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Convergence or divergence from Comparative Advantage

A Comparison of China and the European Union

Bushra Riaz1

Abstract:

We examine the comparative advantage convergence and divergence pattern between China

and the European Union (EU) over the last two decades for a variety of sectors. Using seven

measures of revealed comparative advantage for China and the EU we assess their validity in

order to understand how China's rapid growth has changed its pattern of trade as well as that

of the EU. Applying OLS Galtonian, Quantile and Robust regressions we check the

convergence or divergence pattern. We find that China is gaining a lot by its comparative

advantage in high technology sectors as well and there was a rapid change in its traditional

comparative advantage. Furthermore, China is diverging towards high tech sectors. In

contrast, there is no convergence or divergence in the trade specialization pattern of the EU

and remains more or less stable.

Key words: Comparative advantage, Convergence

JEL Classification: F1, F10

1 Introduction

China's rapid growth and rise as the world's largest supplier of goods is one of the most

notable changes to the global economy in the last three decades. There is a 500 percent

increase in China´s real exports over the last 2 decades. As a consequence, in 2013, China

became the world’s leading exporter of goods. In 2014, China export goods valued at total of

2,342,3 billion USD in the year. This essay decomposes the current pattern of its comparative

advantage in comparison with the EU. China established its trade relations with European

Union (EU) in 1975 and is currently the EU's top supplier of goods and its third largest export

market (Deutsche bank research, 2014). Although China is a single fast growing developing

country while the EU is a highly developed region that includes 28 individual countries, China

has the largest gain in world market share. It is a country that is relatively scarce in capital,

skill and land but still gaining significantly from its exports and comparative advantage. The

main aim of this study is to compare the EU and China in terms of their revealed comparative

advantage in order to get a clear picture of trade specialization and their tendencies towards

1 Bushra Riaz, PhD student , I am thankful to my supervisors Dr Elisenda Paluzie and Dr Kristian Estevez

for their useful guidance Universitat de Barcelona, Avinguda Diagonal 690, Barcelona, Spain 08034;

briaz786@gmail.com I would also like to thank Jonathan Colmer from LSE for his valuable comments

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their current comparative advantage. The motivation behind this study is to understand and

to evaluate how China's world market share is rapidly increasing and how it is affecting the

EU's export market and its current comparative advantage in different sectors. Most recently

China has also shown a comparative advantage in capital intensive sector such as office

machines and tele-communications versus its main trading partners. This technological

upgrading has led to highly internationalized and competitive industries (including the

electrical machinery domain) being capable to sell their exports to the developed economies.

In this paper, we shall investigate the evolution of trade flows between China's international

trade partners, which has grown steadily since the utilization of the opening-up policy, both

exports and imports rising greatly. China relies heavily on export-led increase; in particular,

there has been a shift from resource-and labour-intensive to capital- and technology-intensive

exports. We shall try to answer the following questions: Are China and the EU converging

towards a more homogeneous international trade pattern? Is the EU losing its existing

comparative advantage? To analyse the changes in comparative advantage, we will compare

and analyse various indices of revealed comparative advantage for both regions. Moreover,

we will apply beta convergence method to look at EU and China's convergence and

divergence pattern.

As China becomes the world's largest export country, one would expect that EU will

lose grounds in their traditional sectors and that it will converge to the revealed comparative

advantage neutral point i.e. no revealed comparative advantage/disadvantage. It has been

claimed that developed countries like the EU should be more despecialized relative to

developing countries like China. This is an argument made by De Benedicits et al. (2009) who

made an empirical analysis for 39 different countries over a period of 17 years and showed

that on average, countries diversify and do not specialize. In our case, more diversification

means that the EU loses its comparative advantage in a few sectors after a long experience of

comparative advantage in traditional sectors, but it improves its trade position in other sectors

like Pharmaceuticals, chemicals and automotive sectors. Some authors use the term

despecialization instead of diversification (e.g. Worz (2005) and Ferto and Soos (2008)). In

order to check for convergence or divergence, we use Galtonian regressions to compare the

changes in the past and current comparative advantage. Hinloopen and Marrewijk (2004) and

Sanidas and Shin (2011) also use Galtonian regression for comparative advantage analysis.

Since China is still the developing country in relation to the EU, we can ask whether

China is trying to imitate the EU in terms of the goods it exports? The idea developed in this

paper clarifies that what matters for China's long term sustainability is not only the volume

of exports and its relation to its GDP, but its long term hold in its real comparative advantage,

as the productivity level is much higher as compared to other similar countries. It is for this

reason that China's trade is regarded as challenging advanced countries (Rodrik, 2006). Schott

(2008) also views China as special in its level of sophistication in exports.

