“New trade theory”: New evidence from VietNam
20 tháng 11, 2013 bởi
“New trade theory”: New evidence from VietNam
Trần Hữu Trung


Bài báo này sẽ sử dụng mô hình Lực hấp dẫn, lần đầu tiên được áp dụng bởi Tinbergen (1962), và dữ liệu hỗn hợp bao gồm 18 đối tác thương mại chính của Việt Nam giai đoạn từ 1995 đến 2011. Mục đích để kiểm tra xem liệu Việt Nam có trao đổi thương mại nhiều hơn với các đối tác có cùng quy mô và trình độ phát triển. Kết quả thực nghiệm cho thấy Việt Nam xuất khẩu nhiều hơn sang các nước có cùng điều kiện phát triển và quy mô.

Ngược lại, không có bằng chứng khẳng định rằng Việt Nam nhập khẩu nhiều từ các đối này. Ở một khía cạnh nhất định, kết quả nghiên cứu củng cố cho Lý thuyết Thương mại mới được khởi nguồn từ cuối những năm 1970 đầu những năm 1980.

Dưới đây là bài báo khoa học ““New trade theory”: New evidence from VietNam của NCS. Hoàng Chí Cương- Khoa Quản trị Kinh Doanh, Đại học Dân lập Hải Phòng.

Abstract

This paper employs Gravity model, first used by Tinbergen (1962), and a panel data that concludes 18 Vietnam’s main trading partners in the period from 1995 to 2011. This is for the purpose to examine whether Vietnam will trade more with the countries which have the similarity in size. The empirical results show that the index of country similarity in size promotes strongly Vietnam’s exports. By contrast, there is no evidence that demonstrates convincingly that this index induces the country’s imports. These results partly support for the “New trade theory” which was initiated in the late 1970s and the early 1980s.

JEL Classifications: F13, F14, F15

Key words: Vietnam, Gravity model, export, import, SIMSIZE, Hausman – Taylor estimator

Introduction

International trade is the exchange of capital, goods, and services across international borders or territories. [1] In international trade, inter - industry trade is usually driven by differences in factor endowments (hence price) as stated in neoclassic theories such as Theory of Comparative Advantage of David Ricardo and Hechsker - Ohlin (H-O) Theory of Eli Heckscher and Bertil Ohlin. One of the founding principles of these free trade models is the perfect competition principle, which suggests that multiple producers of goods competing with each other ultimately reduce prices for consumers and that this situation is the most beneficial for the society at large. This advantage might come due to natural factors within a country such as climate or natural resources, or those countries might enjoy a labor advantage when producing a particular product. However these theories/models cannot explain for the occurrence of intra – industry trade (the two-way exchange of goods within standard industrial classifications). These include the fact that most trade is between countries with similar factor endowment and productivity levels, and the large amount of multinational production (i.e., foreign direct investment) that exists. This resulted in a new born of New Trade Theory.

New trade theory is a collection of economic models in international trade which focuses on the role of increasing returns to scale and network effects, which were developed in the late 1970s and the early 1980s. [2] The theory was initially associated with Paul Krugman. [3]New trade theorists relaxed the assumption of constant returns to scale, and some argue that using protectionist measures to build up a huge industrial base in certain industries will then allow those sectors to dominate the world market. New trade theory attempts to understand and explain the way that global trade affects the variety of goods available to consumers around the globe. Even though countries may have no particular disadvantage in producing a particular type of good, they may still import this good from another country. This, in turn, produces more variety for the individual consumer.

Although new trade theory can explain the growing trend of trade volumes of intermediate goods, Krugman’s explanation depends too much on the strict assumption that all are symmetrical, meaning that they all have the same production coefficients. Shiozawa, based on much more general model, succeed in giving a new explanation on why the traded volume increases for intermediate goods when the transport cost decreases. [4] Marc Melitz and Pol Antràs stated a new trend in the study of international trade. While new trade theory put emphasis on the growing trend of intermediate goods, this new trend emphasizes firm level differences in the same industry or the same country and this new trend is frequently called“New” New Trade Theory. This stresses the importance the challenges and the opportunities countries face in the age of globalization. [5]

