EU and China’s comparative advantage, trade complementarity and trade specialization dynamics in agricultural products

Abstract

This paper aims to analyse the comparative advantage of selected agricultural products for both EU and China in the context of EU-China bilateral trade and explore how the trade specialization changes along with the economic development based on the data between 2001 and 2017. First, we obtain the static results from both a comparative advantage perspective and a trade complementarity perspective. We then analyse three types of trade specialization dynamics by using the OLS regression and Markov transition probability matrix from a more dynamic perspective. Results show that EU’s comparative advantage includes meat products, dairy products, animal originated products, preparations of cereals or milk products, beverages, wool and vegetable textile fibres. China has a comparative advantage in fish, animal originated products, edible vegetable, lac, gums, resins, vegetable, plaiting materials, preparations of vegetables, silk, wool and vegetable textile fibres. We also find that both EU and China have unstable trade specialization for their agricultural products trade and we give policy implications for EU-China bilateral trade based on the results.

Introduction

The EU and China trade relationship has a long history going back to the Silk Road during the Pax Romana and Zheng He’s Seven Voyages during 1405–1433. The modern trade relationship between the EU and China started in 1975. Being the world’s biggest exporter and importer, the EU is the leading investor and recipient of foreign investment and the biggest aid donor. China has become the EU’s largest import trading partner and the second largest export trading partner (EC 2016). The total trade between the EU and China was €606 billion in 2018, composed of €395 billion in imports (20% of EU imports) and €211 billion in exports (10.8% of EU exports). China’s accession to the WTO in 2001 can be considered a milestone for Chinese trade with the EU in agricultural products. Both imports and exports of the EU with China have been increasing since 2002. Since 2009, the gap between exports and imports has been increasing year by year and the agricultural trade surplus of the EU historically reached a peak in 2016 with a €7.818.8 billion surplus, of which €12.830.9 billion were for exports and €5.012.1 billion were for imports (Eurostat 2017).

This paper uses the notion of comparative advantage and trade complementarity to analyse selected agricultural products for both the EU and China in the context of EU-China bilateral trade relations. According to the international trade theory of David Ricardo, countries should trade products that have comparative advantages so as to increase their welfare. Trade complementarity can indicate, for example, to what extent Country A (the EU)’s exports correspond to Country B (China)’s imports and vice versa. It therefore can provide useful information for decisions on establishing mutual trade agreements between trading partners.

The study quantifies the comparative advantages of both the EU and China in specific agricultural products from both static and dynamic perspectives. In doing so, this study adopts firstly Balassa’s revealed comparative advantage index (BRCA) and secondly an updated index called the normalized revealed comparative advantage index (NRCA). Given that these indexes’ results are based on trade data of previous years in the long term, the results can be used to delineate the main trade characteristics between the two partner countries. It is also the same with the trade complementarity index (TCI). The results show which agricultural products denote comparative advantages for the EU and which products denote comparative advantages for China; therefore, the indexes imply which products should be traded between the two countries and the results of the trade complementarity index can inform the two regions about which agricultural products should be considered when negotiating trade agreements.

Moreover, this study further identifies three types of trade specialization dynamics for agricultural products in the EU and China so as to see how the trade specialization changes along with the economic development of the EU and China. The trade specialization here borrows from the notion of comparative advantage. The first type of trade specialization dynamics is related to how the comparative advantage index changes from one period to the next. The second type of trade specialization dynamics is in relation to the degree of mobility of the comparative advantage for every two adjacent years within a whole defined research period. The third type of trade specialization dynamics predicts the trends of comparative advantage for the future based on the trade performance over the research time period.

Research related to analysing comparative advantage and agricultural trade specialization dynamics in the context of EU-China bilateral trade relationship is rare. This paper therefore has filled in the research gap by using the updated agricultural trade data at more refined agricultural products for both the EU and China. Results show that EU’s comparative advantage includes meat products, dairy products, animal originated products, preparations of cereals or milk products, beverages, wool and vegetable textile fibres. China has a comparative advantage in fish, animal originated products, edible vegetable, lac, gums, resins, vegetable, plaiting materials, preparations of vegetables, silk, wool and vegetable textile fibres. We also find that both EU and China have unstable trade specialization for their agricultural products trade. We further discuss policy implication for the future EU-China bilateral trade in this paper.