In this essay we use various indices and choose the best among them, i.e. normalised

revealed comparative advantage index, which is used in the most recent research papers like

Yu et.al. (2009) and Sandias and Shin (2011). The previous literature has revealed that this

rigorous comparison has not been considered so far. Still, there are some similar studies about

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EU-China trade2 and China's exports enhancement and comparative advantage in labour and

capital intensive goods3. Therefore, this study makes an effort to fill this gap in the literature

by focusing on convergence and divergence pattern of comparative advantage. We will check

and compare China and the EU's comparative advantage patterns more recently and how and

where (in which sectors) they are similar or dissimilar with each other.

Debate on China's export market share in the EU and other developed and developing

countries has been rising in the recent decade. Some economists believe that the end of

manufacturing has come in developed countries like those of the EU while others think this

will only hold for low and middle-income countries (Schott, 2008). Therefore, this study is

timely to check the structure of comparative advantage of China in comparison with the EU,

and the magnitude with which these two economies compete with each other in the world

export market for labour-intensive and capital-intensive goods. We use WTO trade data

covering 11 different sectors in order to create various indices of RCA and analyse some

quantitative methods, using Galtonian regressions, to make a comparative evaluation of

China and the EU in terms of their trade patterns and export performance from 1990-2013

and 2000-2013 for China and EU, respectively.

The theory of comparative advantage is old but still relevant to compare a country's

factor endowment structures and their trade patterns. However, the analytical approaches that

measure the comparative advantage of countries over other countries engaged in the

production and sale of commodities have changed, expanded and improved over time. The

Ricardian theory finds that trade takes place where countries face differences in labour

productivity while the Heckscher-Ohlin (H-O) theory is about the differences in factor

endowments and intensities. In the new trade theory, the intra industry trade takes into

account two main assumptions of imperfect competition and increasing returns to scale. As

Krugman (1979) developed “a simple general equilibrium model of non-comparative

advantage trade”, nevertheless he used that term in its traditional sense, which can be taken

as a broader notion of comparative advantage (Sanidas, 2011). This notion can be found in

the existing literature as 'internal returns to scale as a source of comparative

advantage'(Tybout, 1993) and also as a 'Product differentiation as a source of comparative

advantage' (Hummels and Levinsohn, 1993). Palley (2008) has also considered these

conceptual differences.

The passage from comprehensive trade theories to measuring comparative advantage

has always been difficult. The major breakthrough was made by Balassa's (1965) Revealed

Comparative Advantage Index (BRCAI), which is still the most widely used index to analyse

trade performance, although it has been criticised for its ordinal ranking and non-

comparability across countries or sectors. Therefore, like Sanidas and Shin (2011), I calculated

alternative indices to overcome the shortcomings of the Balassa Index. Similar to Ballance et

al. (1987), Seyoum (2007) and Yeats (1985), Sanidas and Shin (2011) have systematically

compared alternative indices and formulated a strategy to accurately use various indices which

2 Liang and Xu (2004), Schott (2008), Y Li (2008), Zhuang and Koo (2008), Harrigan and Deng (2008), Berger and Martin (2013), Xudong (2013), Fu and

Mu (2014), G.W. Ren (2014). 3 Li et.al. (2006), Freytag (2008), Li (2009), He and Mu (2012),

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will be applied in this paper as to make an analysis of comparative advantage between China

and the EU.

The remainder of this paper is structured as follows: Section 2 provides a theoretical

background of revealed comparative advantage and explains the various indices of revealed

comparative advantage. Section 3 gives a brief review of the studies that have made

comparisons of China and the EU. Section 4 provides information about nature and sources

of data. Section 5 explains the methodology of our analysis. Major findings of the regression

techniques are provided in Section 6. Section 7 concludes and discusses about future

extensions.

2 Revealed Comparative advantage

The concept of comparative advantage is one of the few concepts in economics that is more

than common sense. The beauty of this theory is that it illustrates how even a country having

no absolute cost advantage in any sector can benefit from trade by specializing in industries

at which it is 'least bad' (Lin and Chang, 2009).

Even having a strong theoretical concept of comparative advantage, problems arise

when this concept is analysed empirically. Empirical researchers have been looking for

measuring comparative advantage by using existing post-trade volume data, exports and

imports, and is known as revealed comparative advantage. Balassa (1965) proposed an index

which has been widely used to determine the revealed comparative advantage of a country's

exports to the rest of the world or a reference country. The Balassa index suffers, though,

from a weak theoretical foundation and empirical distribution. Due to its inconsistency and

poor ordinal ranking property, numerous attempts have been made to overcome the

deficiencies of the Balassa index; The Lafay index (1992), the symmetric revealed comparative

advantage index (1998), the weighted revealed comparative advantage index (1998), the

additive revealed comparative advantage index (2006) and the normalized revealed

comparative advantage index (2009).

Changes in comparative advantage should reflect changes in factor endowment.