In contrast with the large body of empirical studies of foreign researchers (e.g., Mauro (2000), etc) that examine where the countries which have the same size will trade more, is no empirical study examining the case of Vietnam. From this approach, the author will try by this way to examine the case of Vietnam. Vietnam offers a particular interesting case for several reasons. First, there was no empirical study on the case of Vietnam that has examined this matter. Second, an understanding of the impact of the country similarity in size on Vietnam’s foreign trade will be an important implication for the design of supporting trade policies. My hypothesis is that Vietnam will trade more with countries which have the same size with Vietnam, especially in export side. If this prediction holds true, my empirical study will support for the “New trade theory”. The remainder of this paper is organized as follows. The section 1 will first analyze briefly Vietnam’s foreign trade from 1995 to 2011. Then, section 2 details the gravity model and decrypts the data set. After that, section 3 analyzes the empirical results. The final section refers to some concluding remarks.

1. An analysis of Vietnam’s foreign trade from 1995 to 2011

Source: Personally calculated from figures published by the Vietnam General Statistics Office (2012).

Figure 1 Vietnam’s foreign trade and percentage changes from 1995 to 2011

Figure 1 above shows Vietnam’s foreign trade values and percentage changes from 1995 to 2011. Generally, it is clear that Vietnam’s foreign trade kept accelerating gradually. Specifically, the total value of Vietnam’s foreign trade (exports plus imports) has increased from $ 13,604.3 million in 1995 to $ 84,717.3 million in 2006 and to $ 203,665.6 million in 2011 - 15 times greater than 1995, 6.76 times higher than that of in 2000 ($ 30,119.2 million) and a 2.4-fold increase in comparison with total trade in 2006. Its exports rose from $ 5,448.9 million in 1995 to $ 39,826.2 million in 2006 and to $ 96,905.7 million in 2011. Its imports increased from $ 8,155.4 million in 1995 to $ 44,891.1 million in 2006 and to $ 106,749.9 million at the same time. 5 years following WTO accession, the values of both exports and imports of Vietnam were 2.4 times higher than that of in 2006. The average growth rates of total trade, exports and imports in duration from 2007 to 2011, 20.22%, 20.36% and 20.20% in sequence, were equivalent to the duration 2000-2006, 20.55%, 19.67%, and 21.46% in order.

It is also obvious that Vietnam’s trade deficit had experienced an upward trend together with the increase of trade size. Trade deficit has increased from $ 1,153.8 million in 2000 to $ 5,064.9 million in 2006 and stopped at $ 12,609.3 million in 2010, 10 times higher than that of in 2000 and 2.44 times better in comparison with 2006. The average percentage of trade deficit/exports in the duration 2000-2006 was 15.16% compared to 21.57% 5 years afterjoining the WTO.

2. The specification of gravity models and decrypting the data set

The Gravity model in international trade presents a more empirical analysis of trading patterns. The gravity model, in its basic form, predicts trade based on the distance between countries and the interaction of the countries’ economic sizes. The model mimics the Newtonian Law of gravity which also considers distance and physical size between two objects. The model has been proven to be empirically strong through econometric analysis and takes the following forms:

Fij = G(MiMj)/Dij (1)

In which:

. Fij is the bilateral trade flow between country i and country j

. Mis the economic mass of country i(often using GDP, GNP measurements)

. Mis the economic mass of country j (often using GDP, GNP measurements)

. Dij is the distance between countries (i and j), and

. G is a constant.

For further development, many other variables can be added in the model such as: Transport and transaction costs; FDI inflows (FDI stock per capita); Trade policies, Exchange rate regime; Cultural differences: colonial history,language diversity and literacy rate (%); Institution, uncertainty; Preference schemes: Generalized system of preferences (GSP); Limited overlap in consumer preference schemes; Market access, Openness; Index of country similarity in size, economic size similarity, differences in relative endowments etc.