The following sections have been divided into four parts. In the ‘Literature review’ section, we will give a brief review on the related literature. Methodology and data will be presented in the ‘Methodology and data’ section, followed by results. The last section is the conclusion and policy implications.

Literature review

The existing literature on trade comparative advantages and trade complementarity is abundant. They provide both theoretical and empirical foundations for this paper. In this section, the current literature will be summarized from three main perspectives: studies in relation to different countries, time periods and products (sectors); the application of various comparative advantage and complementarity indexes; studies related to trade specialization dynamics.

Literature relating to China and other countries

Of specific interest to this research are the studies that have put a focus on Asia and other emerging areas notably China. Fang and Beghin (2000) researched the comparative advantages of major Chinese crops during 1988 and 1999. Adams et al. (2004) explained why China is so competitive by analysing China’s revealed comparative advantage for its general export performance from 1970 to 2002. Ahmad et al. (2018) analysed the comparative advantages of Indian and Chinese bilateral merchandize trade from 1985 to 2012. The comparative advantages of the agricultural sector of Vietnam in 2014 have also been studied by Hoang et al. (2017). Selek and Kebakile (2017) analysed the comparative advantages of Botswana’s beef industry for the period 1961 to 2011. Esquivias (2017) put the focus on agricultural trade between East Java, Indonesia and six main ASEAN countries during 2007–2013, and Elryah (2015) illustrated Sudan’s agricultural products between 2000 and 2013. The comparative advantages of Nigeria and India in the context of bilateral trade relations, between 2000 and 2014 which covered 20 major product categories, have been studied in the work of Ibrahim and Shehu (2016). Kumar and Ahmed (2015) measured the revealed comparative advantages in bilateral trade between India and Sri Lanka in different sectors from 1975 to 2013. Moreover, the tuna industry in Thailand between 1996 and 2006 had been studied by Kuldilok et al. (2013).

A second group of studies relate to Europe, especially the EU and adjacent countries. Carraresi and Banterle (2015) analysed the comparative advantages of the food industry and agriculture sector at the 2-digit level in the intra-EU market from 1995 to 2011, while Bojnec and Fertö (2009) studied the agri-food sector in eight Central European and Balkan countries from 1995 to 2007. Sahinli (2012)’s work explained the comparative advantages for Turkey and the EU in their bilateral relationship in the agricultural sectors for the year 2008; Serin and Civan (2008) also studied the EU’s comparative advantages with Turkey during the period 1995 to 2005 in the agricultural sector, but they especially focused on the fruit and vegetable industries. Some other studies analysed the comparative advantages of food industry in Russia, Ukraine and other EU countries (Gorton et al. 2000; Qineti et al. 2009; Ishchukova and Smutka 2013).

Literature on the application of comparative advantage and complementarity indexes

The current literature not only analyses the different countries with different year durations but also applies different indicators (indexes) to obtain the results. The most used index in the current empirical research is Balassa (1965)’s revealed comparative advantage (hereafter BRCA) which was first introduced by Liesner (1958). Essentially, BRCA measures the share of a specific country’s export in a given product in the total exports of this specific country as a fraction of the share of world exports in this given product in the total world exports.

The BRCA approach is the most widely used method to identify comparative advantage (Balassa and Noland 1989). This has been proved by many examples from the current literature; for example, Brakman and Marrewijk (2017) used the BRCA to compare the results between using gross export value and value-added trade flows. Also, studies such as Ahmad et al. (2018), Elryah (2015), Kuldilok et al. (2013), Sahinli (2012), Drescher and Maurer (1999), Laursen (2015) and Qineti et al. (2009) all have used this index.

Besides the BRCA index, there are some other ways to quantify comparative advantage; however, most of these are derived from BRCA, such as the revealed symmetric comparative advantage (RSCA) introduced by Dalum et al. (1998) and Laursen (1998), Vollrath’s (1991) revealed trade advantage (RTA) and normalized revealed comparative advantage (NRCA) which was developed by Yu et al. (2009). Moreover, there are some other measurements such as Lafay (1992)’s trade balance index (TBI), export market share (EMS), comparative export performance (CEP) and net export index (NEI).

