇𝜇 of and is a ( 2 × 1) vector𝑉𝑉 of the lag𝑉𝑉 endogenous
𝑡𝑡−1 𝑡𝑡−2 variables, and is a (2 ×𝑦𝑦 1) vector of𝑦𝑦 the random error terms in the system. The lag
𝑡𝑡 𝜀𝜀 5 See http://unctadstat.unctad.org/EN/. 6 All the data are deflated by GDP deflator retrieved from “Key Indicators for Asia and the Pacific 2014” by ADB.
109 length p is selected by the minimum Akaike Information Criterion (AIC) with maximum lag equal to 2 under the limited number of observations.
Before examining the causality relations, we check the stationarity of the time-series and panel data by employing a unit root test, and if needed, a cointegration test for them. Based on the data property, we select either the level series or the first-difference series in the VAR estimation. Regarding a unit root test, we adopt the augmented Dickey-Fuller (ADF) test for the time-series data analysis of individual economies, and the Levin, Lin and Chu (LLC) test for the panel data analysis of all sample economies7. The both tests are conducted by including “intercept” and “trend and intercept” in the test equation. Table 2 reports the test results for the combination of fdi and gdp and that of fdi and ext. In Cambodia and Vietnam for the combination of fdi and gdp, and in panel data for any combinations, both variables are stationary in their level series, and so we use their level series for the VAR estimation. In Indonesia, Lao
PDR, Malaysia, Myanmar, Philippines, Singapore, and Thailand for any combinations, a set of variables are not stationary in the level, but stationary in the first-difference, which are supposed to be the case of I(1), and then can be further examined by Johansen cointegration test8. For the remaining cases, we use the first-difference series for the estimation. Table 3 represents the results of Johansen cointegration test with “intercept” and “trend and intercept” being included in the test equation. Both the trace test and the
Maximum-eigenvalue test indicate that the level series are cointegrated in Lao PDR and
Malaysia for the combination of fdi and gdp, and in Malaysia and Singapore for the
7 For the ADF test, see Said and Dickey (1984), and for the LLC test, see Levin et al. (2002). 8 See Johansen (1995).
110 combination of fdi and ext. The final selection of the level or first-difference series for the VAR model analysis of individual economies is described in the rightmost column of Table 3.
Estimated Causalities
We now show the estimation outcomes on the bilateral Granger causalities between
FDI and GDP and between FDI and exports by their time-series data of individual economies and also by their panel data of all sample economies (see Table 4). The main findings are as follows. First, regarding the panel data analysis, the very clear bidirectional causalities are significantly identified in both the combination of FDI and
GDP and that of FDI and exports. Second, as for the time-series data analysis of individual economies, each economy has different causality relations. When we focus on the direction from FDI to GDP, Lao PDR, Singapore, Thailand and Vietnam have its significant causality, while Philippines, Singapore and Thailand have the significant causality from FDI to exports. At least, such latecomers as Cambodia and Myanmar are turned out not to represent the causalities from FDI to GDP and exports. There should be some reservations in the time-series data analysis of individual economies since the number of observations in a sample country is limited only to 29 of annual data. This point seems to be in line with Hsiao and Hsiao (2006), which argues that the time-series analysis based on a single country cannot yield a general rule.
The panel data analysis of all sample economies, on the other hand, represents the clear causality from FDI to GDP and exports as well as the opposite causality from GDP and exports to FDI. It suggests that as a general tendency as a whole ASEAN, FDI has
111 been a driving force for economic growth through capital accumulation and technical and knowledge transfers, while FDI inflows have been attracted to the growing economies and markets. This finding is also consistent with Hsiao and Hsiao (2006), which verified the significant effects of FDI on GDP directly and also indirectly through exports in the panel-data analysis. It should also be noted that the significant causality from FDI to exports may imply that the inward FDI has facilitated the participation of international production networks.
2.2 How to Attract Inward FDI in Myanmar
We herein turn to the issue on F DI in Myanmar. The causality analysis for individual economies in the previous section revealed no significant impacts of FDI on
GDP and exports in the case of Myanmar. We pick up t he possible reasons for no causality in Myanmar as follows. First, the inward FDI share to GDP since the 2000s has been much lower in Myanmar than those in Thailand and Vietnam. Figure 4 indicates that the share in Myanmar has been around 2%, while that of Thailand and
Vietnam being around 4% and 4 -10% respectively. This fact may suggest that the role of FDI in Myanmar has been too small to give some impacts on GDP, while the larger share of FDI may have affected the trend in GDP in Thailand and Vietnam as the previous causality analysis represented. Second, the inward FDI in Myanmar has highly depended on oil and gas sectors. Figure 5 s hows the industrial composition of the inward FDI during the period from fiscal year of 1989 to 2010 in Myanmar, and tells us that the oil and gas sector occupies 69% whereas the manufacturing sector does only 5%.
112
The natural resources exploration by foreign investors has not much involved domestic job opportunities and technology transfers so far in the case of Myanmar.9
The question is, therefore, how to attract inward FDI into Myanmar, in particular, in manufacturing sectors. As a matter of fact, there have been still several constraints in doing business in Myanmar. Figure 5 r epresents the top 10 business constraints in
Myanmar as the results of the survey that the International Finance Corporation, World
Bank Group conducted from February to April in 2014 by interviewing with business owners and top managers of 632 firms. It tells us that the legal system of the access to finance and land, infrastructure such as electricity and transportation, and educated workforces are main business constraints, which can also be a blockage to attract FDI in
Myanmar. Regarding the soft infrastructure indicated by the Logistic Performance Index
(LPI) by the World Bank in Figure 7, its performances of Myanmar is far behind the other ASEAN economies and even behind such latecomers as Cambodia, Lao PDR and
Vietnam.
As a formal legal system on FDI, the Foreign Investment Law was promulgated in
November 2012 in Myanmar. It is expected that the new law together with the economic reforms under President U Thein Sein will remove the business constraints and facilitate inward FDI in Myanmar.
3. The Way toward Industrial Upgrading for Myanmar
This section discusses the way toward industrial upgrading for Myanmar from a
9 This description is based on the phone interview by Dr. Ni Lar with some oil and gas enterprises.
113 long-term perspective. As we showed in the previous sections, Myanmar now depends heavily on n atural resource production in its economy, and also on l abor-intensive production in its manufacturing sector in the initial process of participating in the GVCs.
In this sense, Myanmar is still facing the risk of “thin” industrialization, where a country enters an industry, but only in its low-value and low skill aspects such as natural resource exploration and assembly of manufacturing products without the ability to upgrade. In a large scale economy like Myanmar with about 60 million populations, its economic growth accompanied with income growth often would induce the higher demand for imported goods, and thus reach so-called “balance of payment ceiling”, which might make its economic growth unsustainable.10 The sustainable growth would, therefore, require such consolidated production structures as industrial diversification towards a variety of manufacturing sectors and industrial upgrading towards upstream and high-valued manufacturing sectors.
Dynamism in GVCs Participation
In the aforementioned context, the ESRI report on t he “potentials of the Asian economic zones” in 2013 r epresents the dynamism of industrial upgrading in Asian economies with empirical evidence. In this report, three key variables in the manufacturing sector are extracted by using value-added-trade data: 1) foreign value added as a share of gross exports (FVX) as a proxy of GVCs participation ratio; 2) domestic value added as a share of gross exports (DVX), and 3) domestic value added
10 The resource dependent economies also have a r isk of suffering so-called “Dutch disease”. Kubo (2014) points out this risk in Myanmar.
114 in exports as a ratio of GDP (DVY). The report then constructs a hypothesis illustrated in Figure 8. At the early stage before GVCs participation, an economy stays at high
DVX and low DVY, in which most of exports are domestically produced and their contribution to GDP is small. When an economy participate in GVCs, it moves to the stage with low DVX and high DVY, since an economy’s production for its exports have to depend highly on imports of parts, components and machineries from foreign countries, whereas its absolute production value for exports contributes a lot to its rising
GDP. At the matured stage of GVCs involvement, an economy can enjoy a combination of high DVX and high DVY; its production for exports continues to contribute to GDP growth, and at the same time, the dependence on imports for its exports declines due to the expansion of domestic productive capacities.
This hypothesis was empirically tested by a panel-data analysis with samples of eight Asian developing economies for 1995, 2000, 2005 and 2008. The main findings were: 1) the economies’ participation in GVCs in manufacturing sectors has allowed the absolute domestic value added for their exports to contribute to their GDP growth; 2) the development paths of domestic value added contributions to exports in the GVCs participating economies have followed “smile curve” with its turning point being 5,651
U.S. dollars in per capita GDP.
These findings imply the dynamic process of GVCs impacts, where at the initial stage of GVCs participation the domestic value added contributions to exports have reduced, but have recovered at the later stage of GVCs involvement with upgrading domestic productive capacities. The process of enhancing local productive capacities may involve a number of mechanisms: the key exporting industries may provide
115 opportunities for local industries to be raised up and participate in GVCs, which will leads to generating additional value added through local outsourcing within and across industries; and/or the key exporting industries themselves may attain their industrial upgrading through technology dissemination and skill building, which will improve their productivity and will facilitate their entries and expansions towards higher valued sectors. It should be noted that these development paths are not always realized automatically and its achievements differ according to the characteristic of the GVCs and the involved countries. Government policies also matter to optimize the economic contributions of the GVCs participation and involvement.
Dynamism in Multi-manufacturing Sectors and Double-track Strategy
We herein extend the analysis above of the previous report by breaking down the
“smile curve” into the ones of individual manufacturing sectors. Specifically, we examine the relationships between DVX and per capita GDP in each of eight manufacturing sub-sector categories: “Food products, beverages and tobacco”, “Textiles, textile products, leather and footwear”, “Wood, paper, paper products, printing and publishing”, “Chemicals and non-metallic mineral products”, “Basic metals and fabricated metal products”, “Machinery and equipment”, “Electrical and optical equipment” and “Transport equipment”.11
As Table 5 reported, all the coefficients of PCY are significantly negative and those of a square of PCY are discernibly positive, and thus the smile curves were identified in
11 As for the data source and methodology, see the ESRI report on the “potentials of the Asian economic zones” in 2013.
116 the development paths of all manufacturing categories. The most noteworthy finding is that the turning points of smile curves differ a lot and even represent a clear contrast according to manufacturing sectors as the sectoral “smile curve” in Figure 9 illustrated.
The sectors of food, wood and textile products reach the turning point at lower per capita GDP ranging from 5,100 t o 5,400 U.S. dollars and at higher ratio of domestic value added contributions to gross exports from 57% to 71%. On the contrary, the sectors of machinery, electrical, and transport equipment face the turning point at higher per capita GDP ranging from 5,800 to 6,400 U.S. dollars and at lower ratio of domestic value added contributions to gross exports from 37% to 49%. It suggests that the sectors of food, wood and textile products, which require relatively less sophisticated technologies and a smaller number of supply chains, can attain the higher degree of localization of production capacity necessary for exports at the earlier time; on the other hand, the sectors of machinery, electrical, and transport equipment, which involve relatively more sophisticated technologies and a larger number of supply chains, take a longer time to raise up the local production capacity, since such sectors need to acquire a lot of technology transfer along with long supply-chains and also to materialize transferred technology for their local production.
What this finding implies for Myanmar industrial strategy is “double-track” strategy.
As the first track, Myanmar should promote such less sophisticated sectors as food, wood and textile products, whose production is rather easy to be localized. It is namely a “quick-win” approach to pursue the maximization of the existing local-resource utilization in the short-term perspective. In a large scale economy like Myanmar with about 60 million populations, however, there should be another track at the same time to
117 clear the afore-mentioned balance-of-payment constraint. Myanmar should raise up some key industries among more sophisticated sectors such as machinery, electrical, and transport equipment in the long run. For this purpose, the role of inward FDI is definitely important in that the FDI involves technology transfers to contribute to enhancing the local production capacity in Myanmar manufacturing sectors. The policy priority should thus be put on a ttracting the inward FDI in the sophisticated manufacturing sectors, in terms of institutional arrangement like special economic zone, infrastructure development and human resource development.
4. The Way toward Geographical Linkage for Myanmar
This section deals with the issue on geographical linkage for Myanmar from a long-term perspective. At present, Myanmar depends on spot-area development through the SEZ framework. Figure 10 shows three big development projects: Dawei projects cooperated by Thailand, Thilawa projects by Japan, and Kayukphyu projects by China.
The new SEZ law to provide privileges for area-development was promulgated in
January 2014, a nd the adaptation of this SEZ law to the three development project above was already decided. Myanmar has development projects in other spot-areas including the border areas with Thailand, China, and India, although the SEZ law has not yet been decided to be adapted to these areas.
The critical issue is then how to extend the economic impacts of the spot-area development to nation-wide level in the long-run. In this context, the creation of
“economic corridor” is one of the most attractive approaches in the sense that this
118 approach would make it possible for the developing spot areas to be linked with each other. The economic-corridor approach is not confined to constructing roads and bridges as infrastructure projects, but contains the momentum to facilitate trade and investment.
In particular, it is expected that the production networks and supply-chain networks would be formed along with the corridor line, since it would create win-win relationships of each spot-area production. If the economic corridor is linked with the production base in more advanced neighboring countries, the approach would be more attractive since it mi ght involve the inward FDI with technology transfers from the neighboring economies. This dimension would be nothing more than the concept of three economic corridors proposed by ADB in the Greater Mekong Sub-region including Myanmar.
We herein focus on t he two supposed corridors crossing Thailand, and describe their development strategies. The two corridors are Mae Sot – Myawaddy – Hpa-an –
Yangon, and Bangkok – Kanchanaburi – Hit Khee – Dawei, which nearly correspond to
East-West Economic Corridor and South Economic Corridor in the GMS economic corridors proposed by ADB (see again Figure 10).12
Mae Sot – Myawaddy – Hpa-an – Yangon: Garment Industry Corridor
We first describe the current situation of the corridor for Mae Sot – Myawaddy –
Hpa-an – Yangon. Mae Sot is a city of Thailand bordering Myawaddy, a city of
Myanmar. Mae Sot has a large industrial agglomeration that is composed of labor-intensive manufacturing such as garment, textile, and food-processing. About 400
12 The description thereafter is mainly based on JICA (2014).
119 factories are located and around 20,000 migrants with legal qualification are working in
Mae Sot. The adaptation of SEZ framework to Mae Sot is under consideration by
Government of Thailand.
Myawaddy is a border city in Myanmar side. Myawaddy has set up t he Border
Trade Zone since 2008 partly and 2009 f ully, and has also been constructing the industrial zone towards its completion in 2015. The Border Trade Zone is located 11 km away from the border on a site with around 200 ha. The industrial zone now under construction is located at the neighbor of the Border Trade Zone on t he west and is planned to occupy 1,301 acres (equivalent to 527 hectare). The zone has completed its planning and designing, in which power supply and transmission system will depend on the Thai side. The expected industries in the zone are garment, food-processing, fertilizer, wood product/ furniture, etc.