In a panel data setting, random-effects and fixed-effects models have been traditionally and widely used for the estimation of Gravity models. The choice between them is using the Hausman test. However, both methods have their own disadvantages. While the random-effects models do not incorporate country fixed-effects (which are likely to be presented in a heterogeneous country sample), time – invariant variables will not yield coefficient estimates in a fixed-effects model. It means that we cannot acquire estimates for the variation that is captured in the country fixed-effects, although these can be quite interesting in a Gravity model, since they reveal the distance between two countries and reveal whether they share a land border. As a remedy, Hausman and Taylor (1981) and Wyhowki (1994) proposed a different model that could incorporate the advantages of the random-effects and the fixed – effects models. Egger (2005) stated that the Hausman-Taylor estimator is consistent and the performance is at least equivalent to the random-effects and the fixed-effects estimators. McPherson and Trumbull (2003) also tested different estimators and found the Hausman-Taylor estimator to be superior in the estimation results. From this perspective, the author will use the Hausman – Taylor estimator for the empirical analysis in this paper. The Hausman – Taylor estimator is basically a hybrid of the fixed - effects and the random - effects models and takes the following form:

yitβ1 x’1it + β2 x’2it + a1z’1i + a2z’2i + ɛit + u(2)

In which, yit  reflects the dependent variable for country i in period/time/year t; x’1itdenotes variables that are time varying and uncorrelated with the error term in the random-effects model (ui); x’2it refers to a set of variables that are time varying and correlated with ui; z’1i represents the time invariant variables that are uncorrelated with ui; z’2i describes the time invariant variables that are correlated with uiβi and ai are the vectors of coefficients associated with the covariates; and ɛit is the random error with the hoping that its value is appropriate zero. Accordingly, one of the main assumptions of the Hausman - Taylor estimator is that the explanatory variables that are correlated with ui can be identified. My benchmark specification models take the following forms:

LnEXjt= β10 + β11LnDISVNj + β12LnGDPVNt + β13LnGDPjt + β14 Ln[1- (GDPVNt/(GDPVNt + GDPjt))- (GDPjt/(GDPVNt + GDPjt))2] +γ11BORVNj + ε1VNj (3)

LnIMjt = β20 + β21LnDISVNj+ β22LnGDPVNt+ β23LnGDPjt + β24 Ln[1- (GDPVNt/(GDPVNt�+ GDPjt))- (GDPjt/(GDPVNt + GDPjt))2] +γ21BORVNj + ε2VNj (4)

In which:

· DISVNj is the weighted distance between Vietnam and country j in km (obtained from CEPII).

· GDPVNt is the real GDP of Vietnam at year t in $ (2005 price).

· GDPjt is the real GDP of country j at year t in $ (2005 price).

· EXjt is the real Vietnam’s exports to country j at year t in $ (2005 price).

· IMjt is the real Vietnam’s imports from country j at year t in $ (2005 price).

· BORVNj is a binary dummy which is unity if Vietnam and country j share the land border.

· [1-(GDPVNt/(GDPVNt+GDPjt))- (GDPjt/(GDPVNt + GDPjt))2]is the index of country similarity in size (SIMSIZE in short) that takes the value in the phase (-∞, -0.69). In case of perfect dissimilarity (GDPVN has a huge difference with the GDPat year t), then Ln[1- (GDPVNt/(GDPVNt + GDPjt))- (GDPjt/(GDPVNt + GDPjt))2] ≈ ln (near Zero) = -∞. In case of perfect similarity (GDPVN has a very small difference with the GDPat year t, or GDPVNt@ GDPjt), then Ln[1- (GDPVNt/(GDPVNt + GDPjt))- (GDPjt/(GDPVNt + GDPjt))2] ≈ ln (0.5) = - 0.69. The index of country similarity in size should have positive impact on foreign trade, especially on exports. All the variables, except the dummies, are in natural logarithm form in the gravity equations.

For the data, the empirical analysis presented in this paper is based on a panel data set in the period from 1995 to 2011 which involves 18 Vietnam’s major/stable trading partners including: Australia, Belgium, Canada, China, France, Germany, Hong Kong, Japan, Malaysia, the Netherlands, the Philippines, Russia, Singapore, the Republic of Korea, Taiwan, Thailand, the United Kingdom (UK), and the United States. 18 trading partners listed above amount to around 80% of Vietnam’s foreign trade for the duration 1995 - 2011. The data is obtained from different reliable sources such as Vietnam’s authorities (e.g., the General Statistics Office (GSO), the Ministry of Industry and Trade (MIT), the Ministry of Planning and Investment (MPI)), and the international organizations (e.g., the Asian Development Bank (ADB), the International Monetary Fund (IMF), the United Nations Statistics Division (UNSD), the World Bank (WB). In regards to the special case of Chinese Taipei (Taiwan), the figures are collected from ADB and the World Economic Outlooks October 2012, available on Knoema’s website.

