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BY
MOK KA YAN
STUDENT NO. 09010408
A PROJECT SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR
THE DEGREE OF
BACHELOR OF SOCIAL SCIENCES (HONOURS) DEGREE IN CHINA STUDIES
ECONOMICS OPTION
HONG KONG BAPTIST UNIVERSITY
APRIL 2012
09010408/China Studies - Economics Option/Honours project (Spring 2012)
HONG KONG BAPTIST UNIVERSITY
April 2012
We hereby recommend that the Project by Miss. Mok Ka Yan entitled “Sino-
Russian Trade: Empirical Studies of increasing the export value of China –
Upgrading China’s Trading Structure” be accepted in partial fulfillment of the
requirements for the Bachelor of Social Sciences (Honours) Degree in China Studies
in Economics.
_______________________ _______________________
Dr. LI, Sung Ko Dr.
Project Supervisor Second Examiner
09010408/China Studies - Economics Option/Honours project (Spring 2012)
Acknowledgements
I would like to thank my supervisor Dr. LI, Sung Ko for his kind help,
suggesting the research methods and guiding me through the entire study, and Dr. Bill
Hung for his website and his suggestions which assist me in computing work.
__________________________
Student signature
China Studies Degree Course
(Economics Option)
Hong Kong Baptist University
Date: ____________________
09010408/China Studies - Economics Option/Honours project (Spring 2012)
Table of Contents
Abstract
I. Introduction 1-2
II. Literature Review 3-5
III. Background and History 6-9
IV. Current Situation 10-15
V. Revealed Comparative Advantage (RCA) – in the world case 16-21
VI. Revealed Comparative Advantage (RCA) – in two countries case 22-25
VII. Trade Combining Density Index (TCD) 26-28
VIII. Empirical Result 29-59
i. Part 1 - Empirical Result on China’s exports to Russia
ii. Part 1 - Regression Model Implication
iii. Part 2 - Empirical Result on Upgrading China’s Trading Structure to Russia
iv. Part 2 - Regression Model Implication
v. Model Limitation and Suggestion
vi. Suggestion to the Sino-Russian trade
IX. Conclusion 60
X. Appendix 61-66
i. Part 1- Descriptions of variables
ii. Test for part 1 of regression
iii. Part 2- Descriptions of variables
iv. Test for part 2 of regression
XI. Bibliography 67-72
i. 中文文獻
ii. 中文參考書籍
iii. 中文報章
iv. 中文網頁
v. English Literature
vi. Online sources
vii. News
viii. Statistics sources
09010408/China Studies - Economics Option/Honours project (Spring 2012)
Abstract
China and Russia are two great and rising economic powers, and they have the
longest boarder. Their trade relations do have great potential power in further
development.
In this paper, we would use three methodologies in finding out their trading
structure, their trade relationship and the determinants that improve the Sino-Russian
trade, namely the Revealed Comparative Advantage index, the Trade Combining
Density Index (TCD), and two parts of regression model.
There are four major important findings. First, the Sino-Russian existing trading
structure matches the market of each other. China mainly exports manufacturing
products (either labour-intensive and high skilled or high-technology products) to
Russia, and Russia mainly exports primary products and fuels to China.
And, their existing bilateral trade relationship is not close enough when
compared to other trading partners of China, and could be further improved.
Third, there are several factors that significantly affect the exports value from
China to Russia, namely, per capita GDP of Russians, high-skilled and technology
intensity exports, exchange rate, and financial crises. And we discover the inefficient
of Russian tariffs in limiting China’s exports to Russia, and trade agreements in
facilitating their trade.
Forth, one of the methods that could increase the exports to Russia is to improve
the export structure by China, i.e. to export more high-skilled and technology
intensity exports. The method to increase its proportion could be done by attracting
more FDI in this field, increasing the R&D expenditure and to increase the number of
researchers in the R&D process. And we notice that increasing the Government
expenditure and increasing the number of firms in this field are not very effective in
raising its exports value.
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I. Introduction
China and Russia have a very good cooperation and geographical background in
trade. For example, they are both emerging economies, and transformed from planned
economies into market-based ones, so they were both transition countries from
socialism, and are now the emerging powers in BRICS. They are also the largest
neighbor of each other, with 4,300 kilometers border and more than 20 trade ports.
And they have a long history (from the year 1653) of cooperation and relationship.
Sino-Russian trade is improving in the past years, and the Governments of the
two countries also have intention to boost their bilateral trade. They has reached
USD560 billion in 2010, and it reached USD719 billion in 2011. The year 2011 is the
10th anniversary of the signing of “The Treaty of Good-Neighborliness and Friendly
Cooperation between the People's Republic of China and the Russian Federation”
(FCT). It is also the 15th anniversary of the establishment of Sino-Russian strategic
partnership. And both China and Russia wanted to increase trade with each other. In June
2011 when China’s President Hu Jintao visited Russia, he announced the blueprint for
development of their bilateral relations in the next 10 years. In the blueprint,
Sino-Russian bilateral trade goal will be developed to USD1000 billion in 2015, and
increased to USD2000 billion in 2020.
As they have geographical and developmental advantages as mentioned
above, and the economic recession due to the dissolution of Russia (1992) has
pasted; the GDP of Russia have increased much in recent years, the purchasing
power of Russians boosted. So, we started to look whether China’s exports could,
and how it could further grab the import market of Russia.
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So, in this project, we aim to find out (1) whether China and Russia have
incentives to continuous or further increase their trade with each other; (2)
whether the existing trade is close enough, (3) from China’s perspectives, the
determinants that affect China to increase her export value to Russia, and (4)
how China could improve her export structure to Russia in order to increase
trade or even grab the import market of Russia.
In the followings, the project will be divided into 4 major parts. In the first part,
we would look at the literature review, background and the current situation of
Sino-Russian trade. Second part would contain the analysis of Revealed Comparative
Advantage, both assuming in the world case and in two countries case, and also the
analysis of Trade Combining Density Index (TCD). The third part would be empirical
analysis about increasing the exports of China to Russia, and suggestions to improve
Sino-Russian trade partly based on the regression result.
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II. Literature Review
There have been a number of academic researches carried out in arguing the
Sino-Russian trade could be increased in value, the major reasons for saying that are:
(1) their trade development does not match with the political development; (2) the
export structure for both countries should be improved; (3) Russia has imposed many
trade barriers for China’s exports, which hinder the development of their bilateral
trade. (4) The problem of “Grey customs clearance”. And there is a great potential for
China to more rely on the trade with Russia, especially after the financial crises in
USA and the potential of outbreak of European Bond Crisis.
For the first reason, scholars suggest that the value of bilateral trade between the
two countries have been relatively low when compared with the trade value of China
and Russia with other countries respectively(魏浩,2008;董銳,2010).They think
that this phenomenon do not match with their political and strategic cooperation
relations(楊希燕,2005;曹英華,2007), and also their needs for economic
development (i.e. they need to trade with each other for facilitating their economic
development in their own countries)(彭傳江、王麗英、周立,2004).Also, as they
are both the emerging power of BRICS, the economic and trade cooperation should
not be that low(丁振輝;2010), thus the bilateral trade value should be improved.
Moreover, there has been trade deficit for China continuously before 2007. Some
scholars suggested that the reason to explain this phenomenon is that Russia has long
been the exporter of resources, especially power sources(張紅燕,2004;曹英華,
2007;馬健美、趙岩,2010). More importantly, it is due to the low-level export
structure of China, i.e. China only export low value-added consumer goods, such as
09010408/China Studies - Economics Option/Honours project (Spring 2012)
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textiles, food and shoes(劉紅雨,2008;董銳,2010;任賢慧,2007). Although
the trade deficit phenomenon has been improved after 2007, the low value-added and
labour-intensive export structure of China still hinders the growth in trade.
Third, there are many trade barriers imposed by Russia towards China’s exports.
Explicit trade barrier is mainly the high tariff rate, such as the high tariff imposed on
costume and shoes in 2010, which is direct against the China’s exports which have
comparative advantage(董銳,2010;魏浩,2008;石艾馨、李嬌,2008). And there
are implicit trade barrier like the increase in the requirements for technical and safety
level of China’s exports(任鐵爭,2010). These trade barriers imposed by Russia limit
the growth of China’s exports to Russia.
The last that hinder their trade is “Grey customs clearance” and “Baoji
Baosui”(smuggling), which leads officials underestimate the trade value between the
two countries(魏浩,2008;劉士濤、王乾,2006). As these smuggling activities
are very common for Chinese merchants to export to Russia, the values of export by
these kind of method are considerably high(李永華、畢崇志,2008).Thus, after
counting the value of these underground activities, the export value for China to
Russia should be greater(馬健美、趙岩,2010).
On the other hand, some suggests that technical and technological level of China
exports (to Russia) should be improved in order to improve the trading structure. For
example, China should focus on exporting machines, electronic appliances and
high-technology products(李青;2001;韓立華,2001;李澤祥、楊定華,2003;
劉紅雨,2008)which are already increase slowly in the proportion of exports for
China to Russia.
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Last but not least, scholars generally take a positive attitude towards the
foreground of Sino-Russian trade. Some of them even point out that the Financial
Tsunami in the US and the European Bond Crisis means China could no longer rely
on the unstable US and European countries in trade. And apart from expanding the
domestic market of China, China should rely on another big and rising economy –
Russia, in her exports(李永華、畢崇志,2008;李春豔,1999;趙傳君,2010;
趙鑫,2010).
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III. Background and History
i. Early stage:
China and Russia have been old trading partners with each other for more than
300 years. They signed the Treaty of Nerchinsk in 1689 to start bilateral trade. At the
beginning, they set Heilongjiang as the main area for Sino-Russian trade.
After the establishment of People’s Republic of China, the great diplomatic
relations between the two countries led to rapid development in their trade. For
example, the new government of China signed a number of free trade agreement,
agreement for the navigation of frontier rivers, tributaries, and lakes, and also giving
Most Favored Nation (MFN) to each other. These agreements led them became the
top trading partner with each other.