Recent work usually applies two or more different indexes to measure comparative advantage. Some studies use both BRCA and RTA,¹ such as Ishchukova and Smutka (2013), Fertö and Hubbard (2003), Bojnec and Fertö (2009) and Bavorová (2003); other studies apply both BRCA and CEP, like Serin and Civan (2008). Concerning the studies on trade complementarity, Ibrahim and Shehu (2016) used TCI as a tool to analyse the trade potential between two trading partners (Nigeria and India), while Kumar and Ahmed (2015) used BRCA and TCI at the same time. They have built a relatively firm methodology foundation for further research. In terms of comparative advantages, BRCA, RSCA, NRCA and RTA are the most popular measurements for researching comparative advantages, while the TCI indicator is the main method used to analyse trade complementarity.

Literature on trade specialization dynamics

Trade specialization represented by comparative advantage is dynamic not static. It changes along with structural change in an economy due to different factors. Theoretically, the changes rely on three elements: (1) the role of factor accumulation (Findlay (1970)); (2) the endogeneity of technological change (Krugman (1979)); (3) the influence of agglomeration economies (Krugman (1985)). It implies that trade specialization which is conceptualized by the concept of comparative advantage in this study is dynamic and it evolves endogenously over time.

According to Hinloopen and Marrewijik (2001), three types of trade specialization are defined: first, the changes of the comparative advantage index from one period to the next; second, the degree of mobility for every two adjacent years during a whole research period; third, the trends of comparative advantage over a research period and predictions for the future. Empirically, Hoang et al. (2017) study the dynamic comparative advantages of Vietnam in its agricultural sector and Proudman and Redding (2000) analyse the evolving trade pattern for France, Germany, Japan, the UK and the USA in the manufacturing sectors. This study therefore follows the ideas of the three dynamic types and it analyses the trade specialization dynamics of the EU and China respectively in terms of agricultural products trade in the context of EU-China bilateral trade relations.

Literature gaps have emerged after analysing the existing studies on comparative advantages and trade complementarity. Though there are many studies analysing comparative advantages and trade complementarity between different trading partners, studies of EU-China bilateral trade are rare. When it comes to EU-China agricultural products trade relations from the perspective of comparative advantages, there is even much less literature from the dynamic point of view. Although Andreosso-O’Callaghan and Li (2018) recently studied agricultural products trade potential between the EU and China, the study only uses a descriptive statistical analysis, and it has also highlighted the notion of trade complementarity between the two trading partners in agricultural products.

Moreover, research in the current literature on this topic is not up to date and the latest research is the analysis for the period 1975 to 2013 (see Kumar and Ahmed 2015). Therefore, this paper will fill in these two main research gaps by analysing the comparative advantages and trade complementarity for both the EU and China in terms of the agricultural products in the context of bilateral trade relationship from 2001 to 2017.

Methodology and data

The results of comparative advantage and trade complementarity will be analysed from both static and dynamic perspectives. Thus, the methodology used in this paper will be a broad two-step approach analysis. The first step is to obtain the static results from both a comparative advantage perspective and a trade complementarity perspective. The second step is to analyse three types of trade specialization dynamics.

Quantifying comparative advantage and trade complementarity (step 1)

The first step is the static analysis which starts by getting the results of comparative advantage and trade complementarity indexes by using the BRCA indicator, the NRCA indicator and the TCI indicator. According to Balassa (1965 and 1977), the BRCA can be expressed as follows:

$${\mathrm{BRCA}}_{\mathrm{i}\mathrm{j}}^{\mathrm{t}}=\frac{{~}^{{X}_{\mathrm{i}\mathrm{j}}^{\mathrm{t}}}\!\left/ \!{~}_{{X}_{\mathrm{i}}^{\mathrm{t}}}\right.}{{~}^{{X}_{\mathrm{w}\mathrm{j}}^{\mathrm{t}}}\!\left/ \!{~}_{{X}_{\mathrm{w}}^{\mathrm{t}}}\right.}$$

(1)

where ${X}_{\mathrm{ij}}^{\mathrm{t}}$ is the country i exports of products j in time t; ${X}_{\mathrm{i}}^{\mathrm{t}}$ is the country i’s total exports in time t; ${X}_{\mathrm{wj}}^{\mathrm{t}}$ is the world’s exports of product j in time t; and ${X}_{\mathrm{w}}^{\mathrm{t}}$ is the world’s total exports in time t.