Hpa-an is the capital city of Kayin State in Myanmar, and 185 km away from
Myawaddy. Hpa-an has set up an industrial zone since 2011, which is located 11 km away from the city and occupies 1,000 acre (equivalent to 405 hectare). The zone has such several factories as those of garment, wood processing, construction materials and candle manufacturing, but not many factories have been established yet. In this zone, electricity and water supply system have not completed yet. In particular, the insufficiency of electricity is so serious that companies have to equip their own generators. Yangon is a well-known for a former capital city in Myanmar with 6 million people, one-tenth of nation-wide population.
The trade flows along with the corridor can be represented by the border trade between Mae Sot and Myawaddy. The trade value (the sum of exports and imports)
120 between Thailand and Myanmar counted at the Mae Sot custom house amounts to 1.5 billion USD in 2013, which occupies more than half of the total Thai border trade with
Myanmar excluding the import of natural gas, and thus the largest among trade values counted at border custom houses bordering Myanmar. The annual growth of the trade value records 82 % increase in 2012 ( affected by the gate-lock from July 2010 t o
December 2011) and 17 % increase in 2013. The import value from Thailand extremely exceeds the export value to Thailand. The main articles of imports from Thailand are gasoline, beer, textile and those of exports to Thailand are buffalo alive, wood and mining products. As for the investment flows, there have been some progressive symptoms in that some Thai garment companies at Mae Sot have shown concern to set up their branch factories in Hpa-an.
Regarding future prospects on t he creation of production networks and supply chains along with the corridor, we can expect the following driving forces for their promotion. First, the large gap in wage-levels between Myanmar and Thailand would urge manufacturing companies at Mae Sot to extend their supply-chains towards
Myanmar side by setting up t heir branch factories with labor-intensive process.
Thailand has raised its minimum-wage drastically to 300 baht per day since January in
2013. Most of factories have then lost an advantage of employing migrant workers at lower wages since the minimum-wage has also been adapted to migrant workers, and thus they are facing the serious and urgent needs to save labor costs. According to “The
24th Comparative Survey of Investment-Related Costs in Major Cities and Regions in
Asia and Oceania May 2014” by JETRO, the monthly average wage of factory workers is 366 US dollar at Bangkok, whereas it is 71 at Yangon, one-fifth of that at Bangkok.
121
Second, the changes in the adaptation of GSP (Generalized System of Preferences) by
EU for both countries also would promote the investment to Myanmar by Thai manufacturing companies. While Thailand will graduate from EU-GSP in January 2015,
Myanmar has been a target of EU-GSP as LDC (least developed countries) framework since July 2013. Third, the growing markets, in particular, at such big cities as Yangon and Hpa-an, in accordance with income increases in Myanmar, would provide the motivation for Thai companies manufacturing consumer goods to invest inside of
Myanmar. These driving forces might facilitate the invitation of Thai manufacturing investors, and if we suppose that Thai manufacturing companies at Mae Sot extend their supply-chains towards Myanmar side, it mig ht create a “garment industry” corridor, since the garment industry is one of the major industries at Me Sot.
On the other hands, there have been such issues to be cleared for the corridor creation as the adaptation of SEZ framework for enabling “in-bond processing”, enhancing connectivity through infrastructure development, and securing labor forces with skill development, as the ESRI report on t he “potentials of the Asian economic zones” in 2013 emphasized. We herein add the progress in road connectivity along with the corridor. The road linkage between Yangon and Myawaddy is important in the sense that for Yangon, Myawaddy is the closest border gate, and the road connectivity further extends to the deep-sea Laem Chabang Port in Thailand. At the event of Cyclone Nargis in May 2008 b y which Yangon Port was seriously damaged, the road linkage from
Yangon through Myawaddy to Laem Chabang Port was refocused much attention on.
The most serious section is the one between Myawaddy and Kawkareik, which is so hilly and narrow that the traffic has been restricted to only one way with the direction
122 being changed on every alternative day. As Figure 11 indicates, the alternative by-pass road between Myawaddy and Kawkareik is under construction by getting the grant from
Thai government towards its completion in 2015. The road from Kawkareik to Ein Du will be rehabilitated by getting the assistance from ADB, while the other sections are under consideration through feasibility study.
5. Prospect of SEZs in Lao PDR
In order to prospect the direction of SEZs in Lao PDR, issues which SEZs have faced are discussed here.
The first issue is human resource development. One aspect is the staff of the SEZA.
As mentioned the case of Boten Beautiful Land and Golden Triangle, coordination ability is essential to implement SEZs. In order to achieve it, the staff should understand the legal framework relating to SEZs, and have strong talent to negotiate to relating agencies. Utilizing the experiences of SaSEZ will be efficient. Another aspect of human resource is labor force in SEZs. As the total population is around 6 million in Lao PDR, labor supply in the future is limited. Improvement of quality of labor and produce high value added products is necessary for next step.
The next issue is institutional arrangement in the field of border transaction. If the customs clearance or quarantine takes time too long, the effect of physical infrastructure will be lost. The Cross Border Transportation Agreement (CBTA) was exchanged among GMS countries under the Mekong Program in 2003. Discussing the Annex and
Protocol in each detail field, memorandum of understanding for each border is being coordinated. One stop service and single window service are expected to progress to
123 facilitate border transaction.
The final issue is infrastructure building inside Lao PDR. In fact, if SEZs of Lao
PDR in border area depend on t he transaction with Thailand only, there are few problems for Lao side to be a free rider of Thai infrastructure. However, in order to increase the transaction of Lao product into the regional production network, transportation should be improved in Lao PDR. It includes the road repair together with new construction. For example, as route 9, w hich is a route of EWEC in Lao PDR, needs road repair frequently, and it requests logistic industry more burden. Not only input the resource to construct new infrastructure, but also improvement of repair of infrastructure is expected to be established.
124
Table 1 Economic Profile of Myanmar
Population GDP GDP per capita HDI Index (million, 2013) ($bil., 2013) ($, 2013) (Ranking/187, 2012)
Myanmar 64.9 56.4 869 149
Cambodia 15.4 15.7 1,016 138
Lao P.D.R. 6.8 10.0 1,477 138
Thailand 68.2 387.2 5,674 103
Vietnam 89.7 170.6 1,902 127
Sources: Population, GDP and GDP per capita are from World Economic Outlook Database April 2014 (Estimates in 2013). HDI (Human Development Index) is from UNDP website.
Figure 1 GDP by Industrial Origin (% share, at current producer prices in 2012)
Agriculture Mining Manufacturing Construction Services
30.5 19.9 38.7 Myanmar
11.1 29.1 53.4 Thailand
0.0 20.0 40.0 60.0 80.0 100.0
Sources: Key Indicators for Asia and the Pacific 2014 by Asian Development Bank (ADB)
125
Figure 2 Trade Structure (% share, Apr. 2013 – Mar. 2014)
Export (total 11.2 bil. $) Import (total 13.8 bil. $)
Marine Others Products 13% 6% Consumer Natural Gas goods
29% 24%
Forest Capital Products goods 9% 44%
Mineral Manufactural Products Products Intermediate 10% 9% goods Agricultural 32% Products 24%
Sources: https://www.mnped.gov.mm/html_file/foreign_trade/s07MA0201.htm
Figure 3 Correlation between Inward FDI Stock and GVC Participation
126
Table 2 Results of ADF and LLC Unit Root Tests
Combination of fdi and gdp ADF Unit Root Test level first difference For estimation intercept trend & intercept intercept trend & intercept Brunei fdi -3.71** -2.09 -2.94* -3.35* first difference gdp -0.34 -7.07*** -9.70*** -9.24*** Cambodia fdi -8.05*** -4.21** - - level gdp -7.54*** -1.81 - - Indonesia fdi -2.07 -2.04 -3.66** -3.59** I (1) gdp -2.99** -1.33 -5.38*** -5.27*** Lao PDR fdi -2.36 -2.19 -2.93* -3.04 I (1) gdp -1.69 -1.93 -5.13*** -5.88*** Malaysia fdi -1.05 -1.64 -5.27*** -5.20*** I (1) gdp -0.61 -1.88 -4.88*** -4.79*** Myanmar fdi -2.50 -2.28 -3.71** -4.02** I (1) gdp -2.79* 0.61 -3.28** -4.98*** Philippines fdi 0.10 -2.01 -4.40*** -4.53*** I (1) gdp -1.26 -0.68 -4.31*** -4.59*** Singapore fdi -0.63 -3.92** -5.86*** -5.91*** I (1) gdp -0.59 -2.30 -3.84*** -3.83** Thailand fdi -1.32 -2.36 -5.70*** -5.84*** I (1) gdp -0.86 -2.27 -3.98*** -3.91** Vietnam fdi -3.81*** -6.74*** - - level gdp -3.61** -7.41*** - -
LLC Unit Root Test level first difference For estimation intercept trend & intercept intercept trend & intercept Panel data fdi -1.70** -1.21 - - level gdp -2.01** -0.21 - -
Combination of fdi and ext ADF Unit Root Test level first difference For estimation intercept trend & intercept intercept trend & intercept Brunei fdi -3.71** -2.09 -2.94* -3.35* first difference ext -1.94 -3.72* -4.94*** -5.11*** Cambodia fdi -8.05*** -4.21** -11.10*** -10.41*** first difference ext -2.30 -1.76 -12.77*** -12.96*** Indonesia fdi -2.07 -2.04 -3.66** -3.59** I (1) ext -2.60 -2.37 -4.78*** -4.87*** Lao PDR fdi -2.36 -2.19 -2.93* -3.04 I (1) ext -1.33 -2.21 -6.02*** -6.13*** Malaysia fdi -1.05 -1.64 -5.27*** -5.20*** I (1) ext -1.75 -1.57 -4.17*** -4.42*** Myanmar fdi -2.50 -2.28 -3.71** -4.02** I (1) ext -0.85 -1.57 -4.61*** -4.49*** Philippines fdi 0.10 -2.01 -4.40*** -4.53*** I (1) ext -0.51 -0.18 -4.15*** -5.61*** Singapore fdi -0.63 -3.92** -5.86*** -5.91*** I (1) ext -0.35 -2.26 -5.27*** -5.19*** Thailand fdi -1.32 -2.36 -5.70*** -5.84*** I (1) ext -1.59 -1.90 -4.10*** -4.16** Vietnam fdi -3.81*** -6.74*** -13.99*** -14.10*** first difference ext -1.41 -2.06 -4.55*** -4.47***
LLC Unit Root Test level first difference For estimation intercept trend & intercept intercept trend & intercept Panel data fdi -1.70** -1.21 - - level ext -2.64*** 1.36 - -
Note: (1) The lag length in the test equation follows automatic selection by Schwarz Info Criterion. (2) ***, **, * denote rejection of null hypothesis of “series has a unit root” at the 1%, 5% and 10% level of significance, respectively.
127
Table 3 Results of Johansen Cointegration Test Combination of fdi and gdp intercept trend & intercept For estimation trace max-eigen trace max-eigen Brunei - - - - first difference
Cambodia - - - - level
Indonesia 9.10 6.15 16.30 12.21 first difference
Lao PDR 26.80*** 21.33*** 27.67** 21.53** level
Malaysia 17.13** 15.74** 26.73** 19.52** level Myanmar 16.83** 10.21 18.05 11.35 first difference
Philippines 2.01 1.95 11.01 9.09 first difference
Singapore 5.56 5.15 20.37 15.85 first difference Thailand 6.26 4.16 8.22 5.94 first difference Vietnam - - - - level
Combination of fdi and ext intercept trend & intercept For estimation trace max-eigen trace max-eigen Brunei - - - - first difference Cambodia - - - - first difference Indonesia 10.83 7.22 13.02 8.88 first difference Lao PDR 15.05* 10.17 16.60 11.31 first difference Malaysia 19.50** 16.16** 21.89 16.96 level Myanmar 7.13 6.43 10.60 7.87 first difference Philippines 1.70 1.67 13.93 12.37 first difference Singapore 7.31 6.65 26.3** 19.80** level
Thailand 10.31 8.01 14.67 8.77 first difference Vietnam - - - - first difference
Note: ***, **, * denote rejection of null hypothesis of “no cointegrating equations” at the 1%, 5% and 10% level of significance, respectively.
128
Table 4 Results of Granger Causality Test Combination of fdi and gdp Lags Null Hypothesis F-Statistic D(FDI) does not Granger Cause D(GDP) 0.52 Brunei 1 D(GDP) does not Granger Cause D(FDI) 0.03 FDI does not Granger Cause GDP 0.46 Cambodia 2 GDP does not Granger Cause FDI 5.61** D(FDI) does not Granger Cause D(GDP) 0.03 Indonesia 1 D(GDP) does not Granger Cause D(FDI) 0.00 FDI does not Granger Cause GDP 3.79** Lao PDR 2 GDP does not Granger Cause FDI 0.10 FDI does not Granger Cause GDP 0.12 Malaysia 1 GDP does not Granger Cause FDI 7.76** D(FDI) does not Granger Cause D(GDP) 0.03 Myanmar 1 D(GDP) does not Granger Cause D(FDI) 0.31 D(FDI) does not Granger Cause D(GDP) 0.00 Philippines 1 D(GDP) does not Granger Cause D(FDI) 0.94 D(FDI) does not Granger Cause D(GDP) 6.76*** Singapore 2 D(GDP) does not Granger Cause D(FDI) 0.31 D(FDI) does not Granger Cause D(GDP) 21.84*** Thailand 1 D(GDP) does not Granger Cause D(FDI) 3.09* FDI does not Granger Cause GDP 12.80*** Vietnam 2 GDP does not Granger Cause FDI 16.76*** FDI does not Granger Cause GDP 20.85*** Panel data 2 GDP does not Granger Cause FDI 9.44***
Combination of fdi and ext Lags Null Hypothesis F-Statistic D(FDI) does not Granger Cause D(EXT) 0.95 Brunei 1 D(EXT) does not Granger Cause D(FDI) 0.41 D(FDI) does not Granger Cause D(EXT) 0.80 Cambodia 2 D(EXT) does not Granger Cause D(FDI) 6.19** D(FDI) does not Granger Cause D(EXT) 0.38 Indonesia 1 D(EXT) does not Granger Cause D(FDI) 0.69 D(FDI) does not Granger Cause D(EXT) 2.06 Lao PDR 1 D(EXT) does not Granger Cause D(FDI) 0.00 FDI does not Granger Cause EXT 0.00 Malaysia 1 EXT does not Granger Cause FDI 6.21** D(FDI) does not Granger Cause D(EXT) 1.13 Myanmar 1 D(EXT) does not Granger Cause D(FDI) 3.76* D(FDI) does not Granger Cause D(EXT) 11.55*** Philippines 2 D(EXT) does not Granger Cause D(FDI) 3.26* FDI does not Granger Cause EXT 3.55** Singapore 2 EXT does not Granger Cause FDI 0.02 D(FDI) does not Granger Cause D(EXT) 8.52*** Thailand 1 D(EXT) does not Granger Cause D(FDI) 0.71 D(FDI) does not Granger Cause D(EXT) 0.01 Vietnam 1 D(EXT) does not Granger Cause D(FDI) 0.16 FDI does not Granger Cause EXT 5.58*** Panel data 2 EXT does not Granger Cause FDI 8.27***
Note: ***, **, * denote rejection of null hypothesis at the 1%, 5% and 10% level of significance, respectively.