During the Cold War period, there was a Sino–Soviet split caused by the
worsening of political and ideological relations between them. They broke ties in
1966. This decreased the dependency for China towards Russia’s exports, and greatly
and directly affected trade. Their trade value had dropped from USD 82.79 million in
1960 to only USD 4.7 million in 1970. The trade value rose slowly in the 1970s,
although there was still confrontation between them.
ii. Late 1991 to 1993: Rapid development stage (First climax)
The dissolution of Soviet Union took place in late 1991. The China's State
Council and Ministry of Foreign Trade had issued a series of policies which
encouraged the scale-up of border crossings trade with Russia.
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During 1992 to 1993, after Deng Xiaoping visited the southern part of China and
encouraged opening up and construction of China1, which speeded up the construction
of basic facilities, leading to large increases in imports for construction materials from
Russia (accounted for 39% of China’s imports from Russia). On the other hand, China
mainly exported consumer goods and food to Russia in order to fulfill the needs of her
market (accounted for 45% of her exports to Russia). In this period, most trade
belonged to barter trade. And the two countries exempted tariffs and other customs
duties of each other in order to facilitate trade2.
iii. 1994 to 1995: Great declination
Although their relations changed from "partnership" in 1992 to "constructive
partnership" in 1994; there was a shrink in trade. The following three reasons could
explain this: First, the Russian government had strengthen the macroeconomic
regulation and control towards trade, e.g. imposing a trade quota system on the export
of raw materials, increasing the tariffs for both imports and exports, and implementing
licenses on some export industries. Second, the quality of life for Russians had
improved, which increase their requirements towards the quality of goods. And at the
same time, there were more and more counterfeit commodities from China entered the
Russian market, which led to bad reputation of China’s products.
1995 was also the turning point for Sino-Russian trade. It was greatly contributed
by the signing of Sino-Russian Joint Communique, which decided to change from
1劉德喜:〈中俄夥伴關係中的經貿關係〉,《從同盟到夥伴-中俄(蘇)關係 50 年》,中共黨
史出版社,2005 年,頁 270。
2 蒒君度、陸南泉:《中俄經貿關係》,中國社會科學出版社,1999 年,頁 101-102。
09010408/China Studies - Economics Option/Honours project (Spring 2012)
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barter trade to payment in cash that was already accepted by the international world.
This helps to standardize the mode of trade, and the trade value between them started
to increase again.
iv. 1996 to 1999: Adjustment of bilateral trade
The two Governments reached an agreement to increase their bilateral trade to
USD 80 to 100 billion. Russia agreed to export more natural gas, oil and energy
devices to China; while China agreed to export more labour-intensive products (e.g.
textiles) and high skilled and technology intensity products (e.g. chemicals) to Russia.
They both agreed to focus more on high-technology products and energy in their
trade3.
v. 21st century: Further cooperation
As the Russian market became more prosper and stable, the index for Russians’
quality of life rose from ranking 66 in 2000 to 55 in 2001, with the further
encouragement by the two governments towards bilateral trade; the trade value
reached higher. In 2004, the two Governments had set a target that in 2010; their
bilateral trade should reach USD 600 to 800 billion4. They agreed to improve their
trading structure5 that China should export more machinery, high value-added
products, and high skilled and technology products to Russia. And Russia agreed to
export more energy devices and energy sources to China.
3蒒君度、陸南泉:《中俄經貿關係》,中國社會科學出版社,1999 年,頁 108-112。
4 《中俄總理簽署第九次定期會晤聯合公報》,中華人民共和國外交部,2004 年 9 月。
5 陸南泉:《中俄經貿關係現狀與前景》,中國社會科學出版社,2011 年,頁 147-152。
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The leaders of two countries also exchanged visits frequently for international
issues and bilateral trade relations. In 2001, they signed the “Treaty of
Good-Neighborliness and Friendly Cooperation”6 in order to develop a strategic
partnership in trade and military aspects. In the treaty, China could obtain a stable,
consistent and affordable level of fuel shipments, and she could better purchase and
deliver Russian oil, including the construction of a Trans-Siberian oil pipeline.
The leaders of the two countries had visited each other and signed
joint-statements almost every year. The Governments also launched a number of
policies to promote trade, e.g. the "Country Year" activities (2006) and the Year of
Russian language (2009). Sino-Russian trade relations were stabilized.
Moreover, as the problem of "Grey customs clearance" (the goods entering
Russia in this way is to avoid issuing official customs declaration documents, which
decrease the great costs of tariffs by Russia) has been a major problem of their trade
since the late 1980s, which caused Russia underestimate her import value from China,
and decreased her revenues from imposing tariffs. The Russian Government started to
fight against the problem since 20097.
After the financial tsunami and the high chance of the outbreak of European
Crisis, people started to think of the possibility for the two countries to set up
tariff-free area, and expected the two countries to improve their trading structure to a
more high-tech and high value-added trade. By doing so, they expected Sino-Russian
trade would be another chance for the revival of their economies.
6 “Treaty of Good-Neighborliness and Friendly Cooperation Between the People's Republic of China
and the Russian Federation”, Ministry of Foreign Affairs, the People's Republic of China. Retrieved
February, 24, 2012.
7 "China, Russia work to facilitate normal custom clearance", People's Daily Online, March 26, 2010.
Retrieved from :http://english.people.com.cn/90001/90776/90883/6932278.html
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IV. Current Situation
In this part, we would first look at the ranking of Russia as China’s trading
partner, then describe the trade balance and the trend for Sino-Russian trade. And last,
we would have an in-depth look at the export and import structure of China with
Russia.
There has been generally an increasing trend for the Sino-Russian trade, both
from China’s exports and imports’ view. First, from table 1, we notice that Russia
ranked 9th
as the importer of China’s products in 2011, with the value of USD389.038
billion. And her ranking has been increasing as she only ranked 13th
as China’s
importers in 2010. Also, the percentage increased in the export value (from China’s
perspective) was the largest among the top importers of China’s products. This, on
one hand because the Financial Tsunami led to decrease in exports; and on the other
hand, may mean Russia has great potential in buying China’s exports in the future.
Table 1:
Top trading partner of China (exports from China) in 2011(Jan-Dec)
Ranking Name of the country Value
(in thousands USD)
% increased
(compared with 2010)
1 European Union $356, 019,832 +14.4%
2 United Nations $324, 492,718 +14.5%
3 Hong Kong (China) $268, 025,399 +22.8%
4 Southeast Asian Nations $170,083,006 +23.1%
5 Japan $148,298,069 +22.5%
6 Korea $82,923,617 +20.6%
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7 Holland $59,500,012 +19.7%
8 India $50,543,174 +23.5%
9 Russian Federation $38,903,829 +31.4%
10 Singapore $35,570,443 +10%
(Generated from: the General Administration of Customs of the People’s Republic of
China)
From the imports perspective of China, Russia was the 11th
exporters to China in
2011, with the import value USD 403.455 billion by China. The ranking also rose
when compared with last year which only ranked 12th
in 2010. And the import value
by China from Russia had increased by 55.6%, which was also the largest among the
top trading partners (table 2).
Table 2:
Top trading partner of China (imports by China) in 2011(Jan-Dec)
Ranking Name of the country Value
(in thousands USD)
% increased
(compared with 2010)
1 European Union $211,193,001 +25.4%
2 Japan $194,591,317 +10.1%
3 Southeast Asian Nations $192,770,814 +24.6%
4 Korea $162,709,438 +17.6%
5 Taiwan $124,919,879 +7.9%
6 United Nations $122,153,945 +19.6%
7 Germany $92,716,442 +24.9%
8 Australia $82,722,742 +35.3%
9 Malaysia $62,144,743 +23.2%
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10 Brazil $52,359,344 +37.3%
11 Russian Federation $40,345,471 +55.6%
(Generated from: the General Administration of Customs of the People’s Republic of
China)
Throughout the period of 1997Q1 to 2010Q4, the bilateral trade balance (exports
minus imports of China) fluctuates, with mainly trade deficit for China before 2007.
Yet, in recent years, from figure 1, we know that except for the period during the
Financial Tsunami, there has been mainly a trade surplus for China in their bilateral
trade.
Figure 1 – the trade balance for Sino-Russian trade
(China’s exports to Russia minus China’s imports from Russia)
(Generated from:《中國統計年鑑》)
The trade between the two countries is generally in an increasing trend (figure 2
and 3). One thing that we should notice, i.e. it clearly shows that the financial crises
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(in 1997 and 2008) have some short-term effects on their trade value. These two
figures show that their trade with each other only drops suddenly after the broke out
of the financial crises, and then increased and recovered to the original level very
rapidly. The Financial Tsunami in 2008 has a greater effect on their trade when
compared with the Asian Financial Crisis in 1997.
Figure 2: Total exports value from Russia to China
(Generated from:《中國統計年鑑》)
Figure 3: Total imports value for China from Russia
(Generated from:《中國統計年鑑》)
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On the other hand, for their trading structure, the export structure of China to
Russia (figure 4) indicates that the top three categories of products that China export
to Russia includes: (1) labour-intensive products, like textiles and shoes; (2) heavy
industrial products which is capital intensive, i.e. base metals; and (3) the proportion
of high skilled and technology intensity products, i.e. electronic products and
machinery, increased rapidly.
Figure 4: Exports structure of China to Russia (by products)
(Generated from: 《海關統計 1997-2010》)
On the other hand, the import structure of China from Russia (figure 5) indicates
that the top two categories of products that China import from Russia includes: (1)
raw materials, i.e. minerals; (2) heavy industrial products which is capital-intensive,
i.e. base metals; and (3) relatively high technology intensity products, i.e. chemicals
and related industrial products.
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Figure 5: Imports structure of China to Russia (by products)
(Generated from: 《海關統計 1997-2010》)
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V. Revealed comparative advantage (RCA) in the world case
As we aim to find out whether the Sino-Russian trade could be further increased
in the future and particular whether the China’s exports could grab the imports market
of Russia, we would first see whether their existing trading situation matches the
needs of the market of each other. That means whether they would be complementary
in trade. So, in the following, we would calculate the revealed comparative advantage
of both China and Russia, and see which types of products they are better to produce
for trade. It is the first step to see whether the two countries could increase their trade,
and then we would look further whether their existing trade is close enough and how
China could further raise exports to Russia in latter parts. So, from this, we can look
in-depth whether their existing trading structure is complementary with each other.