In addition, we also apply the NRCA as a substitute indicator of the BRCA index to overcome the shortcomings of BRCA index. Yu et al. (2009) had proposed the NRCA index which is capable of allowing comparisons across commodities, countries and time. Yu et al. (2009)’s NRCA index can be expressed as follows:

$${\mathrm{NRCA}}_{\mathrm{i}\mathrm{j}}^{\mathrm{t}}=\frac{X_{\mathrm{i}\mathrm{j}}^{\mathrm{t}}}{X_{\mathrm{w}}^{\mathrm{t}}}-\frac{X_{\mathrm{i}}^{\mathrm{t}}\times {X}_{\mathrm{w}\mathrm{j}}^{\mathrm{t}}}{X_{\mathrm{w}}^{\mathrm{t}}\times {X}_{\mathrm{w}}^{\mathrm{t}}}$$

(2)

where ${X}_{\mathrm{ij}}^{\mathrm{t}}$ is the country i’s exports of product j in time t; ${X}_{\mathrm{w}}^{\mathrm{t}}$ is the world’s total exports in time t; ${X}_{\mathrm{i}}^{\mathrm{t}}$ is the country i’s exports in time t; and ${X}_{\mathrm{wj}}^{\mathrm{t}}$ is the world’s exports of product j in time t.

The results derived from NRCA will be symmetric ranging from −1/4 to +1/4 with 0 being the comparative-advantage-neutral point and can be used for making comparisons across countries and time.

The trade complementarity index (TCI) is applied to calculate the degrees of trade complementarity of the EU and China. The TCI index was introduced by Michaely (1996) and is as follows:

$${\mathrm{TCI}}_{\mathrm{EU}}^{\mathrm{j}}=100\times \left(1-\frac{\sum \left|{M}_{\mathrm{China}}^{\mathrm{j}}-{X}_{\mathrm{EU}}^{\mathrm{j}}\right|}{2}\right)$$

(3)

$${\mathrm{TCI}}_{\mathrm{China}}^{\mathrm{j}}=100\times \left(1-\frac{\sum \left|{M}_{\mathrm{EU}}^{\mathrm{j}}-{X}_{\mathrm{China}}^{\mathrm{j}}\right|}{2}\right)$$

(4)

where ${\mathrm{TCI}}_{\mathrm{EU}}^{\mathrm{j}}$ is the trade complementarity index of the EU; ${\mathrm{TCI}}_{\mathrm{China}}^{\mathrm{j}}$ is the trade complementarity index of China; ${M}_{\mathrm{China}}^{\mathrm{j}}$ is the share of China’s imports in product j in China’s total imports; ${M}_{\mathrm{EU}}^{\mathrm{j}}$ is the share of the EU’s imports in product j in the EU’s total imports; ${X}_{\mathrm{EU}}^{\mathrm{j}}$ is the share of the EU’s exports in product j in the EU’s total exports; and ${X}_{\mathrm{China}}^{\mathrm{j}}$ is the share of China’s exports in product j in China’s total exports.

The results from Eqs. (3) and (4) are measured in percentage terms, and the index of 40% is considered the critical value, which means that a TCI index over 40% is indicative of a strong trade complementarity (Kumar and Ahmed 2015).

Methods of measuring trade specialization dynamics (step 2)

For the measurement of the first type of dynamic trade specialization, this study adopts the OLS regression method which was built by Hart and Prais (1965) and which was first used in this context by Cantwell (1989).² The OLS regression model for the first type can be written as:

$${NRCA}_{\mathrm{i}\mathrm{j}}^{t2}={\alpha}_{\mathrm{i}}+{\beta}_{\mathrm{i}}{NRCA}_{\mathrm{i}\mathrm{j}}^{t1}+{\varepsilon}_{\mathrm{i}\mathrm{j}},\left({\varepsilon}_{\mathrm{i}\mathrm{j}}\sim n.i.d.\left(0,\sigma \right)\right)$$

(5)

where ${NRCA}_{ij}^{t2}$ is the country i’s NRCA result in product j at time 2 (t2 represents final year); ${NRCA}_{ij}^{t1}$ is the country i’s NRCA results in product j at time 1 (t1 represents initial year); α_i is the A constant; β_i is the A regression coefficient to be estimated; and ε_ij is the residual terms.