Changes in comparative advantage can also be brought about in cases where the state played

a key role in determining the social and economic conditions. Admitting that the RCA is not

a perfect measure, as it fails to distinguish between a region's factor endowment and changes

in trade policy, economists still believe that the RCA measure are still acceptable as the impact

of changes in trade policies can be seen from movement of the RCA index. (Bender and Li,

2002)

In theory, comparative advantage depends on pre-trade relative prices of commodities

which are unobservable in the real world. Analysts only have access to post-trade data.

Although Liesner (1958) was the first to utilize an index of revealed comparative advantage,

the most frequently used measure in this respect is the Balassa Index, after the refinement

and popularization by Balassa (1965, 1989). It is broadly applied to establish a country's

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efficient sectors and to explore the competency of the strong sectors of the economy.

Therefore, the Revealed Comparative Advantage is measured by the relative value of a

product in total exports of a country divided by the product's relative value in total exports

of the world.

In order to estimate revealed comparative advantage using indicators derived from

real post-trade observations (see for example Kojima 1970; Yamazawa 19704; and a number

of studies have employed the theory of comparative advantage. Given a group of reference

countries the Balassa Index measures normalized export shares, where the normalization is

with respect to the export share in the same industry in the group of reference countries,

which can be written as:

𝐵𝑅𝐶𝐴𝐼𝑐,𝑗 =

𝑋𝑐𝑗

𝑋𝑐𝑡𝑋𝑤𝑗

𝑋𝑤𝑡

(1)

here BRCAIcj is the Balassa revealed comparative advantage index for country c (China or

EU) in sector j, Xcj is the exports of country c in sector j, and Xct is the total exports of country

c (China or EU), Xwj is the total world exports in sector j and Xwt is the total world exports.

If the BRCAI takes a value greater than 1, it indicates that the country has a

comparative advantage in that sector. In contrast, if the index is less than one then it indicates

that the country has a comparative disadvantage in that sector. Michael Porter (1990) defined

Balassa Index above 1, in some cases strengthened to a Balassa Index exceeding 2, to identify

a country's strong sectors. Some other empirical examples which make use of the Balassa

index are Ariovich (1979), Reza (1983), Yeats (1985), Peterson (1988), Crafts (1989), and

Amiti (1999) and Fertö, Imre& Hubbard, L. J. (2003)5.

2.1 Proposed Alternative indices for Revealed Comparative Advantage

Due to some shortcomings like the variable mean of the Balassa index, some alternative

methods were proposed to control the original index around a stable mean which have some

symmetric distributions. Some authors took the logarithm value, whiles other computed the

index in a different way. The first indicator of a sector's contribution to the trade balance,

proposed by Lafay (1992) to check the comparative advantage, is presented:

𝐿𝑅𝐶𝐴𝐼𝑐,𝑗 = 100 [𝑋𝑗 − 𝑀𝑗

𝑋𝑗 + 𝑀𝑗−

∑ 𝑁 (𝑋𝑗 − 𝑀𝑗)𝑗=1

∑ 𝑁 (𝑋𝑗 + 𝑀𝑗)𝐽=1

]𝑋𝑗 + 𝑀𝑗

∑ 𝑁 (𝑋𝑗 + 𝑀𝑗)𝐽=1

(2)

4 Donges and Riedel 1977, Balassa 1979, 1989, Hillman 1980, Bowen 1983, Balance et. al. 1987, Yeats 1985, Tan 1992, Memedovic 1994, Son and Wilson 1995, Maule 1996, Bender and Li 2002, Havrila and Gunawardana 2003, Gunawardana and Khorchaklany 2007. 5 For more details on the Balassa index- see Kunimoto (1977), Hillman (1980), Bowen (1983), Balance et al. (1987), and Vollrath (1991).

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where again c represents the country and j the sector. X and M are exports and imports

towards and from the rest of the world, respectively N is the total number of sectors.

According to the Lafay index, positive values indicate the existence of comparative advantage

in a specific sector; higher values point to a higher degree of specialisation. On the contrary,

negative values indicate comparative disadvantage.

Fagerberg (1995) proposed the symmetry revealed comparative advantage index

(SRCAI) by using a small number to modify the BRCAI.

𝑆𝑅𝐶𝐴𝐼𝑐,𝑗 =𝑅𝐶𝐴𝑐𝑗 − 1

𝑅𝐶𝐴𝑐𝑗 + 1 (3)

This is an estimation of a log transformation of the Balassa revealed comparative advantage

index (Benedictis and Tamberi 2001) which ranges from -1 to +1 where 0 appears when a

country is at comparative advantage neutral point.

Proudman and Redding (1998) calculated the sectoral average share of a country with

world exports taking as denominator, which is known as weighted revealed comparative

advantage index (WRCAI).

WRCAIc,j =BRCAIcj

1N ∑ BIcj

NJ=1

(4)

where N is the number of sectors or commodities. This does not correct the uneven

distribution of the BRCAI index and is still not suitable for dynamic analysis. Furthermore,

this transformation makes the comparative advantage neutral point sensitive to the

classification of sectors (Benedictis and Tamberi 2001).