The law of comparative advantage states that a country which has the ability to
produce a particular good or service at a lower marginal and opportunity cost means
she has a comparative advantage. It guides the direction of trade, i.e. the country
which has comparative advantage in produce a particular product should be the
exporter, and vice versa should be the importer. So, countries would base on it to
adjust their trading structure.
The quantitative indicators for calculating the comparative advantage is
“Revealed Comparative Advantage” (RCA), which was first suggested by Balassa
(1965)8. He suggests that the comparative advantage could be “revealed” by the
observed trade patterns, and this matches with the theory. Some researches counts
8 Balassa, B. (1965), Trade Liberalisation and Revealed Comparative Advantage, The Manchester
School, 33, 99-123.
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RCA at global level (e.g. Vollrath, 1991)9, at a sub-global or regional level (Balassa,
1977)10
, and some evaluates the measurement as bilateral trade between two countries
or trading partners (e.g. Dimelis and Gatsios, 1995)11
. And this project would use
RCA index to measure the last type.
RCA is based on observed trade patterns. The RCA measures a country’s exports
of a commodity relative to its total exports. The formula for calculating RCA is
illustrated as:
RCAik =
where Xik is the exports value of k products in Country I, Xi is the total exports in
Country I, Xk,world is the exports of k products in the world, and Xworld means the total
world exports value. Accordingly, if the RCA index of the export product of Country
I is greater than 1, it has a comparative advantage in exporting that product, i.e. it has
a relatively stronger export competitiveness, and vice versa. In the table, for ease of
9 Vollrath, T.L. (1991), “A Theoretical Evaluation of Alternative Trade Intensity Measures of
Revealed Comparative Advantage”, Weltwirtschaftliches Archiv, 130, 265-79.
10 Balassa, B. (1977), “’Revealed’ Comparative Advantage Revisited”, The Manchester School, 45,
327-44.
11 Dimelis, S. and K. Gatsios (1995), “Trade with Central and Eastern Europe: The Case of Greece”,
in R. Faini and R. Portes (eds.), EU Trade with Eastern Europe: Adjustment and Opportunities, London:
CEPR.
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understanding, we use (*) for products with strong export competitiveness, and use
(**) for extremely strong export competitiveness12
.
Since the data from the database on international merchandise trade by the WTO
(CIS)13
includes almost all of the products traded between Russia and China, and it is
simpler for making comparison than the SITC or the HS-code; we choose the CIS as
the data set for calculating the RCA of the two countries.
As shown in tables 3 and 4, we can classify these sixteen types of export
products into three categories:
Table 3: Revealed Comparative Advantage (RCA) of China
12
The standard of giving (*) and (**) is according to the Japan Export Trade Research Organization
(JETRO) which state that RCA>1.25 means the product is with strong export competitiveness. And for
RCA>2.5, it means the export product is with extremely strong export competitiveness.
13 Time Series on international trade, World Trade Organization. Retrieved January 30, 2012, from:
http://stat.wto.org/StatisticalProgram/WSDBStatProgramHome.aspx?Language=E
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(The above calculations of RCA are based on the database by the WTO)
First, for the export products that China has obvious comparative advantage than
that of Russia, i.e. textiles(15), clothing(16), office and telecom equipment(10),
electronic data processing(11) and office equipment and telecommunications
equipment(12). The first thing to note is that China has a strong comparative
advantage in the fields of textiles (15) and clothing (16). The major reason for this
phenomenon is that there is a large endowment of low-skilled and cheap labour
resources in China (about 13 billion of population). And China had developed in these
industries for many years, leading her to have relatively mature skills. This lead the
cost of production for textiles and clothing industries become lower than that of
Russia. On the other hand, the RCA of these two products for Russia has been low
throughout the years and even has a decreasing trend; it is mainly because of its slow
industrial development. And since some other countries are developing its industrial
sector rapidly but Russia are still relying on other sectors, RCA index for these two
products are decreasing. The second things to note is that the RCA index for (10), (11)
and (12) are high and have an increasing trend for China. It is because Chinese
companies no longer focus on developing low-skilled, low-cost, low-margin
industries, but are trying to move up the value chain to high-skilled and
high-technology. Also, the Government uses incentives to encourage companies to
innovate and discourage low-end manufacturers from operating in southern China. So,
Government policies are now more favor high-tech economic zones, research and
development centers and companies14
. This leads the fields of (10), (11) and (12) to
14
David Barboza (2008, August 1), China’s Ambition Soars to High-Tech Industry, The New York
Times.
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develop, and lead these export products have comparative advantage. And for Russia,
although she has the second largest high-skilled labour resources in the world, the
Government does not give encouragement to these high-tech fields like that of China;
she does not have a comparative advantage in it throughout the years, and her
comparative advantage in the high-tech export products are increasing but in a slower
process.
Second, for the export products that Russia has obvious comparative advantage
than that of China, i.e. fuels and mining products(3), iron and steel(6) and fuels(4). It
is because there is a large amount of mineral resources in Russia. And Russia is also
the top three oil producer in the world market, and she has the largest reserves of
natural gas. Also, she has rich forest resources, and her reserves in timber accounted
for 25% of the world reserves. This explains why she has a comparative advantage in
these exports products. Also, China is the top “consumer” for fuels and mining
products as she is the “world factory”. For example, in 2010, half of the oil consumed
was imported15
. So, this explains why China does not own a comparative advantage in
them. On the other hand, we can note that the value of RCA index for the three types
of export products for Russia has been decreasing. It is mainly due to government
policies of tightening the export of fuels and raw materials.
The third category is the export products that both China and Russia do not own
comparative advantages, such as chemicals (7), machinery and transport equipment
(9), automotive products (14), etc. For China, there is a decreasing trend of the RCA
index for both primary and secondary labour-intensive products. Yet, the RCA index
15楊希燕(2005):〈中俄貿易增長探析與前景預測〉,《現代財經》,第 25 卷第 190 期。
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for the technology-intensive products is increasing. On the other hand, for Russia, the
RCA for primary products, (1) and (2), are increasing. And for the secondary products,
it has not changed much. This means Russia do not have a rapid development in these
export products. And as Russia still imposes many restrictions towards her export
industry, the RCA index for these products has been in a low level.
All and all, we know that in 2000 to 2010, China has comparative advantage in
labour-intensive export products, and she has been developing her comparative
advantages in high-technology products by companies’ incentives and Government
policies in recent years. And for Russia, during the period, she has comparative
advantages in fuel, raw materials and related products. Yet, her RCA in products other
than these has not changed much.
Table 4: Revealed Comparative Advantage (RCA) of Russian Federation
(The above calculation of RCA are based on the database by the WTO)
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VI. Revealed Comparative Advantage in two countries case
In this part, we consider only the trade between China and the Russian
Federation. The purpose of assuming this is to have an in-depth look at the
comparative advantage of each type of products between these two countries. The
RCA in this part measures a country’s exports of a product relative to the total exports
of the two countries. The formula of calculating this is:
RCAik =
where Xik is the exports value of k products in Country I, Xi is the total exports in
Country I to Country J, Xk,ij is the total exports of k products by Country I and J, and
Xij means the total world exports value of Country I and J. Accordingly, if the RCA
index of the export product of Country I is greater than 1, it has a comparative
advantage in exporting that product, i.e. it has a relatively stronger export
competitiveness, and vice versa. In the table, for ease of understanding, we use (*) for
products with strong export competitiveness, and use (**) for extremely strong export
competitiveness16
.
In this project, since we are trying to know their possible trading structure by
comparative advantage, we choose the UNCTAD statistics17
(which use the SITC
code) as the data set for calculating the RCA of the two countries. And we have
16
The standard of giving (*) and (**) is according to the Japan Export Trade Research Organization
(JETRO) which state that RCA>1.25 means the product is with strong export competitiveness. And for
RCA>2.5, it means the export product is with extremely strong export competitiveness.
17 UNCTAD Statistics, United Nations Conference on trade and development. Retrieved February 19,
2012, from: http://www.unctad.org/Templates/Page.asp?intItemID=1584&lang=1
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divided the products into different categories to have a closer look at their
comparative advantage, namely primary products, low-to-medium skilled products,
and high-skilled products.
Table 5: Revealed Comparative Advantage (RCA) of China (2 countries case)
(The above calculations of RCA are based on the UNCTAD statistics)
Table 6: Revealed Comparative Advantage (RCA) of Russia (2 countries case)
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(The above calculations of RCA are based on the UNCTAD statistics)
From the table, for products that China has comparative advantage relative to
that of Russia, it includes low-to-medium skilled and high skilled products. We can
clearly note that she is a bit more competitive in low-to-medium skilled products than
that in high-skilled products. It is because China has long been developed in industries
without high technology or skills; for example, manufacturing and textiles industries.
So, she is more experienced and mature in developing the low-to-medium skilled
products. Yet, there is a decreasing trend in the comparative advantage for
labour-intensive and resource-based manufacture products. The reason is that with the
mature development in the secondary industries, China no longer needs to focus on
producing these products which greatly waste the resources of China. So, the Chinese
Government started to decrease the percentage of these industries in operating in main
parts of China. On the other hand, there is an increasing trend in the comparative
advantage for other secondary industrial products in China; for example, the RCA for
manufactures products with low skill and technology has been increased from 0.100
to 1.521 during 2000 to 2010. And the products with high skills and technology also
doubled in the period. This shows the Government’s effort in encouraging high
technology which somehow improves the trading structure of China. Yet, for Russia,
the situation just reverses. On one hand, it is because of the faster development of
secondary industries in China; on the other hand, it is due to the too much reliance of
primary industries for Russia.
Secondly, for products that Russia has more comparative advantage than China
in this two-country case. They are mainly primary products. And except for iron and
steel, all other primary products have an increasing trend in comparative advantage,
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with the RCA value doubled. This shows that comparatively, Russia developed better
in them, and would focus in exporting primary products.