Making the year 2008 as the cutoff point for the long-term three sub-periods allows the study to see the changing pattern of the trade specialization before and after the economic crisis of 2008. Hence, three time periods are designed for the OLS regression as indicated in Table 1.

Table 1 Three research time periods for the first type of trade specialization dynamics

The estimated β coefficient will indicate the different changing patterns of the trade specialization. When 0 < β < 1, products with an initial weak comparative advantage gain comparative advantage through time, while products with a strong initial comparative advantage lose their comparative advantage; when β > 1, it implies that a comparative advantage will become stronger (or weaker) for products with a strong (or weak) initial comparative advantage; when β = 0, there is no relationship between comparative advantage over time; and when β < 0, the comparative advantage indexes initially below the average value will eventually be above the average value and vice versa.

However, following Cantwell (1989), the case when β > 1 indicates two possible answers as mentioned above. Therefore, in order to find out the trends of trade specialization for each selected time period, Hart (1976) provides a way to make comparisons between β and the correlation coefficient R from the same regression model which can be shown as follows:

$$\frac{\sigma_{\mathrm{i}}^{t2}}{\sigma_{\mathrm{i}}^{t1}}=\frac{\mid {\beta}_{\mathrm{i}}\mid }{\mid {R}_{\mathrm{i}}\mid }$$

(6)

where R_i is the correlation coefficient from (5) and σ is the standard deviation of the variables ${NRCA}_{ij}^{t2\&t1}$.

When β = R, specialization tends to stay unchanged; when β > R, the degree of trade specialization rises; and when β < R, trade specialization falls.

For the second type of trade specialization which refers to mobility and persistence, a one-step Markov transition probability matrix is applied. Firstly, with the help of the results of the NRCA index, this study leaves the NRCA indexes which are less than or equal to 0 as one class named group 1 (denoting a comparative disadvantage); then, a quartile method is used to classify the rest of NRCA indexes into three other groups, namely group 2, group 3 and group 4, where group 2 refers to a weak comparative advantage; group 3 denotes medium comparative advantage; and group 4 represents the case of a strong comparative advantage.

The one-step transition probability shows the possibility of the NRCA index moving from an initial state to other states within two adjacent years; and after obtaining the probability matrix, Shorrocks (1978)’s mobility index (hereafter M index) is used to assess the trace³ of the transition probability matrix in order to obtain the degree of the mobility. The equation of the M index can be written as follows:

$$M=n- tr(P)/\left(n-1\right)$$

(7)

where n is the number of groups (4 groups as mentioned earlier), P is the transition probability matrix and tr(P) is the trace of transition probability matrix.

A higher M index implies a greater mobility while a lower M index mirrors a lower mobility which can be considered relative persistence; when M = 0, this implies a perfect immobility.

For the last type of trade specialization dynamics, a regression trend analysis method is employed to investigate the trend of trade specialization at an agricultural product level from 2001 to 2017. The same method is applied for the trade complementarity index in order to predict the trends of TCI for the future. Therefore, the regression trend analysis model can be defined as follows:

$${NRCA}_{\mathrm{ij}}^t={\alpha}_{\mathrm{ij}}+{\beta}_{\mathrm{ij}}t+{\varepsilon}_{\mathrm{ij}}^t$$

(8)

where t is the time index which is from 2001 to 2017 individually and β_ij is the regression coefficient that shows the trends of NRCA of selected agricultural products.

$${TCI}_{\mathrm{ij}}^t={\alpha}_{\mathrm{ij}}+{\beta}_{\mathrm{ij}}t+{\varepsilon}_{\mathrm{ij}}^t$$

(9)

where t is the time index which is also from 2001 to 2017 individually and β_ij is the regression coefficient that represents the trends of the TCI of defined agricultural products.