Hoen and Oosterhaven (2006) proposed an additive form of Balassa revealed

comparative advantage index, indicating the problem in the multiplicative form of BRCAI

and suggested a formula to overcome the previous problem, which is Additive revealed

comparative advantage or Additive index (AI).

𝐴𝑅𝐶𝐴𝐼𝐶,𝐽 = 𝑋𝑐𝑗

𝑋𝑐−

𝑋𝑤𝑗

𝑋𝑤 (5)

In this index, the values fall between -1 and +1 and zero stands as the comparative advantage

neutral point. This index has many advantages in cross-sector analysis but not in the case of

cross country comparison. Moreover, there is no stable cardinal value for a country where it

has no specialization in a sector or not fully specialised in that sector, which should technically

be at the lower and upper bounds of the index, respectively.

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With regard to the instability of additive index (AI), Yu et al. (2009) developed a similar index

and normalised the term with total world exports. That is;

𝑁𝑅𝐶𝐴𝐼𝑐,𝑗 = ∆𝑋𝑐,𝑗

𝑋𝑤=

𝑋𝑐,𝑗

𝑋𝑤 −

𝑋𝑐𝑋𝑤,𝑗

𝑋𝑤𝑋𝑤 (6)

The above index ranges from -0.25 to 0.25, zero being the comparative advantage neutral

point. The NRCAI is symmetrical and also follows a normal distribution. Though the above

indices overcome the shortcomings of Balassa revealed comparative advantage index however

only the NRCAI gives the correct results, hence we will use the NRCAI in our further analysis.

2.2 Revealed Comparative Advantage: The normalised verses Balassa Index

As stated earlier, the revealed comparative advantage indices have some pros and cons;

however, the statistical properties are checked to develop a strong representation of positive

features as a revealed comparative advantage index. Firstly, we check the corresponding

residuals by some normality assumption by applying Shapiro-Wilk, Anderson-Darling and

Kolmogorov-Smirnov tests. The following table present the properties of Balassa and

Normalised index:

Table 1 Properties of Balassa and Normalised index

Comparative

Advantage Neutral

Point

Sum Over

Sectors

Sum Over

Countries

Symmetry Normality

BRCAI 1 Not constant Not constant X X

NRCAI 0 0 0 √ √

3. Background

Export share and trade performance of China is highly argued in the literature6, because

the China's export performance is better than the rest of the world after the financial

crisis and especially from 2008-2010. In 2009, China became the leading global exporter

and in 2010 passed Japan as the second largest economy in the world. In China, labour

intensive industries benefited from its accession to WTO. Capital intensive industries

particularly, iron and steel benefitted from government subsidies (Berger and Martin

2013).

The success of China's transformation from low to middle income country will

be of crucial importance for China to avoid the middle-income trap and sustain its long

term economic growth (Wu, 2013.) To cope with world challenges as well to maintain

country's stable growth China also needs to strengthen its technology market (Fu and

6 Schott (2004, 2008), Rodrik (2006), Xu and Lu (2009), Wand and Wei (2010), Amiti and Freund (2010),

and Xu (2010).

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MU, 2014). The country is being forced towards a more skill intensive and technology

intensive growth path, with the amount of surplus unskilled labour in China falling. They

further argued that China should continue to increase its investment in R&D and in

education.

The main concern in this essay is to check where China compete with highly

developed zone i.e. EU and where do EU stands, as the developed economies like EU

compete with China by raising the quality of their exports (Schott, 2008). Emerging countries

have been endearing huge market shares over the last twenty years and China stands out with

the most astonishing performance: it has almost tripled its market share since 1995 (Zignago,

2014). After 1999, the imports of China have substituted imports from other emerging

countries (Freytag, 2008), and recently China became the world's biggest merchandise trader

in 2013, with total exports and imports of US$ 4,159 billion and a trade surplus of US$ 259

billion, 2.8 per cent of its GDP. However, most EU countries faced declines in their

merchandise exports in 2012 due to structural glitches in the euro zone. (WTO, trade statistics

2013)

China is popular as a labour abundant country but gaining a lot of competition from

its medium and high technology goods as well. High technology products play a vital role

while comparing international competition among different economies. This theory was

pioneered by Leamer (1987) who defined that what countries export does matter. This idea

was further developed by Hausmann et al. (2007) who explained income level of a country's

exports as another determinant of economic growth. (Zignago, 2014). The economic relations

between China and the EU grew close and both gained benefits from the stable relations

(Huaqun, 2004). However, the EU has lost -0.27 p.p. of market share in high technology

goods, whiles, China has gained massively in high technology market (18.9 p.p.), due to a huge

transfer of the assembly of these goods to China.