Thirdly, there is only iron and steel product that both Russia and China do not
have stable comparative advantage in it, and the RCA index for both countries in this
product has been fluctuated. It is because both Russia and China have great
development and export in this field. China has great steel production and
consumption, and her crude steel production has ranked first in the world for 13
consecutive years18
. And Russia's ferrous metallurgy industry investment plans have
improved her development in this type of product19
To conclude, when we consider only two trading members, China has a
comparative advantage in secondary products (for both labour-intensive and
technology intensive), and Russia is more competitive in exporting primary products.
18
餘華彬(2010):〈重估中國鋼鐵工業發展空間〉,《金融實務》,2010 年第 2 期。
19 〈新興國家鋼鐵產業發展:俄羅斯產量迅速增長〉,上海情報服務平臺,2006 年 9 月 19 日。
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VII. Trade Combining Density Index (TCD)
After knowing the revealed comparative advantage of the two countries, we
would look at the Trade combing Density Index, in order to see whether their bilateral
trade relationship is close enough.
TCD was first proposed by Brown in 194720
, and revised by Kojima Kiyoshi and
Yamazawa Ippei21
. It illustrates the degree of bilateral trade contact and degree of
interdependence between two countries, i.e. whether they are greatly relying on each
other in trade). The model can be illustrated as followed:
ab aab
b w
X XTCD
M M
where TCDab = trade combining density index between country a and country b, and
X means exports, (Xab/Xa) means the share of exports of counry a to country b in the
total share of country a, M means imports. And (Mb/Mw) is the share of country b’s
imports from country a as against the total import values that country b imports from
the world.
The greater the value of TCD index, the closer the bilateral trade relationship
between the two countries. For details, if TCDab is greater than 1, it means country a
and b have a close trade relationship, and vice versa. The model can be illustrated as
TCDab, which means the trade combining density index between country a and
country b. We use it to see how close the trade relationship between Russia and China
is.
20
Brown, AJ (1947). Applied Economics. (London: George Allen and Unwin).
21 Ippei Yamazaw(1970). Intensity Analysis of World Trade Flow. Hitotsubashi Journal of Economics,
10(2): 61-90
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In this part, we use the database on international merchandise trade by the
WTO22
, and we have calculated the value of Sino-Russian(TCDCR) and
Russian-China(TCDRC) TCD index in the period 1995 to 201023
.
Table 7: TCD index between China and Russia
1995 1996 1997 1998 1999 2000 2001 2002 2003
TCD CR 0.843 0.815 0.798 0.876 1.024 1.198 1.093 1.067 1.268
TCD RC 1.693 1.755 1.791 1.700 1.634 1.451 1.432 1.428 1.135
(The above calculations of TCDs are based on the statistics from the World Trade
Organization.)
From table 2, we found that the Sino-Russian TCD (TCDCR) index was relatively
low before the year 2000, from 0.7 to 1, which means export relationship of China to
Russia was not close enough. Yet, from 2000 to 2008, it fluctuated from the value of
1.05 to 1.3. So, export relationship for China to Russia is having a closer relationship
than before. It is because China is rising as an international exporting country, she is
also rising as the top three exporters to Russia. Moreover, in the year of 2008 to 2010,
the TCD was relatively lower than before; it may due to the Financial Tsunami that
leads to decrease in bilateral trade.
22
Time Series on international trade, World Trade Organization. Retrieved February 10, 2012, from:
http://stat.wto.org/StatisticalProgram/WSDBStatProgramHome.aspx?Language=E
23 All the data needed for calculating TCD only available from 1995.
2004 2005 2006 2007 2008 2009 2010
1.341 1.347 1.113 1.348 1.181 0.872 1.050
0.921 0.869 0.809 0.634 0.650 0.690 0.542
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On the other hand, before the year 2004, the Russian-China TCD index (TCDRC)
was relatively higher. However, due to China’s entry to the WTO, China’s market has
been opened to the WTO’s members with the decrease or even zero tariffs. Although
the value of Russia’s exports to China was still increasing, the share of China’s
imports from Russia in the total share of her import has been decreased. This is the
main reason which leads to the decrease of TCDRC index.
Table 8: TCD index of China with Japan and USA respectively
1995 1996 1997 1998 1999 2000 2001 2002 2003
TCD CJ 2.954 3.190 2.908 3.210 3.115 2.892 3.070 2.915 2.725
TCD CU 1.119 1.178 1.128 1.222 1.182 1.093 1.099 1.184 1.248
2004 2005 2006 2007 2008 2009 2010
2.550 2.290 2.014 1.905 1.746 1.864 1.696
1.296 1.325 1.352 1.343 1.339 1.452 1.400
(The above calculations of TCDs are based on the statistics from the World Trade
Organization.)
Moreover, from the table above, when we compare the Sino-Russian TCDs with
other world powers, such as the Sino-US and Sino-Japan TCDs, the Sino-Russian
TCD was lower than that of others. This shows that there is still a room for
improvement in Sino-Russian bilateral trade relations.
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VIII. Empirical Model
i. PART 1 – Exports to Russia from China
In the previous parts, we have counted the RCA of them; it only proves that their
trading structure could be complementing with each other, which provides great
opportunity for trade between them. And we have looked at the TCD that the bilateral
trade for two countries could be further improved. Yet, it doesn’t mean that trade
between the two countries is inevitable. So, in this part, we attempt to set up an
empirical model to investigate the role and the importance of each determinant as
suggested by literature on the trade between China and Russia. The reason why we set
this regression model is basically because we want to find out what factor(s) affect
China’s exports to Russia.
To conceptualize our analysis, a set of variables have been designed to test the
hypothesis. In the following, we would have an in-depth look at the exports from
China to Russia first:
1. Model Specification
A reasonable regression model to be considered might be:
X = f (RPER, HIGH, EX, R_C_PRICE, R_TARIFF, FIN1997, FIN2008,
CONTRACT) + εi
Cobb-Douglas function is:
X = (RPER)^b1.(HIGH)^b2.(EX)^b3.(R_C_PRICE)^b4.(R_TARIFF)^b5
.(FIN1997)^b6.FIN2008^b7.CONTRACT^b8
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The empirical regression equation can be written as:
ln(X) = β0 + β1 ln(RPER) + β2 ln(HIGH) + β3 ln(EX) + β4 ln(R_C_PRICE)
+ β5 ln(R_TARIFF) + β6FIN1997 + β7 FIN 2008 + β8 CONTRACT + ei
The expected sign of the coefficients are:
β1 >0, β2>0, β3<0, β4>0, β5<0, β6<0, β7<0, β8>0
2. Description of variables
i. Dependent Variable: LNX
This is the natural logarithm of the real total exports value (using the year 2000
as the base year) of China to Russia.
ii. Independent Variables:
1. LNRPER
This is the natural logarithm of the per capita real GDP of Russia, which implies
her purchasing power in consuming China’s exports.
Traditionally, real GDP of the export partner has been identified as a major
explanatory variable for exports in empirical studies. It is because it has a direct
relationship with the purchasing power of the export partner.
But, in this model, we use per capita real GDP to represent the ability for
consumption of China’s exports, as it is more representative to explain Russia’s
ability to consume per number of population there. And, we use the real GDP at 2000
as the base year for constant price level. Theoretically, when the people generally
have higher income, the ability to consume (include the consumption of imports)
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would increase. So, the export value for China to Russia would also increase. The
expected sign of this variable is positive.
2. LNHIGH
This is the natural logarithm of the real exports value of the high skilled and
technology intensity products (measured at 2000 constant price). As the high skilled
and technology intensity products generally have higher return, i.e. the export value of
these goods would be higher. So, if China exports more high skilled and technology
intensity goods, the exports value is expected to increase. So, the expected sign is
positive.
3. LNEX
This is the natural logarithm of the exchange rate of the Russian Ruble to that of
China’s RMB. Theoretically, when this variable increases, that means RMB would
appreciate, and Ruble would depreciate. This means the price of China’s exports
would be more expensive, which decrease the quantity demanded of it in the Russian
market. So, the expected sign of this variable is negative.
4. LNR_C_PRICE
This is the natural logarithm of the general price level of Russia divided by that
of China. When the price level of Russia to China increases, it means the price of
goods for Russia increase relatively, and that of China decrease relatively.
Theoretically, a higher price level would increase the relative price of domestic
exports to other countries. This results in a decrease in exports. So, increase in this
09010408/China Studies - Economics Option/Honours project (Spring 2012)
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variable means an increase in China’s exports to Russia. The expected sign of this
variable is positive.
5. LNR_TARIFF
This is the natural logarithm of the average tariff rate for Russia imposed on
China’s imports. Theoretically, the tariff imposed on import goods is a cost for
exports. The higher the tariff imposed, the higher the cost of China’s exports, leading
to higher price of the exports of China in Russian market. So, this would lead to a
drop in the export value from China to Russia. The expected sign of this variable is
negative.
6. FIN1997
This is a dummy variable which imply The Asian Financial Crisis. In this
analysis, when it occurs, we take dummy=1, and otherwise=0. When The Asian
Financial Crisis occurred, which somehow affected Russia, it is expected that the
ability to buy the imported goods would decrease. So, this variable would have a
negative effect on the export from China to Russia.
7. FIN2008
This is a dummy variable which imply The Financial Tsunami. In this analysis,
when it occurs (mainly in 2008Q4 to 2009), we take dummy=1, and otherwise=0.
When there is The Financial Tsunami took place, which somehow affected Russia, the
ability to buy the imported China’s goods would decrease. So, it is expected that this
variable would have a negative effect on the export from China to Russia.
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8. CONTRACT
This is a dummy variable which means Russia and China have signed contracts
or agreements on trade issue which facilitate their bilateral trade (dummy=1,
otherwise=0). There would be a positive effect for the signing of these agreements to
the exports of China to Russia.
To obtain a general description of the exports and other possible relevant
determinants, a profile of all variables please sees the table in Appendix 1.
3. Data Type
In this project, we apply the time series quarterly data in the period 1997Q1 to
2010Q4 and run the OLS regression. We would use the backward regression method,
i.e. first test all the variables and testing them one by one for statistical significance.