When β_ij is close to 0, country i’s trade specialization in product j can be considered stable; when β_ij > 0, the trend shows that the country is gaining a comparative advantage (or trade complementarity) in product j over time; while when β_ij < 0, it shows a trend towards a loss of comparative advantage (or trade complementarity).

Data and the definition of agricultural products

The trade data from 2001 to 2017 for the purpose of calculating the results of the BRCA index and NRCA index are collected from the Trade Map Database. The agricultural products in this study are defined by the harmonized system at the 4-digit level which is from HS01 to HS24 plus HS50 to HS53.⁴ Therefore, 245 agricultural products in total are covered in the study. However, to facilitate the analysis and interpretation, all the 4-digit level agricultural products are compressed into 2-digit level. Moreover, the first type of dynamics and the second type have integrated all the agricultural products into the whole agricultural sectors for both the EU and China. Also, in order to facilitate the presentation of the results, all the NRCA indexes are multiplied by 10,000.

Empirical results

In this section, results of the comparative advantages and trade complementarity derived from the BRCA index equation and NRCA index equation will be firstly analysed in the ‘Comparative advantage of agricultural products in the EU and China’ section, followed by an analysis of the results of trade complementarity by using the TCI index in the ‘Trade complementarity of both the EU and China’ section. After that, a regression analysis of three types of trade specialization for both the EU and China can be found in the ‘Trade specialization dynamics’ section.

Comparative advantage of agricultural products in the EU and China

The results of the BRCA index and NRCA index⁵ have shown that for the EU, between 2001 and 2017, animal originated products, wool and vegetable textile are the product groups that always enjoy a comparative advantage. The EU has gained a comparative advantage in preparation of cereals or milk, beverages, meat and dairy products in the recent years.

We break down the results into four different periods: during the first period (2001~2005), both the BRCA index and the NRCA index indicate that the EU had a comparative advantage in animal originated products (2.64 and 0.05 respectively), wool (1.91 and 0.08) and vegetable textile fibres (1229.74 and 0.16), while for cotton, the BRCA index shows a comparative advantage (44.13) while the NRCA index shows a comparative disadvantage (−0.18).

In the second period, from 2006 to 2009, both BRCA and NRCA show a comparative advantage for the EU in animal originated products (2.97 and 0.07), wool (2.43 and 0.10) and vegetable textile fibres (7.71 and 0.13). However, only the BRCA index shows that coffee and tea is the product category for the EU with a comparative advantage with a value of 7.56.

The number of product categories showing a comparative advantage starts to increase in the third period (2010~2013). The results of the BRCA index accord with the results of the NRCA index in the case of 5 product categories for the EU showing a comparative advantage. They are animal originated products (2.56 and 0.08 respectively), preparations of cereals or milk (1.14 and 0.05), beverages (1.52 and 0.31), wool (2.22 and 0.10) and vegetable textile fibres (6.62 and 0.13).

In the latest period (2014~2017), 7 product categories are categorized by both the BRCA index and the NRCA index as denoting a comparative advantage for the EU. These product groups are meat (1.63 and 0.53), dairy products (1.22 and 0.12), animal originated products (2.66 and 0.11), preparations of cereals or milk (2.52 and 0.71), beverages (1.63 and 0.48), wool (2.19 and 0.11) and vegetable textile fibres (7.50 and 0.20).

Focusing on China, between 2001 and 2017, animal originated products, vegetable plaiting materials, silk, wool and vegetable textile fibres are the product groups for China to enjoy a comparative advantage. In the recent years, China has obtained a comparative advantage in fish, lac, gums and resins. However, China has lost its comparative advantage in preparations of vegetables and edible vegetables, cotton, coffee and tea.

Between 2001 and 2005, the results of the BRCA index accord broadly with the results of the NRCA index with the only exception of cotton (70.45 and −0.15). According to both the BRCA and NRCA index,⁶ China had a comparative advantage in animal originated products (7.49 and 0.29), edible vegetable (1.10 and 0.02), vegetable plaiting materials (1.88 and 0.00), preparations of vegetables (2.06 and 0.28), silk (8.09 and 0.19), wool (2.28 and 0.18) and vegetable textile fibres (232.08 and 0.03).