129
Figure 4 Inward FDI Share to GDP in Myanmar (%)
Myanmar Thailand Vietnam 12.0
10.0
8.0
6.0
4.0
2.0
0.0 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2011 2012 1990 1991 2010
Sources: Inward FDI from International Financial Statistics, IMF; GDP from World Economic Outlook, IMF
Figure 5 Industrial Composition of Inward FDI in Myanmar (%)
Source: DICA, MNPED http://www.dica.gov.mm/dicagraph%200.htm
130
Figure 6 Top 10 Business Constraints in Myanmar The survey results by International Finance Corporation (Feb.- Apr. 2014)
Source: http://www.enterprisesurveys.org/Data/ExploreEconomies/2014/myanmar#firm-characteristics--ownershi p-type
Figure 7 Global Ranking in Logistics Performance Index 2014 by the World Bank (Total: 160 countries)
International Logistics Tracking & Overall LPI Customs Infrastructure Timeliness shipments competence tracing Singapore 5 3 2 6 8 11 9 Malaysia 25 27 26 10 32 23 31 Thailand 35 36 30 39 38 33 29 Indonesia 53 55 56 74 41 58 50 Philippines 57 47 75 35 61 64 90 Vietnam 48 61 44 42 49 48 56 Lao PDR 131 100 128 120 129 146 137 Cambodia 83 71 79 78 89 71 129 Myanmar 145 150 137 151 156 130 117
Source: Logistics Performance Index 2014: The World Bank, (http://lpisurvey.worldbank.org/international/global)
131
Figure 8 Hypothesis on Development Paths of GVCs Participation
Stage I: Before IPN Stage II: IPN Stage III: IPN Participation Participation Evolution High DVX Low DVX High DVX Low DVY High DVY High DVY
GDP Export GDP Export GDP Export
Domestic VA Foreign VA
Source: Taguchi (2014) 7
132
Table 5 Estimation on Development Paths of GVCs Participation by Sectors Variales DVX Asia Food products, Textiles, textile Wood, paper, paper Chemicals and non- beverages and products, leather and products, printing and metallic mineral tobacco footwear publishing products
Const. 88.256 *** 69.926 *** 82.066 *** 72.510 *** (23.555) (10.214) (18.729) (13.731) PCY -6.661*10-3 *** -4.830*10-3 ** -7.894*10-3 *** -8.022*10-3 *** (-3.417) (-2.399) (-3.281) (-3.111) PCY2 6.350*10-7 *** 4.470*10-7 ** 7.640*10-7 *** 7.940*10-7 *** (3.022) (2.162) (2.911) (2.889)
Turning Point 5,245 5,403 5,166 5,052 Average DVX 80.3 64.1 72.7 63.1 Adj R**2 0.203 0.103 0.209 0.185 Sample size 32 32 32 32 Estimation Type Random Random Random Random
Variales DVX Asia Basic metals and Machinery and Electrical and optical fabricated metal Transport equipment equipment, nec equipment products
Const. 70.362 *** 65.220 *** 65.469 *** 69.928 *** (10.097) (10.819) (8.534) (10.608) PCY -8.410*10-3 *** -5.941*10-3 ** -9.767*10-3 *** -7.291*10-3 *** (-3.488) (-2.487) (-3.456) (-2.803) PCY2 6.890*10-7 *** 4.660*10-7 * 8.260*10-7 *** 6.210*10-7 ** (2.763) (1.870) (2.815) (2.290)
Turning Point 6,103 6,374 5,912 5,870 Average DVX 59.6 57.4 53.1 60.7 Adj R**2 0.253 0.141 0.261 0.170 Sample size 32 32 32 32 Estimation Type Random Random Random Random Note: 1) The T-value is shown in parenthese. 2) One, two, or three asterisks indicate that a coefficient estimate is significantly different from zero at 10, 5, or 1% percent level, respectively. Source: OECD TiVA Data May 2013
133
Figure 9 Smile Curve by Manufacturing Sectors
Food Products Wood Products Cambodia China India Indonesia Cambodia China India Indonesia Malaysia Philippines Thailand Viet Nam Malaysia Philippines Thailand Viet Nam 100.0 100.0 95 08 95 08 00 00 05 05 90.0 90.0
80.0 80.0
70.0 70.8 70.0
61.7 60.0 60.0
50.0 50.0
Domestic Value added / Gross Exports (%) 40.0 Domestic Value added / Gross Exports (%) 40.0
5,245 30.0 30.0 5,166 100 1,000 10,000 100 1,000 10,000 Per Capita GDP (US dollar) Per Capita GDP (US dollar)
Textile Products Transport Equipment Cambodia China India Indonesia Cambodia China India Indonesia Malaysia Philippines Thailand Viet Nam Malaysia Philippines Thailand Viet Nam 100.0 100.0 95 08 95 08 00 00 05 05 90.0 90.0
80.0 80.0
70.0 70.0
60.0 60.0 56.9 50.0 50.0 48.5 Domestic Value added / Gross Exports (%) Domestic Value added / Gross Exports (%) 40.0 40.0
5,403 30.0 30.0 100 1,000 10,000 100 1,000 5,870 10,000 Per Capita GDP (US dollar) Per Capita GDP (US dollar)
Machinery Equipment Electrical Equipment Cambodia China India Indonesia Cambodia China India Indonesia Malaysia Philippines Thailand Viet Nam Malaysia Philippines Thailand Viet Nam 100.0 100.0 95 08 95 08 00 00 05 05 90.0 90.0
80.0 80.0
70.0 70.0
60.0 60.0
50.0 50.0 46.3
Domestic Value added / Gross Exports (%) 40.0 Domestic Value added / Gross Exports (%) 40.0 36.6 30.0 6,374 30.0 5,912 100 1,000 10,000 100 1,000 10,000 Per Capita GDP (US dollar) Per Capita GDP (US dollar)
134
Figure 10 Spot-area Development Projects in Myanmar
Big 3 projects Border area projects SEZ prepared in MM-TH Corridors China India Hpa-an & Myawaddy along with EWEC Bangladesh Laos Kayukphyu linked with China, etc. Thailand
Thilawa Southern Corridor (SEC) linked with Japan, etc.
Dawei Hit KheeAlong with along with SEC along with SEC
Figure 11 Road Development from Myawaddy to Kawkareik Toward Yangon Thailand Hpa-an Myanmar 2nd Bridge (construction) Thaton Ein Du Existing Route Thin Gan 44 km Nyi Naung
Mae Sot Myawaddy 1st Bridge Moulmein Kawkareik New Route 28 km
Mudon
Investigated Route Toward Dawei
Source: Author based on “The Daily NNA” on December 25, 2013
135
References: Ahmad, M.H., Alam, S., and Butt, M.S. 2004. Foreign direct investment, exports and domestic output in Pakistan. Paper presented at the 19th Annual General Meeting, PIDE, Quaid-e-Azam University, Islamabad. Cho, K. 2005. S tudies on know ledge spillovers, trade, and foreign direct investment—Theory and empirics, Ph.D. Thesis, Boulder, CO: Department of Economics, University of Colorado at Boulder. Chowdhury, A., and Mavrotas, G. 2006. F DI and growth: What causes what? World Economy, 29(1), pp. 9–19. Cuadros, A., Orts, V., and Alguacil, M. 2004. O penness and growth: Re-examining foreign direct investment, trade and output linkages in Latin America. The Journal of Development Studies, 40(4), pp. 167–192. Economic Research Institute for ASEAN and East Asia (ERIA). 2013. Draft of Myanmar Comprehensive Development Vision (MCDV). Hansen, H., and Rand, J. 2006. O n the causal links between FDI and growth in developing countries. World Economy, 29(1), pp. 21–41. Hsiao, F.S.T., and Hsiao, MC. W. 2006. FDI, exports, and GDP in East and Southeast Asia—Panel data versus time-series causality analyses. Journal of Asian Economics, 17, pp. 1082–1106. Japan International Cooperation Agency (JICA) 2014. R eport on J ob Creation by Border Area Development between Thailand and Myanmar, Phase II. Johansen, S. 1995. Likelihood-based Inference in Cointegrated Vector Autoregressive Models (Oxford: Oxford University Press). Kohpaiboon, A. 2003. Foreign trade regimes and FDI-growth nexus: A case study of Thailand. The Journal of Development Studies, 40(2), pp. 55–69. Kubo, K. 2014. M yanmar’s Non-resource Exports Potential after the Lifting of Economic sanctions: a Gravity Model Analysis. Asia-Pacific Development Journal, 21(1), pp. 1-22. Levin, A., Lin, C.F., and Chu, C. 2002. Unit root tests in panel data: Asymptotic and finite-sample properties. Journal of Econometrics, 108, pp. 1–24. Liu, X., Burridge, P., and Sinclair, P.J.N. 2002. Relationships between economic growth, foreign direct investment and trade: Evidence from China. Applied Economics, 34, pp. 1433–1440. Nair-Reichert, U., and Weinhold, D. 2000. Causality tests for cross-country panels: New look at FDI and economic growth in developing countries. Oxford Bulletin of Economics and Statistics, 64, pp. 153–171. OECD. 2013. Multi-dimensional Review of Myanmar: Volume 1. Initial Assessment, OECD Development Pathways, OECD Publishing. http://dx.doi.org/10.1787/9789264202085-en Said, S., and Dickey, D.A. 1984. Testing for unit roots in autoregressive-moving average models of unknown order, Biometrika, 71, pp. 599–607. Taguchi, H. 2014. Dynamic Impacts of Global Value Chains Participation on A sian Developing Economies. Foreign Trade Review, 49(4), pp. 1-14. UNCTAD. 2013. World Investment Report - Global Value Chains: Investment and Trade for Development, UNCTAD.
136
Chapter VII: Long-term Projection of Myanmar Economy by Macro Econometric Model
Hiroyuki Taguchi, Saitama University, and Visiting Researcher, ESRI
Ni Lar, Chiang Mai University, Thailand
The purpose of this chapter is to investigate the long-term growth prospects of
Myanmar economy under such scenarios as intensifying investment and improving total factor productivity (TFP), to represent demand-management policies necessary to sustain its long-term economic growth, to provide strategic implications on t he prerequisites to achieve an optimal growth path, and also to represent the sectoral breakdowns for GDP and labor projections. For these purpose, we construct a simple macro-econometric model as shown in Appendix 1 i n detail, and conduct sectoral breakdowns by the methodology of using Thailand input-output tables as shown in
Appendix 2.1
1. Long-term Growth Prospects
For investigating the long-term growth prospects of Myanmar economy, we look into the supply side of the economy, specifically, production function. This is based on our postulation that in early stage of development Myanmar is facing with “Supply
1 The main outcomes of Myanmar macro-economic projection in this Chapter was described in the Appendix of “Myanmar Comprehensive Development Vision (MCDV)” drafted by “Economic Research Institute for ASEAN and East Asia (ERIA)” – ERIA (2013). The MCDV was submitted to the Ministry of National Planning and Economic Development, Government of Myanmar, as final draft on August 22, 2013.
137
Constraint” as her main feature of Macro-economic structure. As factors of production side, we herein focus mainly on t he contributions of capital stocks and “total factor productivity” (TFP), since these are scarce factors in Myanmar’s economy, and are related with such strategies as export-oriented growth, human resource development
(HRD) and infrastructure deployment. For the capital formation, intensive investment is needed during a certain period from the quantitative perspective of the economy. At the same time, the TFP, which should be treated as policy target in Myanmar economic development in coming decades, should also be improved to pursue the efficiency of the economy from its qualitative perspective. Thus, we represent the following two scenarios: intensifying investment and enhancing TFP.
Scenarios for Intensifying Investment
The “investment” is a key economic variable in capital accumulation, regardless of the investors, i.e. private or public sectors. The investment is one of the supply-side factors as well as one of demand components. The investment flows create capital stocks, one of the production factors, in dynamic terms. Thus, the intensive investment contributes to the increase in the production capacities in the long run. Since the investment is usually equipped with technology, it also contributes to the enhancement of productivity growth.
The economic growth, therefore, requires the investment in any economies.
Myanmar is not an exception. If Myanmar is going to attain export-oriented growth, in particular, it needs certain production capacities to export, thereby necessitating the intensive investment. When we see the economies of forerunners in the Mekong region,
138 i.e., Thailand and Vietnam (see Figure 1), their intensive investments have led to their export-oriented economic structures as well as their high growth. Thailand experienced the periods with its intensive investment in the 1980-90s before the 1997 financial crisis, in which the investment ratio relative to GDP reached around 40-50 percent. Thanks to its intensive investment, Thailand could attain high growth with around 10 percent as annual rate about the year of 1990, a nd raise its export ratio rapidly from around 40 percent in the 1990s to 70-80 percent in the 2000s, though its investment ratio has slowed down toward 20-30 percent after the financial crisis. Vietnam has also continued to raise its investment ratio beyond 30 percent in the 2000s, and in the parallel way it has recorded the rapid increase of its export ratio beyond 80 p ercent and growth rate around 7 percent in the 2000s. The Myanmar’s investment ratio to GDP with around 30 percent, and its export rate with around 15 percent and its growth rate with around 7 percent2 in 2012 ha ve been still lower than those of the forerunners: Thailand in the
1990s and Vietnam in the 2000s. Thus, it appears to be an appropriate time for
Myanmar to facilitate its investment intensively to attain its export-oriented growth.3
Then, we herein estimate the long-term growth prospect under the scenario with intensive investment. For this purpose, we let the variable of “investment” an exogenous one instead of an endogenous one in the usual model. This specification enables us to get the implication on t he linkage between investment and GDP - how
2 The growth rate in Myanmar here is based on the data of UNDB and IMF, not Myanmar’s official data (Central Statistic Organization). The latter data are considered to be overestimated as we state in Appendix 1. 3 The reasons why we think that Myanmar’s economy will follow the economic paths of Thailand and Vietnam are: 1) three economies have similarities in population size as well as cultural and ethnic backgrounds, and 2) the penetration of international production network among three economies, which we suppose, may make their economic growth paths common among the economies.
139 much investment should be created for capital accumulation as a p olicy target for long-term economic growth and development in Myanmar -, although this specification may ignore the endogenous mechanism in which GDP and other economic variables also affect the level of investment. Another practical reason is that there is no such classified data in Myanmar statistics as “private investment” that is usually endogenous, and “government investment” that is exogenous.