And then delete any independent variables that are less significant and relatively not
supported by economic principals.
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4. Empirical Results (the final model):
ln(X) = β0 + β1 ln (RPER) + β2 ln (HIGH) + β3 ln (EX) +β4ln (R_TARIFF)
+ β5 FIN1997 + β6 FIN2008 + ei
Dependent Variable: LOG(X)
Method: Least Squares
Date: 04/13/12 Time: 17:06
Sample (adjusted): 1997Q2 2010Q4
Included observations: 55 after adjustments
Convergence achieved after 17 iterations
Variable Coefficient Std. Error t-Statistic Prob.
C -2.176299 1.481123 -1.469357 0.1484
LOG(RPER) 1.744202 0.177604 9.820708 0.0000
LOG(HIGH) 0.348691 0.060427 5.770467 0.0000
LOG(EX) -0.446899 0.111011 -4.025732 0.0002
LOG(R_TARIFF) -0.063805 0.402657 -0.158461 0.8748
FIN1997 -0.498449 0.203833 -2.445378 0.0183
FIN2008 0.122066 0.107299 1.137621 0.2610
AR(1) 0.361033 0.168415 2.143714 0.0373
R-squared 0.981302 Mean dependent var 14.35421
Adjusted R-squared 0.978517 S.D. dependent var 1.121250
S.E. of regression 0.164341 Akaike info criterion -0.640020
Sum squared resid 1.269378 Schwarz criterion -0.348045
Log likelihood 25.60056 Hannan-Quinn criter. -0.527111
F-statistic 352.3792 Durbin-Watson stat 1.934193
Prob(F-statistic) 0.000000
Inverted AR Roots .36
From the above, the Durbin-Watson statistics shows no statistical evidence that
the error terms are positively autocorrelated, as the value of it is 1.934 now. And, all
variables, except FIN2008, show correct sign. But, the p-value for LNR_TARIFF is
high, which means it is not very significant. Also, there are no problems of
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multicollinearity and heteroskedasticity for this regression model (for proves, please
see Appendix 2).
The following is an overall regression table for the 1st to 5
th regression model in
order to let us have a clear look, where (*) means the variable is in 10% level of
significance, (**) means the variable is in 5% level of significance, and (***) means
the variable is in 1% level of significance:
1st 2
nd 3
rd 4
th 5
th
C
(se)
-1.360
(1.259)
-2.120*
(1.232)
-2.199
(1.491)
-2.164*
(1.224)
-2.176
(1.481)
LNRPER
(se)
1.816***
(0.184)
1.694***
(0.178)
1.743***
(0.179)
1.698***
(0.177)
1.744***
(0.178)
LNHIGH
(se)
0.228**
(0.090)
0.374***
(0.052)
0.350***
(0.061)
0.373***
(0.051)
0.349***
(0.060)
LNEX
(se)
-0.691***
(0.145)
-0.467***
(0.092)
-0.446***
(0.112)
-0.470***
(0.091)
-0.447***
(0.111)
LNR_C_PRICE
(se)
0.458*
(0.234)
/ / / /
LNR_TARIFF
(se)
0.044
(0.302)
-0.051
(0.307)
-0.061
(0.404)
-0.035
(0.304)
-0.064
(0.403)
FIN1997
(se)
-0.604***
(0.155)
-0.685***
(0.154)
-0.500**
(0.203)
-0.693***
(0.153)
-0.498**
(0.204)
FIN2008
(se)
-0.016
(0.090)
0.040
(0.088)
0.118
(0.108)
0.047
(0.087)
0.122
(0.107)
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CONTRACT
(se)
0.036
(0.050)
0.036
(0.051)
0.020
(0.044)
/ /
correcting
autocorrelation
N 56 56 55 56 55
Adj R-squared 0.978 0.977 0.978 0.977 0.979
D.W. 1.564 1.452 1.927 1.437 1.934
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ii. Model Implication - Part 1 of the regression
From the above first regression, it gives us some important message - there are
several factors that are significant in affecting China’ exports to Russia, i.e. the
purchasing power of the Russians (the real per capita GDP), the trade barrier (tariff
imposed by Russia), the financial crisis (in 1997 and 2008), the relative price of their
currencies (the exchange rate), and the high skilled and technology intensity exports.
And we also see the ineffectiveness of signing the trade agreements in our model.
Firstly, for tariff, our finding shows that the tariff by Russia is indeed a means to
reduce the exports from China, but it is very insignificant. In fact, although more than
30% of Russian imports have imposed with a tariff rate higher than 25%24
, the high
tariff rate is not so effective to block the exports from China to Russia. The reason is
that the “Grey customs clearance” between China and Russia greatly contribute to the
ineffectiveness of tariff25
. It means the exporters of China signed non-disclosure
agreement with the customs officials, which help to under-report their exports goods
to Russia. For example, underreporting the value and weight or concealed the product
name26
. This helps the exporters to enjoy a low tariff rates, yet it could not be
measured. Also, China and Russia are close to each other, a great number of exporters
would make use of the boarders, and carry duty-free goods from China to Russia in
24〈關稅及通關環節壁壘頻繁〉,《俄羅斯商情》。
Retrieved from: http://blog.mostgroup.com/post/278.html
25 王成剛(2011):〈後危機時代中俄經貿問題研究〉,《黑龍江對外經貿》,2011 年第 10 期。
26 〈俄羅斯下調入境免稅品價值界限衝擊中俄貿易〉,《環球財經》,《星島日報》,2009 年
8 月 26 日。
09010408/China Studies - Economics Option/Honours project (Spring 2012)
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person27
. So, the high tariff rates led to these illegal actions, and indirectly led to its
ineffectiveness in limiting exports from China to Russia.
For GDP, like the following diagram, which is drawn on the assumption that
interest rate is fixed. The accounting identity is savings minus investment equals
exports minus imports, S-I = X-M. Both sides of the identity depend on GDP: higher
GDP means more imports and also more savings. So, increase in GDP of Russia
would increase the imports from other countries (e.g. China).
On the other hand, our finding shows the financial crisis have different
implication on the exports to Russia. Our result shows that the Asian Financial Crisis
in 1997 have a negative impact on the exports to Russia. The explanation of this
phenomenon is that the crisis which took place in Thailand, then spread to countries
like Korea and Japan, which had set up their companies in Russia. The crises led these
companies move out their capital (about USD 100 billion) from Russia to save their
27中國駐俄使館:〈2011 年俄羅斯投資經營障礙報告〉,中俄法律網,2012 年 2 月 22 日。
Retrieved from: http://www.chinaruslaw.com/CN/InvestRu/tz009/2012222223451_782314.htm
09010408/China Studies - Economics Option/Honours project (Spring 2012)
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domestic stock market. This led the stock price of Russia dropped by 30%28
. The
Russian Government, in order to save the stock market, had issued large amount of
Government bonds, and the foreign exchange of Russia dropped rapidly29
. This led to
a small-scale financial crisis within the Russian economy. This economic recession
led the purchasing power of Russians dropped sharply, which had a negative effect on
importing China’s goods.
For the Financial Tsunami in 2008, the regression result shows that this crisis
had a positive relationship with the exports from China to Russia, which do not match
with the economic principal that economic recession would lead to a decrease in
income of Russians, and thus lower the purchasing power of China’s exports. The
explanation for this phenomenon is: (1) China mainly export commodity to Russia30
,
such as shoes, textiles products, which have an inelastic demand for these products.
And the exports price of China were cheaper than that of the Western countries, so the
Financial Tsunami didn’t lead to great drop in China’s exports by Russia. (2) The
Financial Tsunami only led to a short-term decrease in the exports of China by Russia.
Although the exports of China to Russia in January to April 2009 dropped by 46.2%,
the exports to Russia increased by 69% to 296.1 billion in shortly after that31
. This
28
張康琴(1999):〈俄羅斯金融危機〉,東歐中亞研究。
http://wiki.hexun.com.tw/view/3059.html
29 〈俄羅斯 98 年金融危機爆發始末〉,《新聞透視》,俄新社。
http://big5.rusnews.cn/xinwentoushi/20080818/42238501.html
30 王酈久:〈中俄貿易:600 億元的尷尬〉,《中國經營報》,2011 年 6 月 25 日。
31 常玢:〈金融危機背景下中俄經貿合作的影響因素和發展思路〉,《新華財經》,新華網,
2009 年 8 月 16 日。
http://big5.xinhuanet.com/gate/big5/news.xinhuanet.com/topbrands/2009-08/16/content_11891308.htm
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shows that the export value from China to Russia didn’t be attack much by the
Financial Tsunami.
And for exchange rate, as the two countries no longer rely on barter trade;
instead, they implemented the local currency settlement, which lead exchange rate
becomes more important in affecting their products’ price. When the exchange rate of
the Ruble to RMB increases (see figure 6), the prices of China’s exports are more
expensive. So, it is by theory that this leads to decrease in the quantity demanded of
China’s exports in the Russian market.
Figure 6 - exchange rate of the Russian Ruble to that of China’s RMB
(Data generated from: IMF)
Before we move on to the implications in high skilled and technology intensity
products in affecting the export value of China to Russia, we notice the signing of
contracts is insignificant in increasing the exports from China to Russia. The
explanation for this is that a number of inter-governmental cooperation projects were
just in file and protocol level. For example, in recent years, Russia put more emphasis
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on the liberalization of trade with China, but in real situation, she has imposed many
resistances and obstacles to China’s exports. In fact, although a number of trade
contracts were signed, the two countries didn’t make efforts in executing the contract.
Instead, arbitrarily detain goods and capital, and the property security of China is not
fully guaranteed. Moreover, through the two governments promised to improve the
bank settlement system, the arbitration system, the credit insurance system, the quality
monitoring system and customs clearance system; not much efforts had been made
and this lead to ineffectiveness of these systems32
.