Hinloopen and Marrewijk (2004) analyzed the dynamics of revealed comparative

advantage for Hong Kong, Taiwan and China. They also used Galtonian regression in their

analysis and concluded that Hong Kong and Taiwan are more specialized in their exports as

compared to China. Lall and Albaladejo (2004), argue that Chinese exports has upgraded in

the technological content from 1990 to 2000, and due to its increasing share in high-tech

good’s exports it is moving rapidly towards high technology goods. Batra and Khan (2005)

made an attempt to check the pattern of revealed comparative advantage of China and India.

They further checked the trade similarities and differences of these two economies by

calculating at the sector and commodity level of the Harmonised System of classification. The

results showed that India and China both enjoy comparative advantage in labour and resource

intensive goods in the world market.

Harrigan and Deng (2008) made an argument about China's local comparative

advantage which is influenced by labour intensive commodities and by geography as well.

They concluded that China has Comparative advantage in heavy goods but only in nearby

markets and for distant markets it has comparative advantage in lighter goods. Amiti and

Freund (2008) analyse that between 1992 and 2005 the export growth of China showed a

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transition from low tech goods (agricultural products) into medium tech good (machinery and

transport equipment). This shift is mainly associated with increasing skill which is driven by

processing exports making up a large share in exports of manufacturing sector. According to

Dettmer et.al. (2009), China is specialising in technology intensive goods and focuses on

mobile type technology goods while EU's comparative advantage sustains in immobile

technology intensive goods. Tuan et.al (2016) argue that after China’s trade openness (1978-

1988) most of its exports growth occurred in the extensive margin (new variety products),

whereas, after 1989 it switched towards the intensive margin (existing variety products). They

find significant evidence and confirmed that from 1978 to 2008 the intensive margin is the

vital factor in the growth of China’s exports.

Additionally, in order to compare the comparative advantage convergence and

divergence pattern between China and the EU, our analysis will be deepened by applying the

Galtonian regression and the error correction techniques in eight different sectors.

4 Data

We use annual data comprising ten major sectors for China and EU from 2000-2012. The

260 observations are derived from the WTO statistics. The sectors are Manufacturing,

Chemicals7. Although, it is obvious that China has comparative advantage in Clothing and

Textiles, but to check the trend this sector is considered as well. Furthermore, Clothing and

Textiles is among one of the most dynamic exports in the world. If we classify the sectors

considered in this paper by technological intensity, we will get the following categorization.

Table 1: Sector ordering by Technological Intensity

High-technology sectors Pharmaceuticals, Office, accounting and

computing machinery, Electronics and

communications equipment

Medium-high-technology Sectors Electrical machinery, Auto-motive,

Chemicals excluding pharmaceuticals,

Machinery and equipment

Low-technology industries Textiles, textile products,

Source: Based on the World Bank Database

7 Pharmaceuticals, office and telecom equipment, machinery and transport equipment, electronic data and

office equipment, integrated circuits and electronic components, automotive products, clothing and

textiles.

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Table 2: Share in World Trade in Goods and Services (%)

Country 2005 2006 2007 2008 2009 2010 2011 2012 2013

World 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

China 8.1 8.7 9.3 9.4 10.2 11.1 11.4 11.9 12.6 e

USA 16.9 16.4 15.5 14.4 14.6 14.1 13.5 13.6 13.5e

EU-28 19.0 18.7 19.0 18.7 18.7 17.3 17.1 16.3 16.4p

Source: Eurostat, WTO Taken from European Commission Directorate General for trade (e) Estimated, (p) Preliminary data (for services)

In Table 2, one can easily distinguish the fact that China's world trade share growing rapidly

at an increasing rate from 8.1% to 12.6% for the time period 2004 to 2013, whereas, For EU

and the United states it is declining from 19.0% to 16.4% and from 16.9% to 13.5%

respectively. Moreover, if one compare the global exports of goods with China and the EU it

shows that China is trying to be the world's largest exporter and its exports of goods increasing

rapidly whereas, EU exports in goods increasing almost at a constant rate.

After accession to the WTO (11 December, 2001), china´s pattern of trade improving

and export volume index rapidly increasing and reaches at world´s highest level i.e. 643.178

in 2013. Interestingly, the pattern of exports and the comparative advantage changing

overtime in all cases especially in integrated circuits. Although, China has comparative

disadvantage in automotive sector but still its exports are increasing over the time from 2000

to 2012. According to the EU-China trade data, another interesting fact is China´s increasing

volume of exports in all sectors except Pharmaceuticals. Meanwhile, European Union follows

different trend, with a slight decreasing trend in all sectors except chemicals. In the

automotive sector, the percentage remains approximately constant (from 11.7% in 2000 to

10.6% in 2012).

According to the normalised index, the highest degree of comparative advantage is

listed in manufactures which also has great contribution in China's overall economic growth,

in addition it has the highest percentage in the total exports (88% in 2000 and 93% in 2012).