Our assumption of “intensive investment” is that Myanmar will nearly follow the past experience of Thailand on the path of investment-GDP ratio. To be specific,
Myanmar will raise the ratio from about 30% to 40% during the upcoming ten years toward 2020 and will let it peak out thereafter (Scenario I), just as Thailand had made the ratio grow from about 30% to 45-50% in about ten-year period before the financial crisis in 1997 ( see Figure 2).4 To clarify its impacts on e conomic growth, we also estimate the benchmark with constant investment-GDP ratio as a “Baseline” scenario.5
Table 1 reports the estimation outcomes: GDP per capita in 2035 will reach 2,236
USD in Scenario I, whereas it will stay at 1,533 USD under Baseline (the trends in GDP per capita and growth rate are shown in Figure 3); growth rate will attain 6.3 % with the contributions of capital 2.4 and TFP 2.7 in Scenario I, while it will only be 4.7 % with the contributions of capital 1.8 and TFP 1.7 in Baseline; incremental capital-output ratio
4 We assume rather gradual slope as Myanmar’s investment-GDP ratio for 2011-35 compared with its slope in Thailand for 1981-2005, while making its 25-year averages the same at 33% between Myanmar and Thailand. It is because we suppose that Myanmar should not repeat the boom–bust cycle in the Thai-1997-crisis. We did not consider the case of Vietnam, since her investment and growth appear to have not reached a steady state yet. 5 As for labor forces, we suppose 2.3% annual growth in 2011-20, and 1.3% in 2021-35 as a common assumption for Baseline, Scenario I and II. The decline of its growth is based on World Population Prospects: The 2010 Revision by Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat.
140
(ICOR) will be getting worse at 3.6 in Scenario I than at 3.3 in Baseline.6
Regarding with the level of TFP, 2.7 i n Scenario I, it is comparable with those of
Thailand and Hong Kong during the two decades of 1970-1990 (see Figure 4). The worse ICOR in Scenario I implies that the volume effects of investment would be limited to enhance the growth and the improvement of investment quality would be needed. This consideration will be covered in the next Scenario II with TFP shift.
Scenarios for Enhancing TFP
The long-term growth prospects are closely linked with any of development strategies as long as those enhance the efficiency of the economy. The innovation of technologies, infrastructure development, institutional reformation, consolidated governance, industrial restructuring, human resource development, and any other strategies would contribute to brushing up the potential growth of economy, and all the factors could be reflected in the enhancement of total factor productivity, TFP. The TFP issue had ever attracted much attention in Asia by the argument of Krugman (1994) that there is nothing miraculous about the successes of Asia's tigers that grew by increasing only inputs, not TFP. As is reported in Figure 4, however, the contribution of TFP to long-term economic growth should not be ignored and would often be linked with economic performances in each country.
We then add one more scenario that includes the improvement of TFP. Specifically, we assume that Myanmar could shift the production function upward without any-input
6 There are many studies on ICOR, e.g. Patel (1968) and Sato (1971). In these studies, the general practice has been to use a fairly fixed ICOR, usually varying from 3 and 4, and relate it to a given, or a desirable, rate of growth.
141 increases during the estimation period of 2011-2035 while keeping the trends in investment-GDP ratio under Scenario I (we call it Scenario II).
Table 1 again reports the estimation outcomes: GDP per capita in 2035 will reach
3,037 USD in Scenario II, which is beyond the level of Scenario I (the trend can also be confirmed in Figure 3); growth rate will further attain 7.6 % with the contributions of capital 2.8 and TFP 3.7 in Scenario II; ICOR will improve toward 3.2 from 3.6 i n
Scenario I.
The level of TFP, 3.7 in Scenario II appears to be rather higher than the average in
Asian economies, but still lower than the level of recent China in 1990-2010. It should also be noted that the ICOR under this Scenario is recovered, thereby implying the improvement of investment quality as well as its quantity.
2. Demand-Management for Sustainable Growth
Suppose that a robust growth were attained in the long-run under the favorable
Scenario II, the question is whether the growth is consistent with the macro-balance of
Myanmar economy. In other words, the problem is whether or not the necessary
“intensive investment” will really be financed by financial resources in the sustainable ways. Here comes the necessity to carefully consider the demand side of the economy, especially external balance (gap between exports and imports), which is equivalent to the saving-investment gap. In this context, the controllability of money supply and price stability are key factors to manage the macro-balance, although these seem to be within the issues with short-run perspectives.
142
We now assume the two Sub-Scenarios under Scenario II in the previous section: the
Sub-Scenario A assumes the 13 % annual growth of money supply, M1 during 2011-35, and Sub-Scenario B presumes its 20% growth. The world GDP volume is supposed to be 2.0% annual growth and the exchange rate is fixed at 802.9 K yat per USD as common assumptions for Sub-Scenario A and B.
Table 2 reports main estimation outcomes as follows: CPI average percentage increase per year is 5.4 % in Sub-Scenario A, while it amounts to 11.4 % in
Sub-Scenario B; export annual growth 16.5 is exceeding that of imports 15.4, thereby trade balance being a surplus at the latter estimation period in Sub-Scenario A, whereas import growth is far exceeding that of export, thereby trade deficit being enlarged extremely in Sub-Scenario B.
The important implication is that too high growth of money supply under
Sub-Scenario B would bring about serious repercussions: first, it would lead to two-digit inflation, which itself might threaten people’s lives, and would give negative impacts on people’s incentives for savings; second, what is the more serious would be that high domestic inflation would make price competitiveness to the world competitors lessen so that the exports grow less and the imports grow more. After all, trade balance would deteriorate, and as its identity relationship, the saving-investment gap would be getting worse. Thus, though there was the need for intensive investment under Scenario
II, it might not be financed by domestic financial resources.
On the other hand, a proper management of money supply would cause less inflation under Sub-Scenario A, and could also keep the trade balance surplus through maintaining price competitiveness. As the result, the intensive investment could be
143 financed by domestic savings. As Figure 5 shows, the intensive investment causes trade deficit at the initial stage due to the need for importing capital goods. The investment, however, enhances the capacity of supply to export with time lag, and finally makes the trade balance surplus at the later stage. Under Sub-Scenario A, the export ratio to GDP will reach more than 100 % of GDP, thereby export-oriented growth being attained under the combination of intensive investment and proper management of money supply.
Under the Sub-Scenario, the aggregate demand will also be nearly matching the supply side of GDP, and thus the macro-balance consistency will hold under this scenario.7
3. Prerequisites to achieve an optimal growth path
We now provide several strategic implications on t he prerequisites to achieve an optimal growth path based on t he model estimation above. The key macroeconomic directions can be focused on t he following two: to intensify investment with careful demand management, and to improve TFP.
Regarding with the direction to intensify investment, the most important prerequisite is to secure financial resources for them in sustainable way. The financial resources come from domestic savings and external capital inflows. The major resources should be domestic savings rather than external finances since the large gap between investment and saving cannot be sustained for a long-term. To enhance the domestic savings, macro-economic stability is definitely needed, since high inflation discourages
7 The model constructed here is a kind of the two-gap model in the sense that the demand-supply gap is not adjusted by price mechanisms. For the two-gap model, see Chenery and Strout (1966).
144 saving activity and international price competitiveness as we state in the previous section. Thus, careful management of demand, especially in terms of monetary control, should be an essential prerequisite. Another prerequisite for intensive investment is to develop financial frameworks to mobilize domestic savings and to intermediate between domestic saving and investment, e.g. the banking sector, equity and bond markets, etc.
As for the TFP improvements, any kinds of policies to enhance the efficiency of
Myanmar economy can be the prerequisites for its improvements. Major contributions would come from innovation of technologies, infrastructure development, industrial restructuring, institutional reformation (regulatory reforms, privatization, capacity development for public sector, consolidated governance, etc.), human resource development (e.g. by reducing illiteracy, raising school participation rate, promoting
Technical/ Vocational Education/ Training- TVET) etc. The combinations and sequences of these instruments should be carefully designed to enhance the overall TFP.
4. Sectoral Breakdowns for GDP and Labor Projections
Based on the growth prospect in Scenario II of Table 1 in the previous section, we herein conduct sectoral breakdowns for GDP and labor projections. As we can observe from Figure 6, the present share of “industry” to GDP in Myanmar is still lower than those in Thailand and Vietnam, though it is in the process of catching-up, while the share of “agriculture” in Myanmar is still higher than those in Thailand and Vietnam.
For the sectoral breakdowns, the input-output (I-O) table can usually be a u seful instrument. Since the I-O table has not been available yet in Myanmar, the alternative
145 way for the sectoral breakdowns is to use the I-O table of another economy in the time when the industrial structure is similar to that of the present Myanmar. We then choose
Thailand as an analogical economy for Myanmar since the I-O table of Thailand is well-developed in retrospective terms. When we look at Figure 7, the share of “industry” to GDP in Myanmar in 2010 is nearly equivalent to that in Thailand in 1975. We thus apply Thailand I-O Table in 1975 to break down the industry of Myanmar in 2010. As for the projection of Myanmar industry in 2020, we use Thailand I-O Table in 1990, since the linear extension of the GDP share of “industry” in Myanmar towards 2020 is supposed to make it reach its level of Thailand in 1990.8 The sectoral labor projection is derived consistently with the sectoral GDP projection by estimating a fixed GDP-labor coefficient in 2010. The concrete methodology of sectoral breakdowns will be shown in
Appendix 2.
Table 3.1 reports the sectoral breakdowns for GDP projection in terms of the share of
GDP. It tells us that the “agriculture, forestry and fishing” sector indicates its decline from 37.8 pe rcent in 2010 t o 10.3 pe rcent in 2020; the “industry”-sector shows its increase from 24.3 percent to 41.2 percent; and the “service”-sector shows its increase from 37.9 pe rcent to 48.6 pe rcent. Table 3.2 r epresents the sectoral breakdowns for labor projection in terms of million persons. According to the growth prospect in
Scenario II of Table 1, the labor will grow by 2.3 percent annually for 2011-20, which is the same rate as the one during the past ten years. The growth will be declined to 1.3 percent for 2021-35, following the slowdown of population growth projected by the
8 One more reason to use Thailand I-O Table in 1990 for Myanmar industry in 2020 is that in the macro-economic framework in MCDV, it is assumed that the investment-GDP ratio in Myanmar will reach 40 percent in 2020, which is equivalent to its level in Thailand in 1990.
146
United Nations.9 When we focus on the upcoming decade for 2011-20, the labor force will increase from 30.96 million in 2010 to 38.92 million in 2020 by 7.96 million. In the sectoral breakdowns, we should emphasize as follows. First, the “industry”-sector and
“service”-sector show the labor increases by 8.33 and 9.44 million respectively, whereas the “agriculture, forestry and fishing” sector indicates its decline by 9.81 million.
Second, among the “industry”-sector, the “manufacturing” signifies the larger increase by 5.31 million. Third, within the “manufacturing” sector, the “textile” and “machinery” reveal the lager increases by 1.17 and 1.38 million respectively.
9 World Population Prospects: The 2010 Revision, by Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat.
147
Figure 1 Comparison of Investment and Export Shares to GDP, and Growth Rate
Investment Share to GDP (%) Export Share to GDP (%)
Myanmar Thailand Vietnam Myanmar Thailand Vietnam 60.0 90.0
80.0 50.0 70.0
40.0 60.0
50.0 30.0 40.0
20.0 30.0
20.0
10.0 10.0
0.0 0.0 1998 1999 1990 1991 1992 1993 1994 1995 1996 1997 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Growth Rate (%)
Myanmar Thailand Vietnam
15.0
10.0
5.0
0.0 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
-5.0
-10.0
Source: ADB Key Indicators and author's estimate for Myanmar's Export
148
Figure 2 Assumption of Investment Ratio to GDP for Scenario I
Myanmar Average for Myanmar Thailand Average for Thailand 60.0
50.0
40.0
30.0
20.0
10.0
0.0 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 2032 2034
Table 1 Long-term Growth Prospect
Baseline Scenario I Scenario II Assumption
Constant Investment Ratio Raising Investment Ratio Scenatio I + TFP shift Investment (IV) & TFP = 25% (2011-35) = 40% (2020 at its peak) = 0.06 point (2011-35)
Labor Annual Growth: 2.3% (2011-20); 1.3% (2021-35)
Estimation Results GDP per capita ($, 2035) 1,533 2,236 3,037 Annual Growth (%, 2011-35) 4.7 6.3 7.6 C apital Contribution 1.8 2.4 2.8 Labor Contribution 1.2 1.2 1.2 T FP 1.7 2.7 3.7 ICOR 3.3 3.6 3.2
149
Figure 3 GDP per capita and GDP Growth under Baseline, Scenario I and II
GDP per capita (USD) GDP Growth (%) Baseline Scenario I Scenario II Baseline Scenario I Scenario II 3,500 12.0
3,000 10.0
2,500 8.0
2,000
6.0
1,500
4.0 1,000
2.0 500
0 0.0 2030 2031 2032 2033 2034 2035 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2010 2011
Figure 4 TFP Growth in Selected Asia Economies
1990-2010 1970-1990
5.0 4.7
4.0
3.0 2.6 2.6 2.4 1.8 2.0 1.7 1.8 1.7 1.5 1.4 1.5 1.2 1.2 0.9 1.0 0.7 0.5 0.5 0.5 0.5 0.3
0.0 -0.1 ROC India China -1.0 Korea -0.8 Vietnam Malaysia Thailand Indonesia Singapore Philippines Hong KongHong -2.0
Source: APO Productivity Database 2012.01
150
Table 2 Demand Management under Scenario II
Sub-Scenario A Sub-Scenario B Assumption
W orld GDP Volume Annual Growth Rate: 2.0% (2011-35) Exchange Rate Constant at 802.9 Kyat per USD (2011-35)
Estimation Results CPI (%, Annual Rate 2011-35) 5.4 11.4 Exports (%, Annual Rate 2011-35) 16.5 8.3 Imports (%, Annual Rate 2011-35) 15.4 20.2 Trade Balance / GDP (%, 2035) 0.4 -298.2
Figure 5 Trends in Export- and Import- Ratio to GDP in Sub-Scenario A
Exports Imports 140.0
120.0
100.0
80.0
60.0
40.0
20.0
0.0 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035
151
Figure 6 Comparison of Industry- and Agriculture- Share to GDP
"Industry" Share to GDP (%) "Agriculture" Share to GDP (%)
Myanmar Thailand Vietnam Myanmar Thailand Vietnam 45.0 70.0
40.0 60.0
35.0 50.0 30.0
25.0 40.0
20.0 30.0
15.0 20.0 10.0
10.0 5.0
0.0 0.0 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Notes: "Industry" is composed of Mining, Manufacturing, Construction and Electricity-Gas-Water. Source: ADB Key Indicators Figure 7 Industry- Share to GDP in Myanmar and Thailand
% Myanmar Thailand 50.0
45.0 1990 2020 40.0
35.0 1975 2010 30.0
25.0
20.0
15.0
10.0
5.0
0.0 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020
Notes: "Industry" is composed of Mining, Manufacturing, Construction and Electricity-Gas-Water. Source: World Development Indicators, World Bank
152
Table 3.1 Sectoral Breakdowns for GDP Projection (share % of GDP)
% 2010 2020 2020 - 2010 Agriculture, Forestry and Fishing 37.8 10.3 -27.6 Industry 24.3 41.2 16.9 M ining 0.7 1.5 0.8 Manufacturing 18.8 29.4 10.6 Food 7.7 6.9 -0.8 T extile 2.7 4.9 2.2 Paper 1.4 1.9 0.5 Chemical, Petroleum and Rubber 2.7 3.2 0.5 Non Metallic Products 0.5 1.9 1.4 Metal Products 1.1 1.4 0.3 Machinery 1.8 5.9 4.1 O ther Manufacturing 0.9 3.3 2.4 Electricity, Gas and Water 0.3 2.6 2.3 C onstruction 4.5 7.7 3.2 Services 37.9 48.6 10.7 Total 100.0 100.0
Table 3.2 Sectoral Breakdowns for Labor Projection (million persons)
million persons 2010 2020 2020 - 2010 Agriculture, Forestry and Fishing 17.48 7.67 -9.81 Industry 4.68 13.01 8.33 M ining 0.29 1.00 0.71 Manufacturing 3.52 8.83 5.31 Food 1.31 1.90 0.59 T extile 0.61 1.78 1.17 Paper 0.25 0.55 0.31 Chemical, Petroleum and Rubber 0.62 1.20 0.58 Non Metallic Products 0.09 0.52 0.43 Metal Products 0.18 0.38 0.20 Machinery 0.32 1.69 1.38 O ther Manufacturing 0.13 0.79 0.65 Electricity, Gas and Water 0.06 0.93 0.87 C onstruction 0.82 2.25 1.44 Services 8.79 18.23 9.44 Total 30.96 38.92 7.96
153
Appendix 1: Description for Macro-economic Model
This Appendix 1 is for describing the structure of the macro-economic model used for presenting the long-term economic prospect in the text above, which includes the data description and the list of estimated equations.