Moreover, we discover from the regression result that exporting more high
skilled and technology intensity products help to increase the export value from China
to Russia. This is because of four reasons: (1) the per capita income of Russia or the
GDP of Russia has been increasing. Thus, the purchasing power of Russians to
China’s high-tech products increases. (2) The Russian Government lower or even
cancel the import tariff of high-tech products, due to the need of the country’s
development and the inability for Russia to produce these products with comparative
advantage33
. For example, in 2007, the Russian Government cancelled the tariff on
high-tech products, like machinery, and lowered the tariff rate to 5% for high-tech
construction machines34
. (3) Labour-intensive export products from China have low
reputation in the Russian market. And Russia also targeted on Chinese exports, and
32田春生:〈中俄經貿合作關係新析——經濟利益的視角〉,《俄羅斯研究》,2010 年第 1 期。
33 〈俄取消部分高科技設備進口關稅〉,國際稅訊,中華會計網校。
http://www.chinaacc.com/new/253/263/2008/2/zh452961124162280029056-0.htm
34 〈俄羅斯取消部分高科技設備的進口關稅〉,中俄蒙資訊網,2007 年 6 月 21 日。
http://big5.xinhuanet.com/gate/big5/www.nmg.xinhuanet.com/nmgwq/2007-06/21/content_10361866.
htm
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imposed higher tariff on labour-intensive products. For example, the tariff on textiles
for other countries is 6-7%, but it is 11% for China35
. So, the only way to further
develop the Russian market is to export high skilled and high-tech products with good
quality. (4) Russia is applying for the entry of WTO, and is expected to enter it in
2015, and the WTO agreement indicates that Russia should cancel her tariffs on high
skilled and technology intensity products36
, and cancel the license exporters for these
products37
.
35
李傳勛(2007):〈俄羅斯「入世」對中俄區域經貿合作的影響〉,俄羅斯中亞東歐市場,
2006 年第 10 期。
36 〈2005 俄羅斯貿易投資環境報告〉,中俄法律網,2006 年 1 月 11 日。
http://www.chinaruslaw.com/CN/InvestRu/Law/2006111134032_5253237E02.htm
37趙學信:〈入世後俄羅斯將對進口高科技產品實行零關稅〉,東寧招商網,2011 年 11 月 16 日。
http://www.dongningzs.com/article.asp?id=1502
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iii. PART 2 - High skilled and technological intensity exports
In the previous part, it shows that high skills and technology intensity exports
from China are one of the significant factors that affect China’s exports value to
Russia. As stated in the literature review in previous chapter(李青;2001;韓立華,
2001;唐朱昌,2002;李澤祥、楊定華,2003;陸南泉,2004;任賢慧,2007;
劉紅雨,2008;董銳,2010), changing the exports structure of China to Russia is an
effective method to increase exports value from China to Russia.
So, in this part, we attempt to set up an empirical model to investigate the factors
that determine the proportion of high skills and technology intensity products in total
exports from China to Russia. The reason why we set this regression mode, not only
because it is suggested by literature, but also because the RCA index in previous
chapter shows that China has a comparative advantage in it, and it is easier for China
to adjust the proportion of its exports than other variables (like GDP of Russia).
To conceptualize our analysis, a set of variables have been designed to test the
hypothesis.
1. Model Specification
A reasonable regression model to be considered might be:
HIGH= f (FDI, RD_COST, GOVT, PEOPLE, EX, ENTERPRISE) + εi
Cobb-Douglas function is:
HIGH = (FDI)^b1.(RD_COST)^b2.(GOVT)^b3.(PEOPLE)^b4.(EX)^b5
.(ENTERPRISE)^b6
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The empirical regression equation can be written as:
ln(HIGH) = β0 + β1 ln(FDI) + β2 ln(RD_COST) + β3 ln(GOVT) + β4 ln(PEOPLE)
+ β5 ln(EX) + β6 ln(ENTERPRISE) + ei
The expected sign of the coefficients are:
β1 >0, β2>0, β3>0, β4>0, β5<0, β6>0
2. Description of variables
i. Dependent Variable: LNHIGH
This is the natural logarithm of the real high skilled and technology intensity
exports (using the year 2000 as the base year) of China to Russia. According to the
“China Statistics Yearbook on High Technology Industry”38
, high skilled and
technology intensity products can be divided as electronic information, software,
aeronautics and astronautics, opto-mechatronics, bio-pharmaceuticals and medical
devices, new materials, new energy and energy-saving products, environmental
protection and modern agriculture. We choose the data of “high skilled and
technology intensity exports from China to Russia” as our data, which means
categories 16 (machinery and mechanical appliances), 17 (vehicles, aircraft, vessels
and associated. transport equipment) and 18 (Optical, photographic, cinematographic,
measuring, checking, precision, medical or surgical instruments and apparatus) from
China.
38《中國高新技術產品出口目錄》,中國科學技術部、財政部、國家稅務總局、海關總署,2006
年。
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ii. Independent Variables:
1. LNFDI
This is the natural logarithm of the real value of foreign direct investment (using
the year 2000 as the base year). FDI helps to improve Chinese exports structure,
especially improve export competitiveness of the high-tech products. As suggested by
literature, FDI helps to improve the technical and technology level of the labor-intensive
products and thus improve the export quality(賴明勇、許和連、包群,2003)39. So, the
expected sign is positive.
2. LNRD_COST
This is the natural logarithm of the cost of research and development (using the
year 2000 as the base year). When the R&D cost of China increases, it means China
have more ability to produce products with higher technological level. And in
long-term, it may lower the cost to produce high technology goods, and increase her
comparative advantage in producing it, which favor the increase in high-tech exports.
The expected sign of this variable is positive.
3. LNGOVT
This is the natural logarithm of the Chinese Government’s financial support
(using the year 2000 as the base year) to the development of high skilled and
technology intensity industry. With the Government’s support, it would favor the
development of this industry, and thus increase the exports value of these goods. So,
39賴明勇、許和連、包群(2003):《出口貿易與經濟增長——理論、模型及實證》,三聯書店
出版社。
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the increase in government support is expected to have a positive relationship with the
exports of high skilled and technology intensity products.
4. LNPEOPLE
This is the natural logarithm of the researchers who are involved in research and
development of the high skilled and technology intensity products. With more
professionals involve in this industry, it is expected that it would have a positive
relationship with the increase in export value indirectly.
5. LNEX
This is the natural logarithm of the exchange rate of the Russian Ruble to
that of China’s RMB. Theoretically, when this variable increases, that means the price
for China’s exports would be more expensive, which decrease the quantity demanded
of her exports in the Russian market. It is a variable suggested by literature, which
stated that the appreciation of RMB to other foreign currencies would especially
decrease the exports of the high-tech products rather than the labour-intensive low
technology intensity product(謝裡、隋楊、張婭,2011)40. So, the expected sign of
this variable is negative.
6. LNENTERPRISE
This is the natural logarithm of the enterprises that involved in research and
development and involved in export activities of the high skilled and technology
40謝裡、隋楊、張婭(2011):〈人民幣匯率與商品出口結構——基於中國省際數據的經驗研究〉,
《投資研究》,2011 年 08 期
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intensity products. Generally, when the value of this variable increases, the exports
for this type of product would also increase. The expected sigh is positive.
In order to obtain a general description of the export value of the high skilled and
technology intensity products, and other possible relevant determinants; the following
table is a profile of all variables:
3. Data Type
In this project, we also apply the time series quarterly data in the period 1997Q1
to 2010Q4 and run the OLS regression. We would use the backward regression method
like in the last part.
4. Empirical Results
ln(HIGH) = β0 + β1 ln(FDI)+ β2 ln(RD_COST) + β3 ln(PEOPLE) + ei
Dependent Variable: LOG(HIGH)
Method: Least Squares
Date: 04/13/12 Time: 17:14
Sample (adjusted): 1997Q2 2010Q4
Included observations: 55 after adjustments
Convergence achieved after 9 iterations
Variable Coefficient Std. Error t-Statistic Prob.
C -10.02818 4.263216 -2.352257 0.0226
LOG(FDI) 1.642300 0.571116 2.875597 0.0059
LOG(RD_COST) 0.144744 0.224279 0.645374 0.5216
LOG(PEOPLE) 0.905908 0.491700 1.842400 0.0714
AR(1) 0.941221 0.048158 19.54450 0.0000
R-squared 0.989632 Mean dependent var 12.29686
Adjusted R-squared 0.988803 S.D. dependent var 1.490586
S.E. of regression 0.157728 Akaike info criterion -0.769379
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Sum squared resid 1.243909 Schwarz criterion -0.586894
Log likelihood 26.15792 Hannan-Quinn criter. -0.698810
F-statistic 1193.172 Durbin-Watson stat 1.825302
Prob(F-statistic) 0.000000
Inverted AR Roots .94
This is the final regression model that is most explainable, with adjusted
R-squared equals to 0.989. All variables have correct sign. And only LNRD_COST is
not significant in 10% significance level.
The following is an overall regression table for the 1st to 8
th regression model in
order to let us have a clear look, where (*) means the variable is in 10% level of
significance, (**) means the variable is in 5% level of significance, and (***) means
the variable is in 1% level of significance:
1st 2nd 3rd 4th 5th 6th 7th 8th
C
(se)
-12.017***
(1.933)
-8.853***
(3.063)
-12.286***
(1.870)
-8.805***
(3.029)
-4.582
(3.218)
-10.600**
(4.471)
-4.429
(2.981)
-10.028**
(4.263)
LNFDI
(se)
-2.506***
(0.451)
-1.571***
(0.425)
-2.411***
(0.421)
-1.580***
(0.534)
2.978***
(0.707)
1.569**
(0.591)
2.987***
(0.698)
1.642***
(0.571)
LNRD_COST
(se)
-0.214
(0.147)
-0.179
(0.184)
-0.267**
(0.119)
-0.166
(0.176)
1.015***
(0.206)
0.082
(0.255)
1.005***
(0.192)
0.145
(0.224)
LNGOVT
(se)
1.435***
(0.184)
1.636***
(0.259)
1.411***
(0.179)
1.637***
(0.256)
-0.058
(0.434)
0.185
(0.363)
/ /
LNPEOPLE
(se)
5.061***
(0.522)
4.127***
(0.642)
5.123***
(0.509)
4.114***
(0.634)
-1.018
(0.720)
0.863*
(0.502)
-1.081*
(0.540)
0.906*
(0.492)
LNEX -0.036 0.022 / / / / / /
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(se) (0.059) (0.092)
LNENTERPRISE
(se)
-5.118***
(0.276)
-4.988***
(0.425)
-5.107***
(0.274)
-4.991***
(0.421)
/ / / /
correcting
autocorrelation
N 56 55 56 55 56 55 56 55
Adj R-squared 0.994 0.997 0.995 0.997 0.954 0.989 0.955 0.989
D.W. 0.795 1.833 0.776 1.824 0.352 1.817 0.354 1.825
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iv. Model Implications - Part 2 of the regression
As improving the exports structure by China to Russia is strongly supported by
literature and the above regression result, and these types of products are less
restricted by the trade barrier of Russia as mentioned above. Exporting high-skilled
and technology intensity products to Russia is one of the most effective methods to
increase the export value from China to Russia.