More interestingly, the EU's comparative advantage at highest degree is also in manufactures

and its percentage is highest in terms of total exports percentage (82% in 2000 and 75% in

2012). The only difference is the rate of change; the China's manufacturing exports are

increasing whiles the EU's manufacturing exports are declining. China's exports are increasing

in all sectors except pharmaceuticals. Whereas, the EU's exports are declining except in the

sectors of chemicals and pharmaceuticals. In order to reach a clear conclusion, it is essential

8 Export Volume index (2000= 100) is derived from UNCTAD’s volume index series and is the ratio of the export value index to the corresponding unit value index, World Bank, statistics.

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to make a clear comparison of comparative advantage between China and EU on each type

of sectors. In order to show the comparison, we will discuss the results of normalized revealed

comparative advantage index only9.

5 Methodology

Investigating structural changes in Comparative advantage

Hart and Praise (1956) pioneered the idea of using revealed comparative advantage indices

in econometric analysis by introducing the Galtonian regression. This method created by

Galton (1889)10, has been applied in different areas of research like Cantwell (1989)11 used it

to examine technological specialization patterns. We employ the following Galtonian

regression:

𝑅𝐶𝐴𝐼𝑐,𝑗𝑡2 = 𝛼𝑐 + 𝛽𝑐𝑅𝐶𝐴𝐼𝑐,𝑗

𝑡1 + 𝜀𝑐,𝑗 (7)

In the above equation, first two time periods are selected as an i.e. 𝑡2 and 𝑡1 which is

for 2000-2002 and 2010-2012 respectively; 𝛼𝑐 𝑎𝑛𝑑 𝛽𝑐 are standard regression coefficients

and 𝜀𝑐,𝑗 is the error term. At first, we will check the corresponding residuals by some

normality assumption by applying Shapiro-Wilk, Anderson-Darling and Kolmogorov-

Smirnov tests12. It is noteworthy that in this case, the normalised index calculation follows a

normal distribution also it is assumed that the corresponding regression is linear, and the

error term is normally distributed and is independent of 𝑡2 and𝑡1.

The basic idea of Galtonian regression is to check the similarity and dissimilarity in

the distribution of revealed comparative advantage at different points of time are relative to

each other. Furthermore, it also looks at the patterns of stability, convergence or divergence

in its trade specialization. Hart (1995) applied the ratios of variances in two different point of

time to calculate the convergence in labour productivity i.e.

𝜎𝑡22

𝜎𝑡12 =

𝛽2

𝑅2 (8)

Where 𝛽2 and 𝑅2 are the coefficients of regression and coefficients of determination

correspondingly.

𝛽 > 1, shows the trade specialization divergence given that the 𝑅2 does not go above 1,

9 See the graphs in Appendix 2. 10 He examined the heights of fathers and sons to analyse the size-wise business concentration. 11 Hart (1995), Frantzen (2008), Laursen (1998), and Worz (2005). 12 For the results see Appendix 3A.

12

which means the pattern of trade specialization is becoming strong since the sectors with traditional

comparative advantage will become more advantaged in the future and vice versa for the sectors

having comparative disadvantage (which is known as 𝛽-specialization (Dalum et al. 1998).

Moreover, 𝛽 < 1 does not certainly suggest convergence. The convergence occurs when 𝛽 <

𝑅2 < 1, here the trade specialization pattern can be reflected to be declining, which states that the

products with initial comparative disadvantage make their position better, whereas those with initial

comparative advantage lose their position. In the condition where 𝛽=𝑅2, it could be the case where

there is no divergence or convergence, so the pattern of trade specialization remains stable. First

we carry out the Galtonian regression analysis. Then we apply some normality tests on the

corresponding residuals such as Shapiro Anderson-Darling and Kolmogorov-Smirnov tests.

6 Econometric Analysis of Revealed Comparative Advantage:

6.1 OLS Galtonian Regression

In this section, we try to prove the arguments made in the paper about China and EU comparative

advantage empirically. The Galtonian regression indicates the convergence and divergence pattern

of a country. However, this is an ordinary regression with respect to two cross sections and two

time periods.

𝑅𝐶𝐴𝐼𝑐,𝑗𝑡2 = 𝛼𝑐 + 𝛽𝑐𝑅𝐶𝐴𝐼𝑐,𝑗

𝑡1 + 𝜀𝑐,𝑗 (9)

where error terms are normally distributed.

To check the authenticity of the indices we first examine whether the normality assumption

is met or not13. We use the average data over 2000_2003 for 𝑡1 and the average over 2010_2012

for 𝑡2. We also plot the influence statistics, histograms and scatter plots for the normalised index

and Balassa Index and find that the distribution of the Balassa index is extremely rightly skewed

hence far from symmetrical distribution. In case of normalised index, the distribution is normal

and histogram is bell shaped and the influence statistics gives us good results (without the outliers)

for normalised index. We consider the R Student, Hat matrix, DFFITS and COVRATIO results

for the influence statistics.

The following table gives us the Galtonian regression results.

13 As our sample size is less than 30, so is necessary to check the normality assumption. we will state the results of normality issue

only for the normalized revealed comparative advantage index.