Data Description
The sample data basically cover the period from 1980 t o 2010 i n terms of annual base. The most important assumption on data selection is that “GDP at constant prices”
(YS) should follow the growth rate estimated by UNDP and IMF (2012), not Central
Statistical Organization (CSO), Government of Myanmar. It is because the growth rates by CSO after 1999 are said to be overestimated. ADB (2012) argues that Myanmar’s official growth figures have been deemed overstated and rather unreliable given the country’s poor statistical capacity and use of outdated methodologies by referring to
Myint (2009). It also points out that various production indicators, presumably correlated with GDP growth, show weaker growth: electricity sales (in kilowatt hours) to households and commercial premises grew on average by 4.5 p ercent per annum during 2002–2009 and cement sales by 1.8 percent per annum during 2004–2009. On the other hand, UNDP and IMF (2012) present their own, rather conservative
GDP-growth estimate: averaging 4.8 percent growth during 2000–2010, although official growth data records 12.1 percent growth during that period.
The data for “investment” (IV) is computed by multiplying YS by investment ratio relative to GDP (IVY). The investment rate (IVY) is estimated by dividing
154
“Expenditure on G DP at constant prices” by “Gross domestic capital formation at constant prices” in ADB Key Indicators with various issues. The data for “export” (EX) and “import” (IM) are estimated originally since their data by CSO are said to be underestimated due to using official (overvalued) exchange rate to convert their US dollar values into local-currency values. First, the local currency values of export and import at constant prices in CSO data are converted into their US dollar values by official exchange rate in International Financial Statistics (IFS) of International
Monetary Fund (IMF). Second, the US dollar values are again converted into their local-currency ones by the market exchange rate instead, which is calculated by using
World Economic Outlook (WEO) Database of IMF with various issues.10 Third, the ratios of export and import relative to GDP are computed by dividing their re-estimated local-currency values by the GDP at constant prices in CSO data. Finally, the data for
EX and IM is computed by multiplying YS by the re-estimated ratios of export and import to GDP. The data for “consumption” is obtained by subtracting IV and EX-IM from YS.
The data for “capital stock” (KR) is estimated following the equation in Appendix 1
-Table 1, in which the depreciation ratio is obtained from its ratio in the 1970s of
Thailand, 4.5 percent. The initial capital stock is estimated under the assumption that the incremental capital output ratio (ICOR) is equal to capital output ratio following the
Harrod-Domar Growth model.11 The average ICOR in Myanmar in the 2000s estimated
10 The market exchange rate can be computed by dividing “Gross domestic product, current prices, National currency” by “Gross domestic product, current prices, U.S. dollars”. 11 See Harrod (1939) and Domar (1946).
155 by CSO data is 1.0, and the initial capital stock is calculated by multiplying GDP in
1981 by the ICOR (1.0).
The data for “labor forces” (LB) and “Money Supply (M1)” (MN) are retrieved from
ADB Key Indicators with various issues, and the data for “Consumer Price Index” (CP) and “World GDP Volume” (WY) come from IFS of IMF. The exchange rate (market rate) is computed by WEO database as shown by Note 8. GDP as aggregate demand
(YD) is the sum of CN, IV and (EX-IM), and the trade balance (CB) is defined as
(EX-IM) divided by YS. GDP per capita (YPC) is calculated by the formula in
Appendix 1 -Table 1, where 741.67 is the GDP per capita (US dollar) in 2010 in WEO database (October, 2012); 20,946 i s GDP at constant prices (billion Kyat) in 2010 in
CSO data; and 30.96 is labor forces (million persons) in 2010 in ADB Key Indicators
2012. TFPS signifies the shift of TFP in production function by 0.06 for the Scenario II during 2011-2035.
The sample data created by the fore-mentioned methods are attached in Appendix 1
-Table 2 for the reference.
Model Description
For the simulation, we construct a rather simple model including ten equations, which are divided into production block and expenditure block. In usual macro-econometric models, the GDP on pr oduction (supply-side) and the GDP on expenditure (demand-side) would be adjusted through price-mechanism; the GDP supply-demand-gap would be reflected in the price-determination, and the price effects caused by the GDP gap would be fed back to the GDP demand through real balance of
156 money supply. In this model, however, such a price mechanism is not incorporated, since the prices like consumer price index have not been sensitive to the GDP gap, and thus the price mechanism has not yet been working well. Then, this model focuses the
GDP determination only on the production side by sacrificing the price-adjustment mechanism on demand-side in the short-run, just because the purpose of simulations lies in showing the long-term growth prospect. Alternatively, the GDP gap can be checked in the ex post sense after the simulation so that we can judge whether the simulated scenario is realistic one or not from the supply-demand perspective. In this sense, this model appears to be a kind of the two-gap model as shown in Note 7. At the stage when the mechanism works, however, that mechanism should be incorporated in the model-building.12
The more detailed description of mode-construction is presented as follows. For the stochastic estimation, the variables take a logarithmic form. The figures in the parentheses in the second line of the estimated equation denote the T-value, and “*”, “**” and “***” shows the probability to reject null-hypothesis on t he existence of each coefficient by 90, 95 and 99 percent, respectively. The “AR” is auto-regressive model, the “RR” is the Adjusted R-squared, the “DW” is the Durbin-Watson statistics, and the
“EP” is the estimation period.
Production Function
12 An advanced macro-model to contain price mechanism has ever been constructed such as Aung (2009).
157 ln(YS/LB)=(0.484+TFPS)+0.347*ln(KR/LB)+0.535*ln(YS(-1)/LB(-1))+0.822*AR(1)
--- (1)
(0.614) ( 2.541)** ( 3.732)*** ( 9.863)***
RR=0.96, DW=2.00, EP=1983-2010
The production function above is a usual Cobb-Douglas type that related output per labor to capital per labor. The coefficient, 0.347, s ignifies the capital’s share whereas
(1-0.347) means the labor’s share. In this equation, LB and TFPS is exogenous while
YS and KR is endogenous.
Capital Stock
KR = (1 - 0.045) * KR(-1) + IV --- (2)
The capital stock is defined by the previous-time one, depreciation ratio and investment.
The depreciation ratio is obtained from its ratio in the 1970s of Thailand, 4.5 percent.
The initial capital stock is calculated by multiplying GDP in 1981 by the ICOR as we stated in the data description. The IV is determined by the next equation (3).
Investment
IV = YS * IVY --- (3)
The investment is defined by multiplying GDP by investment ratio to GDP. The investment ratio is exogenously determined.
GDP per capita
YPC = 741.67 * (YS/ 20,946) / (LB/ 30.96) --- (4)
158
The GDP per capita is calculated by the GDP per capita in 2010 (741.67 U.S. dollar) and the growths of GDP and labor force as we stated in the data description.
Consumption per labor ln(CN/LB) = -0.286 + 1.020 * ln(YS/LB) - 0.024 * ln(CP) + 0.099*AR(1) --- (5)
(-0.285) (6.260)*** ( -1.965)* ( 0.483)
RR=0.76, DW=1.96, EP=1983-2010
The consumption per labor is determined by GDP per labor and Consumer Price Index
(CP). The CP is determined by the equation (8), mainly money supply.
Export ratio to GDP ln(EX/YS)=-33.118+0.525*ln(IVY(-4))+6.449*ln(WY)-1.324*ln(CP/ER)+0.322*AR(1
) (6)
(-44.464)*** (4.853)*** ( 43.400)*** ( -19.899)*** (1.458)
RR=0.99, DW=2.16, EP=1987-2010
The export ratio to GDP is determined by investment ratio (IVY) with four-year lags, world GDP (WY) and real exchange rate (CP/ER). The IVY, WY and ER are exogenously determined and CP is decided by the equation (8).
Import ratio to GDP ln(IM/YS) = -3.247 + 0.735 * ln(IVY(-2)) + 0.734 * ln(CP) + 0.879*AR(1) --- (7)
159
(-2.074)* (2.174)** ( 1.880)* ( 6.461)***
RR=0.95, DW=1.28, EP=1984-2010
The import ratio to GDP is determined by investment ratio (IVY) with two-year lags and Consumer Price Index (CP). The IVY is exogenously determined and CP is decided by the equation (8).
Consumer Price Index ln(CP) = 9.859 + 0.972 * ln(MN/YS) + 0.192*AR(1) --- (8)
(61.665)*** (50.272)*** ( 1.010)
RR=0.99, DW=2.00, EP=1982-2010
The Consumer Price Index is determined by money supply (MN) and GDP, which is the modified formula of “Quantity Theory of Money” under the assumption of constant income velocity. MN is exogenously determined.
GDP as demand aggregation
YD = CN + IV + EX – IM --- (9)
The GDP as demand aggregation is identified as we noted in data description.
Trade balance to GDP
CB = (EX - IM) / YS * 100 --- (10)
The trade balance to GDP is identified as we noted in data description.
160
Appendix 1 -Table 1 Data Description
Variables Description Data Sources Estimated by growth rate of YS endogenous GDP; 2005/6 price; bil. kyats IMF(2012) and UNDP CN endogenous Consumption; 2005/6 price; bil. kyats YS-IV-(EX-IM) IV endogenous Investment; 2005/6 price; bil. kyats IV = YS* IVY IVY exogenous Investment ratio to GDP ADB Key Indicators EX endogenous Export; 2005/6 price; bil. kyats Estimated by authors IM endogenous Import; 2005/6 price; bil. kyats Estimated by authors KR = (1-0.045)*KR-1 + IV KR endogenous Capital Stock; 2005/6 price; mil. kyats Depreciation 4.5%: Thailand (1970s) LB exogenous Labor Force; mil. persons 1981-2010: ADB Key Indicators CP endogenous Consumer Price Index; 2010=100 IFS (IMF) MN exogenous Money Supply M1; 2010=100 ADB Key Indicators WY exogenous World GDP Volume; 2010=100 IFS (IMF) ER exogenous Exchange Rate (market rate); kyat per USD WEO (IMF) YD endogenous GDP as aggregated demand CN+IV+EX-IM CB endogenous Trade Balance (EX-IM)/YS YPC endogenous GDP per capita 741.67 * (YS/ 20,946) / (LB/ 30.96) TFPS exogenous TFP shift by 0.06 (2011-35) for Scenario II
Appendix 1 -Table 2 Sample Data
FY YS CN IV EX IM KR LB CP MN WY ER 1980 7,086 0.6 37.0 6.6 1981 7,086 5,569 1,546 8 37 8,313 14.4 0.6 0.2 37.9 7.3 1982 7,470 5,921 1,583 8 41 9,522 14.9 0.6 0.2 38.1 7.9 1983 7,796 6,463 1,358 9 34 10,451 14.9 0.6 0.2 39.1 8.1 1984 8,180 6,979 1,227 8 33 11,207 15.2 0.7 0.2 41.0 8.6 1985 8,417 7,133 1,307 7 29 12,010 15.5 0.7 0.2 42.6 8.2 1986 8,325 7,260 1,080 8 24 12,550 15.7 0.8 0.3 44.1 7.1 1987 7,992 6,961 1,049 7 24 13,034 15.6 1.0 0.2 45.6 6.5 1988 7,081 6,128 965 8 20 13,412 15.9 1.1 0.3 47.6 6.5 1989 7,343 6,393 958 10 18 13,766 16.2 1.4 0.4 49.3 6.7 1990 7,548 6,455 1,237 100 244 14,383 16.5 1.7 0.6 50.9 58.3 1991 7,495 6,246 1,432 141 324 15,168 17.0 2.2 0.8 52.6 84.0 1992 8,222 6,928 1,451 236 392 15,937 19.0 2.7 1.1 54.8 99.3 1993 8,708 7,475 1,570 325 663 16,790 19.5 3.6 1.4 56.1 119.7 1994 9,300 7,632 1,942 331 605 17,976 20.0 4.4 1.8 58.5 113.2 1995 10,016 7,984 2,512 265 746 19,679 20.5 5.5 2.4 60.6 110.0 1996 10,657 8,374 2,837 407 961 21,631 22.0 6.5 3.2 62.9 159.8 1997 11,264 8,830 3,132 720 1,418 23,789 22.5 8.4 4.2 65.4 240.4 1998 11,918 9,141 3,736 729 1,688 26,455 23.1 12.7 5.4 66.8 249.2 1999 12,513 9,375 4,024 768 1,654 29,288 23.7 15.0 6.7 69.3 258.1 2000 13,289 9,253 4,181 1,378 1,523 32,151 24.3 15.0 9.0 72.6 286.7 2001 13,980 12,520 1,607 2,802 2,949 32,311 24.9 18.2 12.5 74.2 547.8 2002 14,749 11,615 1,666 4,998 3,529 32,523 25.6 28.5 18.0 76.1 829.9 2003 14,749 12,269 1,825 3,162 2,507 32,884 26.4 39.0 21.2 78.7 737.2 2004 15,487 11,701 2,124 4,045 2,384 33,529 26.9 40.7 26.5 82.5 859.2 2005 16,184 11,725 2,537 4,593 2,671 34,557 27.4 44.5 34.8 86.2 1,025.0 2006 17,316 12,777 2,453 6,152 4,065 35,455 28.0 53.4 44.1 90.5 1,162.0 2007 18,269 13,756 2,963 5,804 4,254 36,822 29.3 72.2 57.3 95.0 1,156.3 2008 18,926 16,197 3,254 3,777 4,301 38,419 30.0 91.5 61.0 97.2 917.5 2009 19,892 15,529 4,167 3,431 3,236 40,857 30.5 92.8 75.7 95.8 918.4 2010 20,946 16,533 5,321 3,291 4,199 44,340 31.0 100.0 100.0 100.0 802.9
161
Appendix 2: Methodology of Sectoral Breakdowns for GDP and Labor Projections
This Appendix 2 is for describing the methodology of sectoral breakdowns for GDP and labor projections by using Thailand I-O tables. It takes two steps as follows.