From part 2 of the empirical result, it shows the insignificance of Government
investment on the exports of high-skilled and technology intensity products to Russia.
This is because R&D investment by the Chinese Government accounted for only
1.3% of GNP, while that of the Western countries are usually 2.5%. And investment
in R&D by the Government is mainly on space development and manned space flight;
only very little investment are invested on scientific research or improving the level of
products or the trading structure (less than 6% in the proportion of Government’s
R&D investment, while in Western countries accounted for 20%)41
.
On the other hand, we know that the factors that could affect the export values of
high skilled and technology intensity products are cost for research and development
(R&D), the number of professionals involved in the R&D process, and the foreign
direct investment (FDI).
Yet, from the result, it shows that the cost of R&D is not very significant in
affecting the export value of the high skilled and technology products from China to
Russia, although this is supported by theory. The explanation of its insignificance is
that: (1) the importance for R&D is more encouraged in China only since the year
41〈「中國製造」的高科技主要依賴外資〉,《經濟縱橫》,2005 年 7 月 16 日。
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200042
. So, the achievement for R&D may not be significant, as the technique may be
still not mature enough; (2) R&D cost may be invested in various types of
development, but not only in high-skilled and technology products. For example,
forestry, transportation, software industry, etc43
. So, R&D investment on high skilled
and technology intensity products is only one kind of investment. (3) The products
which developed by R&D process would not be totally exported to Russia. Instead, it
may be exported to other countries with higher purchasing power or that China with
more comparative advantages, or sell in the domestic market. (4) The capital which
put on R&D is still low when compared to other countries (see figure 7), , such as EU
and the US.
Figure 7: R&D expenditure per GDP for major powers
(Source: 2010 Science and Engineering Indicators, the US National Science Board)
42思坦芬•濤特(2007):〈中國的創新經濟:為中國成為世界領先者奠定基礎〉,Medstat Research。
43羅彥平(2011):〈中國研究與發展投入產出效率分析〉,《經濟與管理》,2011 年第 6 期。
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Moreover, the regression result also shows that the number of researchers
involved in R&D is, to a large extent, significant in affecting the exports of
high-skilled and technology intensity products. The reason for that is probably
because the number of PhDs, masters and professionals for China rank first
throughout the world44
. And China keeps sending students to foreign countries since
198545
. For example, over 80% of students from Tsing Hua University who are
major in high-tech products are sent to USA for further studies, and over 76% of that
from Beijing University46
. Also, average annual growth rate of researchers in China
ranked first in the world (see figure 8). The large amount and high-quality of
professionals probably leads the research and development process more efficient in
practice, and thus contributes to the growth in exports of high skilled and technology
products.
44
“IMD World Competitiveness Yearbook 2007”, International Institute for Management
Development (IMD), 10 May 2007.
45王輝耀(2010):〈中國人才戰略須著眼於攬全球人才為我所用〉,《綠葉》。
46《中國科技人力資源發展研究報告 2008》,中國科學技術協會發展研究中心。
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Figure 8: Average annual growth rates in number of researchers for major powers
(in 1995-2007)
(Source: 2010 Science and Engineering Indicators, the US National Science Board)
And for the FDI, the regression result shows that it is the most significant in
affecting the dependent variable. It is supported by many researches. FDI not only
improves the exports of labor-intensive products, but also increases the high
value-added technology-intensive exports if more technical input is invested. It also
leads to multinational vertical and horizontal integration; which increases the
opportunities for exporting high value-added products(賴明勇、包群,2002)47. This is
also supported by Bergsten. C.F.(1978) that in the US case, the more the FDI invested
in a particular high-tech industry, the more the export value of that industry. FDI also
promotes the improvement in the structure of export commodities, and enhance the
competitiveness of export commodities(江小娟,2002)48.
47賴明勇、包群:〈中國外商直接投資與技術進步的實證研究〉,《經濟評論》,2002 年第 6 期,
第 62-66 頁。
48江小娟:〈中國出口增長與結構變化:外商投資企業的貢獻〉,《南開經濟研究》,2002 年第
2 期,第 30-34 頁。
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v. Model Limitation and Suggestion
There are two limitations in this research report, namely the data source and the
size of the data base.
1. Data Source
The data of the variables are from different organizations (e.g. IMF, the National
Bureau of Statistics of China, etc). The calculations and measurement of different
variables from these organizations might be a bit different. Although I have tried to
take all data from the National Bureau of Statistics of China, updated data for some
variables are not free in use. That makes us take the data from other international
organization, or find other similar variables to replace it. The suggestion for future
study is to use other variables to replace them, which are updated and completely free.
Also, more data will be available for future study when times go on.
2. Size of Data Base
The availability of data source only allows us to take data from 1997Q1 to 2010Q4.
Since the dissolution of Soviet Union took place in late 1991, which means the
establishment of the Russian Federation; it is better to take data from the year 1992 if
more data will be released in the future. This leads our model more accurate in
reflecting the real situation of Sino-Russian trade.
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vi. Suggestion to the Sino-Russian trade
The above sections have shown us the Sino-Russian trade can be improved in
order to make China able to grab more proportion of the Russian market. So, in this
section, we would make suggestions on how China could increase and stabilize her
exports to Russia.
Firstly, literatures and part 1 of our regression have shown us the most effective
way to increase the exports from China to Russia– to export more high-skilled and
technology intensity products. The RCA index also shows us that China have a
comparative advantage in exporting these products in their trade. In fact, this
suggestion is not pushing China to do something that she couldn’t. Because figure 9
shows us that China’s exports of high-skilled and technology manufacturing products
to her trading partners have surpassed the US in 2004, and surpassed the EU in 2006.
And, Russia had implemented prohibitive tariffs towards China’s cheap and
labour-intensive exports in order to protect her domestic industry, e.g. she
implemented prohibitive tariffs on clothing, textiles and footwear in 2010. Moreover,
over 10% of China’s exports are subjected to high tariffs (more than 25%), such as
plastic products. And textile fabrics, garments and sugar even have an average import
tariffs up to 30%. It is only the high skilled and technology-intensity products that the
Russian Government imposed a lower tariff rate, as she needs these imports for local
development (as Russia focus too much on producing capital goods throughout the
years). So, increasing the proportion of high-skilled and technology intensity products
would lead China’s exporters enjoy a lower tariff rate by Russia. Moreover, Russia
has high demand in China’s high-tech products, such as products of electronic
appliances manufacturing technology, computer production technology, production
technology of communications equipment, engineering machinery production
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technology, etc. By our regression result, the Chinese Government could achieve
exporting more high-skilled and technology intensity products by increasing the
inputs (capital and researchers) for R&D of export products, and attract more FDI in
high-skilled and technology fields (like the “vacating cage to change bird” policy (騰
籠換鳥) by the Guangzhou government).
Figure 9: Exports of high-technology manufactured goods by major powers (in
1995-2008)
(Source: 2010 Science and Engineering Indicators, the US National Science Board)
Secondly, China could export high value-added products. The export structure
of China to Russia is rather simplistic now, i.e. she exports mainly food, clothing,
textile and other necessities to Russia. For example, textile, clothing, footwear and
leather goods accounted for 52.03% of her exports to Russia, and although higher
technology products, such as mechanical and electrical products, has accounted for
only 15.7% of her exports to Russia. Yet, a large proportion of them were just
electronic home appliances which are not high value-added. Unlike those exports
from the European countries to Russia, exports from China to Russia are mainly in
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low-to-medium class. As shown in figure 10, the GDP for Russia is no longer as low
as she was just dissolute in 1992; instead, the GDP(at PPP) has increased dramatically
throughout the years, and reached USD2100 billion in 2010. This shows the
purchasing power of Russians for high quality products with higher price has been
increased. Also, with the establishment of the Russian market system, her economic
recovery and the rise of the living standard of Russians; the consumer's preferences
and tastes has been improved, they now prefer higher quality products. That explains
why the European countries who produce goods with better quality are grabbing more
proportion of the Russian market. So, as the demand for Russians is directed to
medium-to-high class products, China could supply more of them to meet the
increasing Russians’ demand. Also, Chinese exporters could establish brand name on
these products in order to create an image of good quality and make them high
value-added.
Figure 10: GDP (at PPP) of Russian Federation (1992 to 2010)
(Source: International Monetary Fund)
Thirdly, many researchers suggest the establishment of free trade area (FTA). In
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fact, FTA would lead to a better flow of factors of production between the two
countries, and improve the efficiency of resources allocation. And the two countries is
in fact have good foundations in establishing FTA, e.g. stable political environment,
the longest border (4300 km), and exporters have used Heilongjiang as the base of
border trade for many years, etc. The establishment of FTA leads their trade closely
connected.
The forth suggestions is to enforce the trade agreements and put it in progress.
Our regression result in part 1 has shown the ineffectiveness of the signing of trade
agreements between the two countries. We have already explained with examples that
many trade agreements are just in file and protocol level, but not under strict
enforcement. In fact, many of these agreements, if they can put in progress, would be
effective in helping the development of Sino-Russian trade. Examples include
liberalization of trade with China, improving the bank settlement system, etc. These
trade agreements would, to a large extent, help to facilitate the Sino-Russian trade.