3. An analysis of the empirical results

The empirical results of LnEXjt and LnIMjt gravity equations are summarized and reported in the Table 1 below using the Stata 11 and Hausman – Taylor estimator.

Table 1 Gravity Model Estimations (Hausman – Taylor estimation)

Explanatory Variables

Dependent Variables

LnEXjt

LnIMjt

Time Varying Exogenous

   

LnSIMSIZE

2.251414*

-0.205074

Time Varying Endogenous

   

LnGDPVNt

0.217585

2.025894*

LnGDPjt

2.627169*

0.860687

Time invariant Exogenous

   

LnDISVNj

-0.92879*

-1.80686*

BORVNj

-0.420703

-0.654966

Constant

-43.6462*

-38.2143*

Note: * Significant at the 1% level (or better);

The estimated results presented in the Table 1 indicate that a large share of the variation of Vietnam’s exports and imports recently could be explained by a considerable number of factors, namely, GDP, Distance, and the index of country similarity in size. The coefficient of the SIMSIZE variable in the LnEXjt equation is positive and statistically significant at the 1% level suggesting that Vietnam exports much more to the country which have the similarity in size. By contrast, the coefficient of this variable in the LnIMjt equation is not significant indicating that Vietnam does not import as such from those similar trading partners.

Concluding remarks

By employing gravity models and a panel data using the Hausman-Taylor estimator, the paper finds the evidence that the SIMSIZE promotes strongly Vietnam’s exports to similar trading partners but does not induce imports from those countries. These results partly support for the New trade theory. It means international trade is not only driven by differences in factor endowments as stated in neoclassic theories but also by the identical factor endowments. However, available data have been too limited to produce a persuasive test of the hypothesis. The results do require arbitrary judgments from the researchers and readers. Future researches should catch attention on this matter.

References

Mauro, F.D., November 2000. The Impact of Economic Integration on FDI and Exports: A Gravity Approach, Working Document No. 156.

[1] dictionary.reference.com

[2] In economics, returns to scale and economies of scale are related terms that describe what happens as the scale of production increases in the long run, when all input levels including physical capital usage are variable (chosen by firm). They are different terms and should not be used interchangeably. The returns to scale arise in the context of a firm’s production function. It refers to changes in output resulting from a proportional change in all inputs (where all inputs increase by a constant factor). If output increases by that same proportional change then there are constant returns to scale. If output increases by less than that proportional change, there are decreasing returns to scale. If output increases by more than that proportional change, there are increasing returns to scale. Notably, the returns to scale faced by a firm are purely technologically imposed and is not influenced by economic decisions or by market conditions.

network effect (also called network externality or demand-side economies of scale) is theeffect that one user of good or services has on the value of that product to other people. When network effect is present, the value of a product or service is dependent on the number of others using it such as online social networks: Facebook, Twitter (Shapiro, C. et al (1999)).

[3] He is an American economist, Professor of Economics and International Affairs at the Woodrow Wilson School of Public and International Affairs at Princeton University, Centenary Professor at the London School of Economics, and an op-ed columnist for The New York Times who got the Nobel Prize Award in 2008.

[4] Shiozawa, Y. (2007). “A New Construction of Ricardian Trade Theory: A Many-country, Many-commodity with Intermediate Goods and Choice of Techniques”, Evolutionary and Institutional Economics Review 3 (2), pp. 141–187.

[5] Melitz, Marc J. (2003). “The Impact of Trade on Intra-Industry Reallocations and Aggregate Industry Productivity”, Econometrica 71, pp. 1695–1725; Antras, Pol; Helpman, Elhanan (2004). “Global Sourcing”, Journal of Political Economy, 112, pp.552–580; Ottaviano, Gianmarco I. P. (2011). “‘New’ new economic geography: firm heterogeneity and agglomeration economies”, Journal of Economic Geography 11 (2), pp.231–240.

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