At the first step as shown in Appendix 2 – Table 1, we fix the industrial details in labor force and GDP13 in the benchmark year of 2010 t o obtain the GDP-labor coefficient. For the industrial classification except the details of “manufacturing”, the data are retrieved from those of Central Statistical Organization (CSO), Government of
Myanmar. Regarding with the breakdowns of “manufacturing”, we adapt the sector-share in Thailand I-O Table in 1975, i.e., the share of “value added” for sector-GDP and the share of “wages and salaries” for sector-labor force. We then obtain the GDP-labor coefficient through dividing labor force by GDP in labor force in each sector.
At the second step as shown in Appendix 2 – Table 2, we estimate the industrial
GDP in Myanmar in 2020. We first get the total GDP in 2020 from Scenario II of Table
1, and then divide it by industrial sector share of “value added” in Thailand I-O Table in
1990. The industrial labor force in 2020 is obtained by multiplying industrial GDP by the GDP-labor coefficient at the first step (The number of industrial labor force is controlled by the total of labor force in 2020 in Table 1 assumption).
13 The GDP here is “GDP at constant prices in 2005/6”.
162
Appendix 2 -Table 1 GDP – Labor Coefficient in 2010
2010 Labor. (mil.) GDP (bil.kyat) Coef. (Emp/mil.GDP) Agriculture, Forestry and Fishing 17.48 7,927 2.21 Mining 0.29 151 1.95 Manufacturing 3.52 3,937 Food 1.31 1,615 0.81 T extile 0.61 564 1.08 Paper 0.25 294 0.84 Chemical, Petroleum and Rubber 0.62 565 1.10 Non Metallic Products 0.09 113 0.80 Metal Products 0.18 225 0.81 Machinery 0.32 370 0.85 O ther Manufacturing 0.13 191 0.70 Electricity, Gas and Water 0.06 52 1.06 Construction 0.82 943 0.87 Services 8.79 7,936 1.11 Total 30.96 20,946
Notes: The labor in 2010 is based on CSO and Thai IO 1975 for manufacturing. The GDP in 2010 is based on CSO and Thai IO 1975 for manufacturing.
Appendix 2 -Table 2 Estimate of Labor in 2020 Based on GDP – Labor Coefficient
2020 GDP (bil.kyat) GDP*Coef. Labor (mil.) Agriculture, Forestry and Fishing 5,328 11.75 7.67 Mining 790 1.54 1.00 Manufacturing 15,262 13.51 8.83 Food 3,578 2.91 1.90 T extile 2,523 2.73 1.78 Paper 1,010 0.85 0.55 Chemical, Petroleum and Rubber 1,664 1.84 1.20 Non Metallic Products 999 0.80 0.52 Metal Products 728 0.59 0.38 Machinery 3,037 2.59 1.69 O ther Manufacturing 1,723 1.21 0.79 Electricity, Gas and Water 1,335 1.42 0.93 Construction 3,982 3.45 2.25 Services 25,195 27.91 18.23 Total 51,892 59.59 38.92
Notes: The GDP in 2020 i s based on Scenario II of Table 1 and Thai IO 1990. The labor is controlled by its total of Table 1 assumption, and divided by the share of "GDP*Coef".
163
References:
Asian Development Bank (ADB). 2012. Myanmar in transition: Opportunities and challenges, ADB, Mandaluyong, the Philippines. Aung, S.H. 2009. A Macroeconometric Model of Myanmar, Yangon Institute of Economics Research Journal, Vol.1 No.1, pp. 30-49. Chenery, H.B. and Strout, A.M. 1966. Foreign Assistance and Economic Development, The American Economic Review, Vol. LVI, No. 4, pp. 679-723. Domar, D. 1946. Capital Expansion, Rate of Growth and Employment, Econometrica, Vol. 14, No. 2, pp. 137-147. Economic Research Institute for ASEAN and East Asia (ERIA). 2013. Draft of Myanmar Comprehensive Development Vision (MCDV). Krugman, P. 1994. The Myth of Asia's Miracle, Foreign Affairs, Vol. 73, No. 6, pp. 62-78 International Monetary Fund (IMF) 2012. Article IV Consultation with Myanmar, available at: http://www.imf.org/external/pubs/ft/scr/2012/cr12104.pdf (accessed 19 February 2013). Harrod, R.F. 1939. An Essay in Dynamic Theory, Economic Journal, Vol. 49, No. 193, pp. 14-33. Myint, U. 2009. M yanmar Economy: A Comparative View, Institute for Security and Development Policy, Stockholm. Patel, S. J. 1968. A Note on the Incremental Capital Output Ratio and Rates of Economic Growth in the Developing Countries, Kyklos, Vo l . 21, No 1, pp. 147–150. Sato, K. 1971. International Variations in the Incremental Capital-Output Ratio, Economic Development and Cultural Change, Vol. 19, No. 4, pp. 621-640.
164
Chapter VIII: Progress in Intra-industry Trade in the Greater Mekong Sub-region
Kenji Nozaki, Surugadai University, and Visiting Researcher, ESRI
This chapter addresses the economic linkage among GMS countries. In particular, progress in intra-industry trade was surveyed. Bilateral trade figures by industry are used in both trade figure analysis and gravity model estimation. The author expects that this chapter gives material to discuss the overall economic relation as a zone in
Indochina region.
1. Introduction
Greater Mekong Sub-region (GMS) is composed of such countries as Cambodia,
Lao PDR, Myanmar, Thailand and Vietnam1. Although they are at different stages of development, the region as a whole attracts the investors’ interests by various means.
Firstly, the total number of population, which are as many as 235 million in total, can work for development. It means that there will be a large market in the future.
Secondly, also relating to population, large number of population make it possible to provide labor supply. In particular, the gap of the development stage can be interpreted as possibility of the labor supply for different types of labor. It may make the region possible to achieve vertical division of labor in the manufacturing process. For the time,
Thailand is the most developed country in the region. The per capita GDP of Thailand in
1 Under the Mekong Program, two Chinese provinces, Yunnan and Gwangxi are included. In this paper, five countries mentioned above are studied region.
165
2012 is as much as 5,390 US dollar, while that of Myanmar is as low as 876. It is well known that Thailand has collected many industries. For example, the number of automobile production in these years are over two million. Thirdly, the institutional arrangements are being built in the region. Under the Cross Border Transportation
Arrangement (CBTA), which is led by The Asian Development Bank, many individual arrangements have been exchanged or are under coordination bilaterally. They are promoting intra-regional trades, and make the division of labor easier in terms of cost reducing.
Under such environments, economic linkage in the GMS is strengthening. If we see the intra-regional trade among the GMS counties, the intra-regional trade ratio is increasing, although it is not so high (Table 1). The industrial network is now formulating and can be expected further from now2. The recent events are proving such situation. For example, Toyota-boshoku, which is a provider of automobile parts started to operate in Savannakhet, Lao PDR in 20143.
Under the recognition that economic relation in the GMS is growing, this paper aims to clarify the stages of division of labor in the manufacturing process by industrial sector. The author selects two sectors to analyze comparatively. They are clothing industry and automobile industry. The former is one of the typical labor intensive industries and the latter is composed of multiple process including the high-tech procedure and capital intensive production. The main purpose of this paper is to find the sector specific characteristics. In order to achieve it, bilateral trade data are utilized.
2 Nozaki (2014) pointed the possibility with general survey using the trade data of Thailand. 3 According to the press-release of Toyota-boshoku, it started to operate a satellite plant to provide parts for mother plants in Thailand.
166
Concretely, analysis by gravity model, together with deep consideration of trade balance by item, are adopted.
In the next section, the author confirms the methods of analysis. Literature survey indicates the framework of analysis. Intra-regional trades by individual item are surveyed in the section 3, a nd the gravity models both ordinary model and fragmentation model are estimated in the section 4. These two sections will clarify the characteristics of division of labor in the GMS. Finally, the section 5 is conclusion.
2. Literature Survey
To measure the progress of intra-industry trade, comparing the mutual exports is a usual way. Among them, Grubel and Lloyd (1971) introduced an index (GL) expressed
( ) | | as following; = 𝑋𝑋𝑖𝑖+𝑀𝑀𝑖𝑖 − 𝑋𝑋𝑖𝑖−𝑀𝑀𝑖𝑖 𝑖𝑖 Where Xi is export𝐺𝐺𝐺𝐺 of a 𝑋𝑋country𝑖𝑖+𝑀𝑀𝑖𝑖 to partner country, and Mi is import from partner country (equal to export of partner country to that country), for item i respectively. This
GL index goes from 0 to 1, a nd when the index is close to 1, i ntra-industry trade is active, while it is inactive when the index is close to 0. This index has been widely used in many empirical studies. However, it has structural shortage, which Greenaway and
Milner (1983) pointed that the influence of categorical aggregation affects the GL index figures. If we can eliminate the influence, the high value of GL index may mean that division of labor in the manufacturing process is taking place. Then the next point is whether such division of labor is vertical or horizontal. One answer is proposed by
Greenaway et al. (1995). When we discuss the type of intra-industry trade, using the GL index, additional information of relative unit value is helpful to distinguish the vertical
167 division of labor from the horizontal division of labor. That is, if the ratio of unit values between export and import is similar, the trades can be understood as horizontal division of labor. On the contrary, if the ratio is different, the trades can be vertical. Greenaway et al. (1995) proposed the threshold line of the unit value gap as plus or minus 15%, which gives a wedge of 30%. However, with the imperfect information, the threshold is widened to plus or minus 25%, which gives a wedge of 50%.
As for the application of the gravity model to trade theory analysis, Anderson
(1979) found that gravity model is consistent with various trade theories. Since then, many empirical studies and adjustments of the model have been conducted. Among them, Bergstrand (1989) is to be mentioned. It developed the gravity model by including per capita GDP for both exporters and importers as additional parameters for regression. The per capita GDP is understood by two meanings. For exporting countries, it expresses capital and labor endowment ratio of trading goods, while it is an indicator luxuries and necessities ratio for importing goods. Gravity models are applied for analysis of the industrial fragmentation. Jones and Kierzkowski (1990) proposed such idea in studying the cross-border production sharing between the United States and
Mexico. Kimura et al. (2007) observed the international production networks in East
Asia as vertical division of labor. Such vertical division of labor follows fragmentation theory. Recently, Taguchi and Lar (2014) examined the bilateral trades of machinery parts and components between Thailand and other Mekong countries by gravity model.
It revealed that trades between Thailand and Vietnam is two-way integration and it is explained by fragmentation factors.
168
3. Analysis of Trade Data
In this section, the author calculated the GL indexes in two sectors, clothing industry and automobile industry, between two countries among the GMS, following the basic idea of Grubel and Lloyd (1971). Then the unit value comparison is conducted in detail. Clothing industry, which is considered to be labor intensive, is widespread in the region. On the other hand, the automobile industry, which has many production processes with capital intensive and high-tech processes, has been developed in
Thailand, while some parts manufactures are located in neighboring countries.
For trade data analysis, the author employs the UN-Comtrade database.
Unfortunately, due to some data constraints, this analysis is conducted under specific treatments. Although the Greenaway et al. (1995) proposed trade data to use one country’s exports (f.o.b) and imports (c.i.f) for analysis, the author thinks that mutual exports data should be used, because country A’s import data from country B is generally larger than country B’s export data to country A, due to the additional costs of c.i.f. Then the data should be unified to exports data. However, due to lack of sufficient information, some data were substituted by import data. Concretely, they are exports from Lao PDR and Myanmar4 . Thus, analysis in this section and next section is conducted by exports from Cambodia, Thailand and Vietnam and imports from Lao
PDR and Myanmar.
3.1 Intra-regional Trade and Grubel Lloyd Index
4 As for reporting countries, almost all figures of Lao PDR and many of Myanmar are lacked, while other countries’ imports data exist.
169
In this sub-section, after overviewing the intra-regional trade matrix in 20105, GL indexes are calculated for both clothing and automobile industry.
As for the clothing industry, trade data are of HS 2-digit HS61 and HS626. Table 2 shows that mutual trades exist in the GMS countries. Among them, export amounts from Thailand are larger than other countries’ exports to Thailand. At the same time, exports from Vietnam is larger than imports except for trade with Thailand. However, as exports to Thailand and Vietnam are not small, mutual trades are formulated. As a result,
GL indexes are somehow high among some countries. We can find the GL index of
0.396 between Cambodia and Lao PDR, 0.642 between Lao PDR and Vietnam, 0.567 between Thailand and Vietnam and so on. We can say that production in this sector is not dominated by Thailand, but each member country exports each other.
On the contrary, intra-regional trades of automobile industry, whose HS code is
HS87 appear in different features in the same year 2010 (Table 3). Export from Thailand accounted for as much as 89.4% of intra-regional trades. Mutual trade is not yet activated in the overall region. However, Vietnam is following Thailand to keep mutual trade between these two countries. When we see the GL index, figures between
Thailand and Vietnam (0.256) and between Cambodia and Vietnam (0.545) are observed notably. Comparing to clothing industry, in which mutual trades among the
GMS is satisfied, automobile industry is in a primitive stage in terms of intra-industry trade, although some signs of intra-industry trade are observed.
5 As the latest figures are not fully reported to the United Nations, the author selected the survey year in 2010 to balance the newness and accuracy. 6 HS is abbreviation of Harmonized System. Data collected in this paper is “HS as reported”.
170
3.2 Comparison of Unit Value
The outline of the intra-industry trades became cleared in both clothing and automobile industries. The next analysis is to divide these intra-industry trades into horizontal and vertical division of labor by observing the unit value. In this analysis, trades between Thailand and Vietnam, Thailand and Cambodia are investigated in clothing industry, while trades between Thailand and Vietnam are in automobile industry, as their intra-industry trade ratios are relatively high. In order to find unit value, we need more detailed classification, so that HS 6-digit figures are adopted.
As the clothing industry has many items, the author selected some items. They are shirt, coat and trousers. Among them, items which mutual trade is recorded are picked up and both GL index and unit value ratio are calculated. As for the unit value ratio, unit value of Vietnam or Cambodia to Thailand is listed. Table 4 is the calculation results of the trade between Thailand and Vietnam, and Table 5 is between Thailand and
Cambodia.