Moreover, the two Governments should fight against the problem of “Grey
customs clearance". Although it leads the Chinese exporters could use a lower cost to
transport products to Russia by bribing the Russian Customs union, these
underground activities lead the low quality or even fake products could enter the
Russian market. In long-term, this destroys the reputation of “made in China’s
products”, and lead to a bad image of China’s exports.
Last but not least, the law and legislation of trade in Russia should be improved.
One of the existing problems(see figure 11) is that the laws and regulations between
the two countries are not perfect, e.g. the operation of banking institutions are not
standardized, the customs clearance ability is low, the imperfect trade arbitration
mechanism also makes trade disputes difficult to resolve; and the inconsistence of tax
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and tariffs of Russia lead confusion. These problems must be solved by implementing
laws and regulations with strong enforcement in order to keep the good trade
relationship between the two countries and to facilitate the Sino-Russian trade.
Figure 11: Problems incurred in Sino-Russian trade
(Source: 王靜(2003):〈中俄關係:一瘸一拐〉,《經濟月刊》,頁75。)
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X. Conclusion
All in all, although the scale of Sino-Russian trade may not be that large and
strong like China with the EU and the US now, and China could hardly rely on the
trade with her to face another financial crisis in this moment, Russia’s growth as
China’s major trading partner should not be overlooked because their trade does have
a great potential to further develop in the future.
Our empirical studies show that China could further export to Russia with a
greater trade value in the future. As Russia is growing in her economy and her GDP,
she has greater purchasing power in consuming medium-to-high level products; China
could further grab the market of Russia by exporting more high-skilled and
technology intensity products, which can be achieved by attracting more FDI,
increasing the expenditure of R&D and increasing the number of researchers as
suggested by our regression model.
In their future trade development, they could make use of their geographical
advantage (long boarder and near to each other), comparative advantages and matched
trading structure, and to make improvements of their problems involved in their trade.
By doing so, they could probably achieve the bilateral trade goal that suggested by
President Hu Jintao in 2011, which is USD 1000 billion in 2015, and USD2000
billion in 2020. Thus, China could further rely on the trade with this BRIC’s member
in the coming future.
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XI. Appendix
i. Part 1 – Description of variables
Variable Unit Description Source Expected
sign
LNX
(dependent
variable)
USD Log value of the total real
exports value of China to
Russia (measured at 2000
constant price)
China Statistical
Yearbook
1. LNRPER USD
(in ten
billions)
Log value of the per capita
real GDP of Russia
(measured at 2000
constant price)
China Statistical
Yearbook
(Appendix), China
INFOBANK
(International)
Limited.
+
2. LNHIGH USD (in
thousands)
Log exports value of the
high skilled and
technology intensity
products from China to
Russia (measured at 2000
constant price)
United Nations
Conference on trade
and development
(UNCTAD)
+
3. LNEX % in USD Log value of exchange
rate of Russian Ruble to
RMB
International
Monetary Fund
(IMF)
-
4. LNR_C_PRICE % in USD Log value of general price
level of Russia divided by
the price level of China
China Financial
Statistics Yearbook,
China Statistical
Yearbook
(Appendix),
China INFOBANK
(International)
Limited.
+
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5. LNR_TARIFF % Log value of average tariff
for Russia imposed on the
China’ s exports
China Customs
Statistics Yearbook,
China Financial
Statistics Yearbook
-
6. FIN1997 dummy The Asian Financial crisis
occurs=1, otherwise=0
WiseNews -
7. FIN2008 dummy The Financial Tsunami
occurs=1, otherwise=0
WiseNews -
8. CONTRACT dummy signed contracts or
agreements on trade issue
= 1, otherwise = 0
WiseNews +
ii. Test regression for part 1
We will test whether the 5th
regression model involves multicollinearity. From
below, the VIF for all variables is less than 10, meaning there is no problem of
multicollinearity.
Variance Inflation Factors
Date: 04/13/12 Time: 17:09
Sample: 1997Q1 2010Q4
Included observations: 55
Coefficient Uncentered Centered
Variable Variance VIF VIF
C 2.193727 1824.077 NA
LOG(RPER) 0.031543 1446.456 4.399975
LOG(HIGH) 0.003651 468.8821 6.762520
LOG(EX) 0.012323 15.76799 2.339237
LOG(R_TARIFF) 0.162133 730.8511 3.107189
FIN1997 0.041548 2.084872 2.017156
FIN2008 0.011513 1.178069 1.098952
AR(1) 0.028364 1.635013 1.634078
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In order to ensure there is no problem of heteroscedasticity, we use the
Breusch-Pagan-Godfrey Test (LM test):
H0: homoscedasticity var(εi) = 2
H1: heteroscedasticity var(εi) = 2
Heteroskedasticity Test: Breusch-Pagan-Godfrey
F-statistic 1.817018 Prob. F(6,48) 0.1156
Obs*R-squared 10.17987 Prob. Chi-Square(6) 0.1173
Scaled explained SS 4.926118 Prob. Chi-Square(6) 0.5533
Test Equation:
Dependent Variable: RESID^2
Method: Least Squares
Date: 04/13/12 Time: 17:10
Sample: 1997Q2 2010Q4
Included observations: 55
Variable Coefficient Std. Error t-Statistic Prob.
C -0.129765 0.183004 -0.709081 0.4817
LOG(RPER) 0.045139 0.026665 1.692790 0.0970
LOG(HIGH) -0.013804 0.007695 -1.793952 0.0791
LOG(EX) 0.011311 0.013590 0.832315 0.4094
LOG(R_TARIFF) -0.011005 0.045298 -0.242941 0.8091
FIN1997 0.043467 0.024177 1.797847 0.0785
FIN2008 -0.011353 0.012993 -0.873794 0.3866
R-squared 0.185089 Mean dependent var 0.023080
Adjusted R-squared 0.083225 S.D. dependent var 0.026815
S.E. of regression 0.025675 Akaike info criterion -4.368209
Sum squared resid 0.031641 Schwarz criterion -4.112730
Log likelihood 127.1257 Hannan-Quinn criter. -4.269413
F-statistic 1.817018 Durbin-Watson stat 2.022529
Prob(F-statistic) 0.115635
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From the above result, it shows that the p-value is larger than 0.05, meaning F*
or the Breusch-Pagan-Godfrey (BPG*) is smaller than the critical F. So, we do not
reject the assumption that homoscedasticity is exists, meaning that there is no problem
of heteroscedasticity.
iii. Part 2 – Descriptions of variables
Variable Unit Description Source Hypothesis
LNHIGH
(dependent variable)
USD
( in
thousands)
Log value of the total
real exports value of the
high skilled and
technology intensity
products of China to
Russia (measured at
2000 constant price)
United Nations
Conference on
trade and
development
(UNCTAD)
1. LNFDI USD (in
billions)
Log value of the real
value (measured at 2000
constant price) of foreign
direct investment(FDI)
中國商務年鑒 +
2. LNRD_COST USD
(in ten
thousands)
Log value of the real
value (measured at 2000
constant price) of R&D
cost for China
中國科技統計年鑒 +
3. LNGOVT USD(in
ten
thousands)
Log value of the real
value (measured at 2000
constant price) of
Government support in
this industry
中國科技統計年鑒 +
4. LNPEOPLE Person Log value of the
researchers involved in
the R&D development
中國科技統計年鑒 +
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5. LNEX % in USD Log value of exchange
rate of Ruble to RMB
International
Monetary Fund
(IMF)
-
6. LNENTERPRISE Enterprise Log value of the
enterprises involved in
the R&D development
which also involved in
exports activities
中國科技統計年鑒 +
iv. Test regression for part 2
In order to ensure there is no problem of heteroscedasticity, we use the
Breusch-Pagan-Godfrey Test (LM test):
H0: homoscedasticity var(εi) = 2
H1: heteroscedasticity var(εi) = 2
Heteroskedasticity Test: Breusch-Pagan-Godfrey
F-statistic 0.549157 Prob. F(3,51) 0.6510
Obs*R-squared 1.721088 Prob. Chi-Square(3) 0.6323
Scaled explained SS 8.114081 Prob. Chi-Square(3) 0.0437
Test Equation:
Dependent Variable: RESID^2
Method: Least Squares
Date: 04/13/12 Time: 17:15
Sample: 1997Q2 2010Q4
Included observations: 55
Variable Coefficient Std. Error t-Statistic Prob.
C -0.415488 0.730947 -0.568425 0.5722
LOG(FDI) -0.157981 0.171739 -0.919891 0.3620
LOG(RD_COST) 0.020892 0.048198 0.433453 0.6665
LOG(PEOPLE) 0.068035 0.132574 0.513184 0.6100
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R-squared 0.031293 Mean dependent var 0.022617
Adjusted R-squared -0.025690 S.D. dependent var 0.077097
S.E. of regression 0.078081 Akaike info criterion -2.192197
Sum squared resid 0.310927 Schwarz criterion -2.046209
Log likelihood 64.28543 Hannan-Quinn criter. -2.135743
F-statistic 0.549157 Durbin-Watson stat 2.115170
Prob(F-statistic) 0.650997
From the above result, it shows that the p-value is larger than 0.05, meaning F*
or the Breusch-Pagan-Godfrey (BPG*) is smaller than the critical F. So, we do not
reject the assumption that homoscedasticity is exists, meaning that there is no problem
of heteroscedasticity.
Then, we will test whether the 8th
regression model involves multicollinearity.
From below, the VIF for all variables is less than 10, meaning there is no problem of
multicollinearity.
Variance Inflation Factors
Date: 04/13/12 Time: 17:16
Sample: 1997Q1 2010Q4
Included observations: 55
Coefficient Uncentered Centered
Variable Variance VIF VIF
C 18.17501 138.8251 NA
LOG(FDI) 0.326174 71.21707 2.077844
LOG(RD_COST) 0.050301 111.7440 2.170501
LOG(PEOPLE) 0.241769 347.5793 3.107665
AR(1) 0.002319 1.471542 1.249783
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7. United Nations Conference on trade and development (UNCTAD)
8. International Monetary Fund (IMF)
9. China Statistical Yearbook On Science And Technology(中國科技統計年鑒)
10. China Commerce Yearbook(中國商務年鑒)