In the second period (2006~2009), China has a comparative advantage in fish (1.03 and 0.03), animal originated products (5.05 and 0.29), lac, gums, resins (1.00 and 0.00), vegetable plaiting materials (1.54 and 0.00), preparations of vegetables (1.38 and 0.18), silk (5.49 and 0.17), wool (2.06 and 0.16) and vegetable textile fibres (2.37 and 0.05). However, only the BRCA index indicates that coffee and tea (68.84) is a product category with a comparative advantage for China during this period.

In the third period (2010~2013), in contrast with the EU, the number of product categories for China with a comparative advantage starts to decrease. Both the BRCA and NRCA indexes show that China has a comparative advantage in fish (1.06 and 0.05), animal originated products (4.46 and 0.29), lac, gums, resins (1.51 and 0.03), vegetable plaiting materials (1.68 and 0.01), silk (6.90 and 0.18), wool (2.70 and 0.23) and vegetable textile fibres (2.39 and 0.05) (7 product categories in total).

Coming to the most recent time period (between 2014 and 2017), there are 6 product categories for which the BRCA index and the NRCA index denote a comparative advantage for China. They are animal originated products (3.91 and 0.28), lac, gums, resins (2.49 and 0.10), vegetable plaiting materials (2.51 and 0.01), silk (7.29 and 0.15), wool (2.13 and 0.15) and vegetable textile fibres (2.04 and 0.05).

Trade complementarity of both the EU and China

In general, most of the agricultural product categories have a strong degree of trade complementarity (TCI > 40%) for both the EU and China.⁷ Overall, between 2001 and 2017, China can always match the demand of EU’s imports in edible vegetables and milling products, whereas the EU can always match with China’s import structure in coffee and tea (Tables 10, 11, 12 and 13 in the Appendix). However, China has lost its trade complementarity in tobacco but it has obtained new complementarity in meat preparation and wool. The EU’s trade complementarity in cotton has disappeared in the recent years but it has gained new trade complementarity in cereals and animal or vegetable fats.

Between 2001 and 2005, the EU had 21 product categories that were highly matched with Chinese imports, while China had 23 categories of product which strongly matched EU imports.

Among the 29 product categories, during 2001 and 2005, only dairy products, oil seeds and animal or vegetable fats show TCI results less than the critical value of 40% in both the EU and China which denotes a low degree of trade complementarity. For China, the product groups that are over 40%, matching with EU imports, are meat (57.32%), edible vegetables (46.22%), milling products (58.45%) and tobacco (78.76%), while the EU product categories that have an index over 40%, matching with Chinese imports, are coffee and tea (76.95%) and cotton (61.32%).

Product categories with a degree of trade complementarity for the EU higher than for China are live animals, animal originated products, edible fruit and nuts, cereals, lac, gums, resins, vegetable plaiting materials, cocoa, beverages, silk and vegetable textile fibres, while the degree of trade complementarity of China is higher than the EU’s in fish, live trees, meat preparations, sugar, preparations of cereals or milk, preparations of vegetables, various edible preparations, food wastes and wool.

Between 2006 and 2009, both the EU and China show a low degree of trade complementarity in only dairy products and animal or vegetable fats. Meat, fish, edible vegetables, cereals, milling products, oil seeds and tobacco are the only product categories for China which match the EU’s import structures, while for the EU countries, its exports strongly match with Chinese imports in coffee, tea and cotton.

In terms of the product categories with high values in the TCI index (>40%) for both the EU and China, the TCI indexes of the EU are higher than China’s in live animals, edible fruit and nuts, lac, gums, resins, vegetable plaiting materials, sugar, various edible preparations, beverages, food wastes and silk. By contrast, the TCI indexes of China in animal originated, live trees, meat preparations, cocoa, preparations of cereals or milk, preparations of vegetables, wool and vegetable textile fibres are higher than the EU’s.

In the third period (2010~2013), both the EU and China show a low degree of trade complementarity in only three product categories: dairy products, oil seeds and cotton. China has strong TCI indexes in edible vegetables, milling products, meat preparations, tobacco and wool, and this is not the case for the EU. However, the EU has strong TCI indexes in coffee, tea, cereals and animal or vegetable fats, and this is not the case for China.