Although the amount of trade values are not so large, due to detailed division of items, some items are mutually traded. With trade between Vietnam and Thailand, we find 10 items whose GL index is more than 0.3. The unit value ratio of 4 items is close7, while the ratio of the rest of 6 i tems is different. The former items are HS610510,
HS610520, HS610610 and HS610462, while the latter items are HS610220, HS610230,
HS610342, HS610343, HS610349 and HS610463. The references of HS codes and the items are on the Table 6. As for the trade between Cambodia and Thailand, we can find
7 Following the definition of Greenaway et al. (1995), unit value ratio between 0.75 and 1.25 is onsidered to be close.
171
4 items whose GL index is more than 0.3, and unit value ratio of all item is different.
They are HS610190, HS610422, HS610442 and HS610459.
From these results, intra-industry trades in this sector has some points. Firstly, even though both Vietnam and Cambodia produce the mutual trading goods with Thailand, the kinds of items are different. Furthermore, intra-industry trades between Cambodia and Thailand are considered to be vertical division of labor, as unit value ratio is different. On the contrary, intra-trades between Vietnam and Thailand can be divided into two gropes, one grope is vertical as the unit value ratio is different, and the other is horizontal as the ratio is similar. In other words, Cambodia, whose per capita income is relatively lower than Vietnam, is developing clothing industry to be able to increase the intra-industry trade with Thailand, but it is limited by vertical division of labor. It may come from the wage difference. On the other hand, clothing industry in Vietnam still produces some items to be vertical division of labor, but at the same time, some items are for horizontal division of labor. It may support the idea that clothing industry in
Vietnam is more advanced than that in Cambodia.
As for the automobile industry, if we investigate HS 6-digit figures, mutual trade of finished vehicle has not occurred, only parts and accessories mainly between Vietnam and Thailand are mutually traded. Among them, items whose GL indexes are more than
0.3 are HS870893 and HS870895, while HS870840, HS870870 and HS870899 are scored their GL indexes more than 0.2 (Table 7). The references of the HS codes and items are on the Table 8. We can find that all of such items are vertical division of labor in terms of understanding from unit value difference, with no horizontal division of labor. The characteristics in trading of such items is unit value of Vietnamese exports is
172 higher than that of Thai exports. It may come from the following situation. Plants in
Thailand used to operate integrated production to assembly from parts production. In recent years, as firms in Vietnam became to be able to machine the parts, Thai firms started to entrust the Vietnamese firm to finalize the parts from intermediate goods from
Thailand. Even in that case, final assembly is conducted in Thailand, and the final cars should be sold in Thailand or export to other countries. If it happens, unit trade value should be higher in Vietnamese export, as final goods are not re-exported to Vietnam. It is difficult to say firmly, but there is possibility for such case to be taking place.
3.3 Consideration from the Trade Data Analysis
There is a clear difference between clothing industry and automobile industry. As clothing industry is labor intensive industry, less developed members in the GMS could grow the firms and export products each other, although production in Thailand is still strong in the region. Among them, production in Vietnam gives us some signs to grow the industry to partly horizontal division of labor which may mean that the quality of the products is high. Cambodia follows the Vietnam, but its production is still at a stage of vertical division of labor. Unfortunately, production in Lao PDR and Myanmar is not yet the stage of division of labor with Thailand.
Although Thailand is one of the major countries to export automobiles, the spillover effect is still in a primitive stage. Almost no mutual export of assembled cars expresses the situation. Even in parts and accessory products, the intra-industry trade is very limited. It is because automobile industry requests very precise machining and needs capital intensive technology. However, we can find some signs for Vietnam to
173 supply parts to Thailand. Together with the some horizontal division of labor in the clothing sector, quality of Vietnamese industry is well developed to some extent.
Intra-industry trade in these two sectors shows us that Vietnam, although the gap is still large, is catching up with Thailand in manufacturing. Then in the next section the author tries to find how the gravity model draw the situation.
4. Estimation of Gravity Equations
In this section, the author tries to clarify the difference of types of division of labor in the GMS between clothing industry and automobile industry. In particular, we expect the features of both horizontal division of labor, which can be proved by ordinary gravity model including the per capita income, and vertical division of labor, which can be led by gravity model for fragmentation, in the clothing sector. As for the automobile sector, we expect only the vertical division of labor. Thus the author applied two types of model for both sectors.
4.1 Equations for Estimation
The first gravity model is an ordinary gravity trade model. It includes the GDP of each member country to express the size of market, per capita GDP to express the type of industry (labor intensive or capital intensive) for exporter and the type of consumption for importer, and the distance. In addition to them, as Anderson and van
Wincoop (2003) pointed, we should control for multilateral resistance terms. In this paper, following the Vandenbussche and Zanardi (2010) and its application of Taguchi and Lar (2014), the author introduced the bilateral real exchange rate. The equation is as
174 follows:
ln(Exportijt) = c1 +α1 ln(Joint GDPijt) +β1 ln(Joint GDP per capitaijt)
+γ1 ln(Distanceij)+δ1 ln(REXijt) … (Equation 1)
where the subscript i and j denote the exporting country and importing country, respectively, and the t denotes the year. Exportijt is exports of clothing or automobile from country i to j in the year t. The exports here are HS 2-digit, HS61-62 for clothing and HS87 for automobile. Joint GDPijt represents a product of GDP, Joint GDP per capitaijt is a product of GDP per capita in country i and j in year t, respectively.
Distanceij is the geographical distance between the capital cities of country i and j.
REXijt is the real exchange rate in the bilateral term between country i and j in year t8. α,
β, γ and δ are coefficients to be estimated.
The second gravity equation, following Kimura et al. (2007) and Taguchi and Lar
(2014), is designed to identify the fragmentation factor. The characteristics of this equation is to introduce the gap in GDP per capita between two trade partners, which is denoted by GAPijt. The equation is as follows:
ln(Exportijt) = c2 +α2 ln(Joint GDPijt) +β2 ln(GAPijt)
+γ2 ln(Distanceij)+δ2 ln(REXijt) … (Equation 2)
In order to calculate in a style of logarithm, GAPijt should be positive scale. Thus, the GAPijt is expressed by square of the gap between country i and country j in year t.
As for the estimation methodology, the author uses a Tobit model, as some samples of trade data are observed at zero values.
8 The author defines the real exchange rate, as for example, real exchange rate between Thailand and Vietnam is expressed by (Vietnamese Dong per Thai Baht)*(CPI in Vietnam)/(CPI in Thailand).
175
4.2 Empirical Results of Estimation
In estimating of the equations, the author adopted the data as follows. Firstly, exports are from UN-Comtrade database HS 2-digid (HS61 and HS62 for clothing industry and HS87 for automobile industry), and same as Section 3, exports from Lao
PDR and Myanmar are substituted by imports of partner countries, and trades between
Lao PDR and Myanmar are omitted. GDP and GDP per capita are from the database of
World Economic Outlook of the IMF. Nominal exchange rates and CPIs for real exchange rates are from Key Indicators 2014 of ADB. For calculation of the distance, the author used the website available to measure the distance between two cities9.
As for the period of dataset is from 2003 to 2012. In order to maintain the freedom of the estimation, the period of dataset should be longer. However, as the intra-industry trades are developing in recent years, too old information should be eliminated. When the bilateral trades between Thailand and Vietnam for clothing and automobile are investigated historically, the author found that very few mutual trades were conducted before 2002 in automobile industry10.
The estimation results of both industries are shown on Table 9 and Table 10. Firstly, the result of ordinary gravity trade model (Equation 1) for clothing industry is significant. The coefficient of joint GDP is significantly positive and that of distance is negatively significant, which supports the basic idea of gravity model. Furthermore, the negative significance of coefficient of joint GDP per capita means that the clothing
9 The website is http://www.distancebetweencities.us/. 10 As for the clothing industry, mutual trades between Thailand and Vietnam before 2002 were already active. For example, GL index in 2000 was 0.38. On the other hand, GL index in 2000 was 0.002, started to increase in 2003 of 0.05.
176 industry in the region is labor intensive and such products are not luxury items.
Secondly, the estimation of fragmentation model (Equation 2) for clothing industry is also significant. Together with positive significance of the coefficient of joint GDP and negative significance of distance, the coefficient of gap of GDP per capita is positively significant. It is with the author’s expectation that the gap of the wages facilitates vertical division of labor in production.
On the other hand, the results of the estimation for automobile industry are different.
The ordinary gravity trade model is insignificant, while the fragmentation model is significant. As for the ordinary gravity trade model, insignificance of coefficient of joint
GDP per capita may mean that this industry is not labor intensive in production. On the contrary, the coefficient of the gap of GDP per capita is positively significant in the fragmentation model together with the positive significance of joint GDP and negative significance of distance. This means that the gap of the wages facilitates vertical division of labor in production.
4.3 Consideration from Estimation
The results of gravity model estimation are consistent with the analysis in the
Section 3 f or both clothing industry and automobile industry. As for the clothing industry, the outcome in trade data analysis suggests that existence of both horizontal and vertical division of labor in production. The significance of the ordinary gravity trade model will support the former, and the significance of the gravity model will support the latter. Although the development stages are different among the GMS members, clothing industry have been developed in the region as it is labor intensive
177 industry. In some items, lower income members have comparative advantage, mutual trade, which follows the market size and other factors, is progressing. In some other items, production process is divided with the difference of labor cost.
Different from clothing industry, the automobile industry has not been developed in the region as a whole, although Thailand is one of the major exporters to the global market. We should understand that Vietnam has just started to follow Thailand, and other members are still at a primitive stage in automobile production. Thus, mutual trades have not been widespread in the region. In such situation, we cannot expect horizontal division of labor in this sector yet, and neither analysis of trade figures nor gravity model estimation support such idea. However, raising mutual trades of some items between Thailand and Vietnam can be explained by vertical division of labor.
5. Conclusion
In this paper, study for clarifying of the structure of intra-industry trades in the
GMS by specific sectors is conducted, under the situation of increase of the intra-regional trades. Different types of production network are suggested through analysis of detailed trade information. Firstly, production of clothing industry, which is considered to be labor intensive industry, has several stages in division of labor.
Horizontal division of labor in some items is observed among some member countries in the GMS, and some items are considered to be vertically divided in producing between Thailand and Vietnam. As the production process is not so complicated, lower income countries can maintain the production process by themselves. At the same time, in order to produce more value added items, vertical division of labor is effective, and
178 we can observe some signs to start such production.
Meanwhile, automobile industry, which is considered to have multi-processed production, is difficult to formulate the horizontal division of labor as lower income countries cannot have such facilities as Thailand, which need high-technology and enough capital. Under such situation, horizontal division of labor where final automobiles are mutually exported is far from practical. However, rapid industrialization in Vietnam makes the country able to join the production network. This kind of network will formulate the vertical division of labor.
It is possible to say that the economic linkage is strengthening in the GMS. We can understand that some kinds of division of labor in production process is formulating.
One of the contribution of this chapter is to clarify the current situation in the GMS.
Of course there are some points to be improved in the analysis of the outcome of this study. For example, this study is conducted to select only two sectors. Almost all the industries are operating in Thailand, which means many sectors are formulating production network in the GMS11. Furthermore, in estimating the fragmentation model, logistic conditions should be included. As the improvement of hard and soft infrastructure in the region, the environment for production network is changing rapidly.
They are the future issues to be considered.
11 Recently, Nikon Thailand, which is a manufacturer of camera, has set up a satellite plant in Lao PDR in 2013. It assembles the parts of camera to supply mother plant in Thailand.
179
Table 1: Intra-regional Trade: Exports from GMS 5 Nations (Unit: %) GMS5 ASEAN Japan USA World 1995 2.7 21.7 16.8 17.9 100 2000 3.2 18.9 15.0 19.1 100 2005 4.3 20.5 13.3 16.9 100 2010 6.9 21.7 10.4 13.3 100 2012 6.7 21.4 10.4 12.7 100 Source UN Comtrade
Table 2: Intra-regional Trade and GL Index of Clothing in 2010 (a) Intra-regional trade (Unit: USD) From To CambodiaTo Lao PDR To Myanmar To Thailand To Vietnam Cambodia - 19,004 0 546,802 381,657 Lao PDR 4,688 - 0 3,671,942 933,680 Myanmar 0 0 - 821,776 2,500 Thailand 3,023,811 8,238,883 21,008,700 - 9,663,239 Vietnam 1,684,481 1,973,776 14,345 3,824,296 -
(b) GL index Lao PDR Myanmar Thailand Vienam Cambodia 0.396 n.a. 0.306 0.369 Lao.PDR n.a. 0.617 0.642 Myanmar 0.075 0.297 Thailand 0.567 Source UN Comtrade Note Trade data of clothing is HS61 and HS 62.
180
Table 3: Intra-regional Trade and GL Index of Automobile in 2010 (a) Intra-regional trade (Unit: USD) From To Cambodia To Lao PDR To Myanmar To Thailand To Vietnam Cambodia - 0 0 5,263,405 7,047,076 Lao PDR 17,190 - 0 7,714,103 163,740 Myanmar 0 0 - 58,844 0 Thailand 148,154,567 271,138,744 107,468,177 - 379,059,601 Vietnam 18,816,873 12,645,583 412,897 55,634,085 -
(b) GL index Lao PDR Myanmar Thailand Vienam Cambodia 0.000 n.a. 0.069 0.545 Lao.PDR n.a. 0.055 0.026 Myanmar 0.001 0.000 Thailand 0.256 Source UN Comtrade Note Trade data of automobile is HS87.