13

Table 3 Results of Regressions for China and the EU

China, EU R2 /R2

OLS Galtonian 2,03 0,86 2,36

China Robust 1,87 0,63 2,97

Quantile 1,86 0,64 2,91

OLS Galtonian 1,02 0,99 1,03

EU Robust 1,06 0,91 1,16

Quantile 1,07 0,89 1,20

Note: we combine all the sectors in this analysis.

To check the robustness, we apply different regression models. The first one is the robust

regression which deals with the outliers and the non-normality and the second one is the

Quantile regression, which deals with the median or other quantiles. According to De

Benedictis and Tamberi (2004), the median is more useful instead of mean in the case of

Balassa index. The results of OLS, Quantile and the robust regression are also given in Table

3. Our results suggest that there are noticeable differences between the results of China and

the EU. In case of China the OLS Galtonian regression indicates there is more divergence

as the value of β is much greater than 1. It also interprets that the pattern of trade

specialization in China is more strengthened as the sectors with initial comparative

advantage become more advantaged. Whereas, in the EU, the value of β is close to one and

also close to R2 and according to Sanidas and Shin (2011), it means that there is no

convergence or divergence, therefore the trade specialization pattern in the EU remains

more or less stable.

7 Conclusions and Policy Implications

China is the main challenge for the EU's trade strategy, as China re-appeared as the World's

leading economy and the pinnacle exporter in the global economy for 2015. EU-China trade

increased dramatically in recent years. This paper challenged the view that China has

comparative advantage in labour intensive goods only, since China is gaining comparative

advantage in low-medium and medium-high technology sectors. Using the well-known

Galtonian model, data on the EU and China, our work estimated and analysed the revealed

comparative advantage from calculating different indices in various sectors. We found that

China is gaining a lot by its comparative advantage in high technology sectors as well and

there was a rapid change in its traditional comparative advantage. The results of Galtonian

14

regression reveal that China is diverging towards high tech sectors or diverging from its

traditional comparative advantage whereas, there is no convergence or divergence in the

trade specialization pattern of the EU and remains more or less stable.

The current essay documented the extent to which EU is exposed to China in terms

of their Comparative advantage and trade competitiveness. According to our findings EU and

China overlap in some sectors like manufacturing, machinery and transports equipment. We

found that China is not only gaining comparative advantage in labour intensive sectors but

also in capital intensive sectors. Moreover, China is gaining more from its comparative

advantage in terms of its strong sectors which is revealed from higher values of Balassa Index

and positive values for other indices like in case of textiles, clothing, Office and telecom

equipment (O&T), machinery and transport equipment (M&T), electronic data processing

and office equipment (EDP & OE), Telecommunications equipment (T). Furthermore, the

EU has comparative advantage in high-tech goods like pharmaceuticals, chemicals and

automotive products (AP) but not in telecommunications and integrated circuits products,

which are also from high-tech goods.

The comparative advantage indices presented in this paper were based on actual

export data. Hence the results show that the EU seems to be losing its comparative advantage.

There are some measures that can be taken by the EU to overcome the problem highlighted

in the paper above. The following are some recommendations for EU: Though China has

comparative advantage in labour intensive and medium to high-tech sectors and EU cannot

compete China in cost efficiency, because at first, China has more cheap labour as well as

cheap factors of production, secondly there is huge labour force which is available in China

not in the EU, thirdly wages are not very high in China as compared to the wages in the EU.

So the only solution to compete with China in terms of these sectors is that;

As, it is a famous saying, "never put your all eggs in one basket", the EU should invest

equally in different developing countries like India, Pakistan Brazil etc., not only in china, so

in the future they will not be solely dependent on China's products.

1. Policy to protect EU's clothing and textile industry;

Though China has comparative advantage in clothing and textile industry because of cheap

factors of production and cost deficiency but still EU has comparative advantage in the field

of fashion and designs. Fashion launched in EU because sense of fashion and design is still

with EU. China make copy of that new more fashioned product which was initiated by EU

and send it on cheaper amount, so EU should work on patent laws and copyrights rules to

restrict China from making copies of their products, this will bound china to make copies and

it will give more protection to EU's own industry and EU can still survive with comparative

advantage in clothing and textile industry.

This paper has focused on the exports and goods of China and the EU, using different

tools to check their respective comparative advantages. Further research related to digital

trade comparison between China and EU would be very useful in this research scenario.

15

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Appendix 1

China:

The Balassa index of RCA is greater than one in all sectors excluding Pharmaceuticals (PH),

Chemicals (CH)and automotive products (AP), indicating that China holds comparative

advantage in these sectors in the global market. Results show that China has revealed

comparative advantage in Manufacturing (Manf), Office and telecom equipment (O&T),

machinery and transport equipment (M&T), electronic data processing and office equipment

(EDP & OE), Telecommunications equipment (T)integrated circuits and electronic

components (IC&EC) from 2000-2012. China has revealed comparative disadvantage in

Pharmaceuticals (PH), Chemicals (CH) and automotive products (AP). Interesting issue is

that China had comparative disadvantage in machinery and transport equipment from 2000-

2003 and in integrated circuits and electronic components but after 2003 and 2008 it gains

19

comparative advantage in these sectors respectively. To analyse the changing trend of

comparative advantage, if we check China´s comparative advantage from 1980, it is

surprising that China does not have any comparative advantage in the respective sectors.