Table 4: Grubel and Lloyd Index and Unit Value Comparison (Thailand and Vietnam: Clothing, 2010) Thai Export Vietnamese HS Code GL Index Unit Value Ratio (USD) Export (USD) (Vietnam/Thailand) 610510 36,823 7,785 0.349 0.922 610520 92,155 179,693 0.678 1.001 610610 7,238 2,253 0.475 1.193 620520 1,292,959 50,339 0.075 0.969 610120 1,485 13,554 0.197 13.367 610130 472 17,742 0.052 0.583 610190 3 8,488 0.001 565.867 610220 2,207 5,956 0.541 7.394 610230 8,056 11,272 0.834 1.774 610342 1,837 2,780 0.796 4.448 610343 25,635 17,610 0.814 1.609 610349 5,746 32,031 0.304 4.248 610432 588 5,887 0.182 1.130 610442 19,066 1,489 0.145 0.050 610459 81 5,116 0.031 0.748 610462 8,984 14,093 0.779 0.834 610463 8,122 19,488 0.588 1.871 Source UN Comtrade
181
Table 5: Grubel and Lloyd Index and Unit Value Comparison (Thailand and Cambodia: Clothing, 2010) Thai Export Cambodian HS Code GL Index Unit Value Ratio (USD) Export (USD) (Cambodia/Thailand) 610510 50,869 8,645 0.291 1.569 610190 864 300 0.515 5.760 610342 83,077 14,244 0.293 0.270 610349 72,604 2,994 0.079 0.395 610422 2,106 9,427 0.365 0.409 610442 6,417 4,077 0.777 0.422 610459 4,956 3,411 0.815 20.856 610462 2,934 44,443 0.124 5.902 Source UN Comtrade
Table 6: HS Code and Trade Item (Clothing) HS Code Item 610510 Men's/boys' shirts, knitted/crocheted, of cotton 610520 Men's/boys' shirts, knitted/crocheted, of man-made fibres 610610 Women's/girls' blouses, shirts & shirt-blouses, knitted/crocheted, of cotton 620520 Men's/boys' shirts (excl. knitted/crocheted), of cotton 610120 Men's overcoats and similar articles, knitted of cotton 610130 Men's overcoats and similar articles, knitted of man-made fibres 610190 Men's overcoats and similar articles, knitted of other textile material 610220 Women's overcoats and similar articles, knitted of cotton 610230 Women's overcoats and similar articles, knitted of of man-made fibres 610342 Men's trousers knitted of cotton 610343 Men's trousers knitted of synthetic fibres 610349 Men's trousers knitted of other textile materials 610432 Women's jackets and blazers, knitted of cotton 610442 Women's dresses, knitted of cotton 610459 Women's' skirts and divided skirts, knitted of textile materials other than wool fibres 610462 Women's trousers, knitted of cotton 610463 Women's trousers knitted of synthetic fibres Source UN Comtrade
182
Table 7: Grubel and Lloyd Index and Unit Value Comparison (Thailand and Vietnam: Automobile, 2010) Thai Export Vietnamese HS Code GL Index Unit Value Ratio (USD) Export (USD) (Vietnam/Thailand) 870810 2,730,994 43,552 0.031 0.804 870829 5,535,846 21,297 0.008 0.967 870840 112,927 914,245 0.220 4.616 870850 1,182,984 2,022 0.003 1.199 870870 5,558,816 799,936 0.252 0.498 870892 9,243,051 5,011 0.001 0.537 870893 719,473 777,587 0.961 0.706 870894 13,627,170 79,248 0.012 1.542 870895 1,469,677 540,816 0.538 1.270 870899 102,491,291 11,358,238 0.200 1.303 Source UN Comtrade
Table 8: HS Code and Trade Item (Automobile) HS Code Item 870810 Bumpers & parts 870829 Parts & accessories of bodies 870840 Gear boxes and parts 870850 Drive-axles 870870 Road wheels and parts and accessories 870892 Silencers (mufflers) and exhaust pipes 870893 Clutches and parts 870894 Steering wheels 870895 Safety airbags with inflater system 870899 Other parts and accessories Source UN Comtrade
183
Table 9: Gravity Model Estimation on GMS Member Countries' Exports of Clothing
Dependent variables GMS member's exports: Exp it Equation (1) Equation (2) Const. 64.365 *** 7.167 (13.337) (6.991) Joint GDP: ln(GDP X*GDP M ) 3.048 *** 0.737 ** (0.372) (0.312) Joint GDP per capita: ln(GDP X /P X*GDP M /P M) -1.614 *** (0.605) 2 Gap of GDP per capita: ln((GDP X /P X-GDP M /P M) ) 1.018 *** (0.195) Distance: ln(D ) -8.070 *** -2.305 ** (1.280) (1.029) Real Exchange Rate: ln(REX ) -0.079 -0.079 (0.061) (0.061) Number of observations 180 180 Sources : UN Comtrade, World Economic Outlook Database (April 2014) by IMF and Key Economic Indicators 2013 by ADB Note : *, ** and *** indicate significance at the 10%, 5% amd 1% level, respectively. The figure in parenthesis denotes standard error.
Table 10: Gravity Model Estimation on GMS Member Countries' Exports of Automobile
Dependent variables GMS member's exports: Exp it Equation (1) Equation (2) Const. 41.000 *** 33.555 *** (15.879) (8.504) Joint GDP: ln(GDP X*GDP M ) 1.691 *** 0.894 ** (0.443) (0.038) Joint GDP per capita: ln(GDP X /P X*GDP M /P M) 0.612 (0.720) 2 Gap of GDP per capita: ln((GDP X /P X-GDP M /P M) ) 0.816 *** (0.237) Distance: ln(D ) -7.449 *** -5.804 *** (1.524) (1.252) Real Exchange Rate: ln(REX ) -0.112 -0.112 (0.077) (0.074) Number of observations 180 180 Sources : UN Comtrade, World Economic Outlook Database (April 2014) by IMF and Key Economic Indicators 2013 by ADB Note : *, ** and *** indicate significance at the 10%, 5% amd 1% level, respectively. The figure in parenthesis denotes standard error.
184
References Anderson, J.E. 1979. A Theoretical Foundation for the Gravity Equation. American Economic Review, 69(1), pp.106-116 Anderson, J.E. and van Wincoop, E. 2003. Gravity with Gravitas: A Solution to the Border Puzzle. American Economic Review, 93(1), pp.170-192 Bergstrand, J.H. 1989. The Generalized Gravity Equation, Monopolistic Competition and the Factor Proportions Theory in International Trade. The Review of Economics and Statistics, 71(1), pp.143-153 Greenaway, D. and Milner, C. 1983. On the Measurement of Intra-Industry Trade. The Economic Journal, 93(372), pp.900-908 Greenaway, D., Hine, R. and Milner, C. 1995. Vertical and Horizontal Intra-Industry Trade: A Cross Industry Analysis for the United Kingdom. The Economic Journal, 105(433), pp.1505-1518 Grubel, H.G. and Lloyd, P.J. 1971. The Empirical Measurement of Intra-Industry Trade. Economic Record, 47(4) pp.494–517 Jones, R.W. and Kierzkowski, H. 1990. The Role of Services in Production and International Trade: A Theoretical Framework, In: Jones R. and Kruger, A. (Eds.), The Political Economy of International Trade: Essays in Honor of Robert E. Baldwin, Basil Blackwell, Oxford, pp.31-48 Kimura, F., Takahashi, Y. and Hayakawa, K. 2007. Fragmentation and parts and components trade: Comparison between East Asia and Europe. North American Journal of Economics and Finance, 18(1), pp.23-40 Nozaki, K. 2014. Regional Disparity and Economic Linkage in the Greater Mekong Sub-region. International Journal of Development Issues, 13(1) pp.59-71 Taguchi, H. and Lar, N. 2014. Fragmentation and Trade of Machinery Parts and Components in Mekong Region. The Singapore Economic Review (forthcoming) Vandenbussche, H. and Zanardi M. 2010. The Chilling Trade Effects of Antidumping Proliferation. European Economic Review, 54(6), pp.760-777
185
Chapter IX: Summary
Hiroyuki Taguchi, Professor, Saitama University, and Visiting Researcher, ESRI
This report aimed to address the development issues for Cambodia, Lao PDR and
Myanmar (CLM), the latecomer’s economies in ASEAN, and to investigate how they can catch up with the ASEAN forerunner’s economies, thereby leading to a convergence among ASEAN economies. Since each chapter dealt with individual country issues except the last Chapter VIII, we herein summarize the whole chapters from the cross-country perspectives. According to the World Economic Outlook Database
(October 2014 Edition) of the International Monetary Fund, the GDP per capita in 2014
(estimate) is: 1,104 U.S. dollars in Cambodia, 1,697 U.S. dollars in Lao PDR, and 1,270
U.S. dollars in Myanmar, respectively. Their economies, thus, is entering the critical development phase in terms of GNI per capita – 1,045 U.S. dollars, which are the World
Bank criteria to classify economies into low-income and middle-income ones. For their economies, the urgent issue should be to get away from the low-income stage of
“poverty trap” and to consolidate their middle-income position. The middle- and long-term issue for them would be to sustain their growth and development in the middle-income stage by avoiding “middle-income trap”.
If we follow the textbook of development economics, a low-income economy usually follows a Lewis-type development process, in which labor moves from low-productivity sectors like agriculture to higher-productivity and labor-intensive industries such as food-processing and garment manufacturing. In a sense, this initial stage development depends on “factor-driven” and “resource-driven” growth in the context of “growth
186 accounting”. However, the economies with factor-driven growth cannot sustain their growth, due to “diminishing returns” and their wage increase, thereby being unable to compete with lower-wage economies and also with advanced economies in high skill innovations. To avoid this middle-income trap, a middle-income economy has to transform the growth pattern from factor-driven one to “productivity-driven” and
“innovation-driven” one.
If we apply the growth pattern above to the CLM economies, they still have much room to pursue factor-driven growth and Lewis-type development, but at the same time they should have a long-term strategy to approach productivity-driven growth to avoid middle-income trap. Then, in this summary, we divide the contents of the chapters into the urgent issues for factor-driven growth, and the long-term issues for productivity-driven growth. We finally conclude by demonstrating the role of Japan and
Thailand for the sustainable development of the CLM economies.
Urgent Development Issues
When it comes to the issues for factor-driven growth, the sufficient supply of labor forces and the capital formation through inward foreign direct investment (FDI) are key factors to promote growth. The frameworks of special economic zone (SEZ) is one of the effective devices to facilitate the usage of surplus labor forces and the acceptance of
FDI, and the attendance of global value chains is also an important mechanism to induce factor-driven growth.
Regarding the adaptability of the Lewis-type development process to Cambodian economy in its initial development stage, Egawa (Chapter II) argued that in accordance
187 with the trend in the extension of value chains in the form of “Thailand plus one”, the emerging labor-intensive manufacturing sectors has started to absorb the surplus labor forces in the countryside; and that this development process would make Cambodian economy achieve growth at 7% annually from the analogy of Thailand’s past experience in the 1970s. His argument seems to be commonly applicable to the other two economies of Lao PDR and Myanmar.
Concerning the SEZ development, Saran (Chapter III) and Nozaki & Phouphet
(Chapter V) described the frameworks of several investment incentives and also some progress in inviting manufacturing companies in Cambodia and Lao PDR, although at the same time they pointed out some issues to be cleared such as lack of infrastructure and high-cost of logistics. As for the SEZ in Myanmar, Ni Lar & Taguchi (Chapter VI) explained that the new SEZ law has just been promulgated in January 2014, but it would be adapted only to three big development projects. In a sense, Myanmar seems to be a bit behind Cambodia and Lao PDR in the SEZ development.
It should be noted that Phouphet & Nozaki (Chapter IV) and Ni Lar & Taguchi
(Chapter VI) casted a doubt on the effect of the current inward FDI on growth in Lao
PDR and Myanmar, since their inward FDI has depended much on such resource sectors as energy and mining. Phouphet & Nozaki (Chapter IV) emphasized that the FDI in the mining sectors in Lao PDR might even cause “Dutch disease” i.e., appreciation of real exchange rate, through their simulation analysis, and implied the necessity of SEZ development to diversify its FDI into non-resource sectors including manufacturing sectors. Ni Lar & Taguchi (Chapter VI), although presenting the evidence of positive impacts of inward FDI on growth in general as a whole ASEAN, could not find any
188 significant effects of FDI on growth in Myanmar, and interpreted its reasons as high dependence of FDI on oil and gas sectors and as much lower share of FDI relative to
GDP. They also emphasized the needs to remove the business constraints for inward
FDI in manufacturing sectors under the new Foreign Investment Law that was promulgated November 2012.
Long-term Development Issues
When we consider the issues for productivity-driven growth, the issues might be related with many elements and aspects: technology, innovation, industrial upgrading, human resource development, infrastructure development, institutional arrangement, and so forth. Egawa (Chapter II) picked up s ome challenges for Cambodian future sustainable development: quality of labor force, economic infrastructure and governance.
Ni Lar & Taguchi (Chapter VI) addressed this long-term issue in Myanmar from the perspective of “industrial upgrading”, and argued that a large-scale economy like
Myanmar with about 60 million populations should have such consolidated production structures as industrial diversification towards a variety of manufacturing sectors and industrial upgrading towards upstream and high-valued manufacturing sectors. Then, based on their value-added trade analyses, they proposed the “double-track” strategy for
Myanmar’s industrial upgrading: to promote less-sophisticated industries such as food, wood and textile by maximum use of local resources through “quick-win” manner, and to raise up more-sophisticated industries such as machinery, electrical and transport equipment by getting technology-transfers through inward FDI in the long-run. Taguchi
& Ni Lar (Chapter VII) illustrated the long-term growth prospect in Myanmar economy
189 under such scenarios as intensifying investment and improving total factor productivity by constructing a macro-econometric model. It reveals in a sense the development path towards middle-income stage in Myanmar through a combination of factor-driven and productivity-driven growth.
In the long-run development process for latecomer’s economies, the geographical consideration is another important focus as development strategies. The aforementioned
SEZ creation itself means “spot-area” development in an economy. For extending the economic impacts of the SEZ development to nation-wide level, the SEZ should be linked with each other and contribute to “economic corridor” development in a whole
Mekong region, which would be materialized in terms of trade and investment flows and value-chain-network creations in the region. Saran (Chapter III) and Nozaki &
Phouphet (Chapter V) presented interest findings on the international linkage of SEZs between Cambodia and Lao PDR and between Lao PDR and Vietnam, as well as their linkages with Thailand. Ni Lar and Taguchi (Chapter VI) discussed the future possibilities to create economic corridors between Myanmar and Thailand along with the Greater Mekong Sub-region (GMS) corridors proposed by ADB.
Nozaki (Chapter VIII) contributed to the report from a cross-country viewpoint by analyzing the intra-industry trade in the GMS. He argued that in clothing industry the both horizontal and vertical division of labor are observed in the GMS intra-industry trade, while in automobile industry, only vertical division of labor is found in its intra-industry trade. It suggested that in labor-intensive industries even the CLM economies have caught up i n their production of some items and it has enabled
“horizontal product differentiation” for intra-industry trade, while in high-tech
190 industries the intra-industry trade in the form of “fragmentation” is dominant. It implied that if the CLM economies sustain their growth in the productivity-driven manner and catch up f orerunner’s economies in the long-run, the pattern of intra-industry trade might be shifted towards the “horizontal product differentiation”.
Role of Japan and Thailand
The creation and extension of global value chains have been and will be one of the driving forces for the CLM economic development. Accepting value chains contributes to factor-driven growth by absorbing labor forces and inviting inward FDI, and also contributes to productivity-driven growth by facilitating the acceptance of technological transfers from more developed economies. As long as income gap between Thailand and with CLM exists, Thailand would continue to play a central role in prevailing value chains in Mekong region, in such a way that Thailand fragments the labor-intensive production functions towards the CLM and focuses its industries on more high-valued ones. Many of Japanese companies have been and will be involved in the creation and extension of value chains in Mekong region, thereby taking a major role to facilitate
FDI and transfer of technologies.
For the government side in both Japan and Thailand, the supports for infrastructure development, institutional arrangement and human resource development in Mekong region are definitely the essential roles for the CLM sustainable development.
191