China gained comparative advantage in manufacturing sector from 1989, in office and

telecom equipment from 1998, in machinery and transport equipment from 2003 and in

integrated circuits and electronic components from 2008. Our analysis suggests that China

intend to gain comparative advantage in most dynamic sectors of the world trade, more

interestingly the high technology sectors.

European Union:

The Balassa index of RCA is greater than one in all sectors indicating that EU holds

comparative advantage in these sectors in the global market. Results indicate that EU has

revealed comparative advantage in Manufacturing (Manf), Pharmaceuticals (PH), Chemicals

(CH), machinery and transport equipment (M&T), automotive products (AP) from 2000-

2012. EU has revealed comparative disadvantage in Office and telecom equipment (O&T),

Telecommunications equipment (T), electronic data processing and office equipment (EDP

& OE) and integrated circuits and electronic components (IC&EC) from 2000-2012. In

total, EU gain comparative advantage in five out of nine sectors and has comparative

disadvantage in four sectors. One thing is strange here that EU had comparative advantage

in Telecommunications equipment (T) in the year 2000 but after 2000 EU lost his

comparative advantage in this sector.

Table 4 World Exports of Goods (Billion EUR)

Country 2005 2006 2007 2008 2009 2010 2011 2012 2013

World 6,214.1 7,144.7 7,550.3 8,247.0 6,786.5 8,983.9 10,344.2 11,484.2 11,330.1

China 612.5 771.7 890.5 972.7 861.5 1,190.1 1,363.8 1,594.6 1,663.3

USA 724.3 817.1 837.8 875.3 757.1 964.4 1,065.0 1,203.1 1,189.4

EU-28 1,049.5 1,152.4 1,234.3 1,309.1 1,094.0 1353.2 1,554.2 1,684.2 1,736.6

Source: Eurostat, WTO Taken from European Commission Directorate General for trade

20

Appendix 2 Figure 1: China-EU Revealed Comparative Advantage Index in different Sectors

0

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Telecom Equipment

Tele Eq EU Tele Eq Ch

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Integrated Circuits

IC EU IC Ch

21

Appendix 3 A

Normalised Revealed Comparative Advantage Index: Normality Tests

Sectors Shapiro-Wilk Anderson- Kolmogorov-

Test Darling Smirnov

Manf EU 0,91 0,43 0,18

Manf Ch 0,94 0,27 0,14

Textile EU 0,94 0,27 0,13

Textile Ch 0,94 0,29 0,13

Clothing EU 0,79 1,28 0,30

Clothing Ch 0,79 1,17 0,22

Chemicals EU 0,98 0,15 0,12

Chemicals Ch 0,98 0,34 0,14

Pharma EU 0,86 1,00 0,32

Pharma Ch 0,85 0,91 0,25

Mach/trans equ EU 0,90 0,46 0,19

Mach/trans equ Ch 0,95 0,26 0,14

Office/tele equ EU 0,96 0,29 0,15

Office/tele equ Ch 0,90 0,59 0,22

Elec data Equ EU 0,89· 0,56· 0,18

Elec data Equ Ch 0,86· 0,78· 0,22

Telcom Equ EU 0,94 0,35 0,19

Telecom Equ Ch 0,91 0,49 0,20

Integrated Circuits

EU 0,94 0,34 0,13

Integrated Circuits

Ch 0,93 0,36 0,15

Automotive EU 0,95 0,29 0,15

Automotive Ch 0,83 0,90 0,22· H0: F(Y) = N (μ, σ), The distribution of the population is normal with unspecified mean and standard deviation. H1: F(Y) ≠ N

(μ, σ), The distribution of the population is not normal.

Do not reject the null hypothesis at 1%·, 5 %·· and 10% level

22

Appendix 3 B

Rank Correlation Tests

Sectors,(EU,Chi

na)

Spearsman

Test

Pearson Test Kendall

Test

Manufacture 0,582 0,593 0,462

Textile -0,703 -0,909 -0,487

Clothing 0,747 0,851 0,513

Chemicals -0,846 -0,909 -0,744

Pharmaceuticals -0,934 -0,917 -0,846

Mach/trans equ 0,654 0,686 0,513

Office/tele equ 0,501 0,512 0,410

Elec data Equ 0,670 0,744 0,462

Telcom Equ -0,505 -0,529 -0,410

Integrated

Circuits

0,841 0,819 0,692

Automotive -0,484 -0,681 -0,385