Among the products with TCI indexes over 40% for both the EU and China, the EU’s trade complementarity is higher than China’s in live animals, animal originated products, edible fruit and nuts, lac, gums, resins, vegetable plaiting materials, sugar, various edible preparations and beverages, while China’s trade complementarity is higher than the EU’s in meat, fish, live trees, cocoa, preparations of cereals or milk, preparations of vegetables, silk and vegetable textile fibres.

In the most recent period (from 2014 to 2017), both the EU and China show a low degree of trade complementarity in oil seeds and cotton. China can highly match the EU’s imports demand in edible vegetables, milling products, meat preparations and wool, and this is not the case for the EU. However, the EU is able to strongly satisfy Chinese import demand in dairy products, coffee, tea, cereals and animal or vegetable fats, and this is not the case for China.

The EU’s trade complementarity is stronger than China’s in live animals, animal originated products, edible fruit and nuts, lac, gums, resins, vegetable plaiting materials, cocoa, preparations of cereals or milk, preparations of vegetables, beverages and vegetable textile fibres, whereas China’s trade complementarity is stronger than the EU’s in meat, fish, live trees, sugar, various edible preparations, food wastes, tobacco and silk.

Trade specialization dynamics

For the first type of trade specialization dynamics, Table 2 shows the OLS regression results by using the NRCA index over the three defined time periods for the EU and China respectively. All the regression coefficients are significant at the 1% level. In each time period and for both the EU and China, the β-s are all greater than 1 which implies that for both the EU and China, agricultural product categories with an initial strong comparative advantage gain more comparative advantage, while product categories with an initial weak comparative advantage lose comparative advantage. This situation happens in all the three defined time periods.

Table 2 The OLS regression (type 1) results for both the EU and China over the three periods

To identify the first type of trade specialization dynamics given the two possibilities from β in Table 2, the alternative method needs to be used, which is a comparison between β and the correlation coefficient R. The results are shown in the last column of Table 2; and all the β are larger than R which indicates that the degree of trade specialization rises for both the EU and China in all the three time periods. This also indicates that the economic crisis in 2008 had no significant influence on the agricultural products trade specialization dynamics for both the EU and China, and that China’s accession to the WTO enhanced trade specialization for both the EU and China.

For the second type, after grouping the results of the NRCA indexes of the 245 selected agricultural products from 2001 to 2017 for the EU, the numbers of the agricultural products in each group vary from year to year, but on average, as the last column in Table 3 shows, there are 214 products in group 1, 8 products in group 2, 15 products in group 3 and 8 products in group 4. It therefore shows that for most agricultural products, the EU has a comparative disadvantage in its agricultural trade relations with China.

Table 3 Group classification of the EU’s NRCA index

In terms of the degree of mobility within the four groups, the movement from a comparative disadvantage to a strong comparative advantage is defined as ‘forward moving’, while the movement from a strong comparative advantage to a comparative disadvantage is termed as ‘backward movement’. Table 4 depicts the transition probability of the trade specialization from one group (or state) in the current year to another group (or state) in the next year for the EU.The probabilities which are highlighted on the diagonal represent the stability of each group. The agricultural products with a comparative disadvantage have a 93.4% probability of keeping this comparative disadvantage, while the products with a medium comparative advantage and a strong comparative advantage have a 32.0% and 39.5% probability respectively to stay in the same state. However, the products in group 2 with weak comparative advantage have a very low probability (0.14%) of retaining the weak comparative advantage.

Table 4 The Markov transition probability matrix of the NRCA index (EU)

For the products initially with a comparative disadvantage (in group 1), there is a 3.57%, 2.89% and 0.15% chance to move forward in the next year into group 2, group 3 and group 4 respectively. For the products initially with a weak comparative advantage (in group 2), there is 100% chance that they will lose the comparative advantage to become comparative disadvantage products next year, and there are no chances to move forward to group 3 and group 4 from group 2. Products initially with a medium comparative advantage will have a 36.63% chance of moving backwards to the comparative disadvantage group in the following year and a 0% probability of forming the weak comparative advantage group next year. However, it has a 35.34% chance of moving forward to strong comparative advantage the following year. For the products with initially a strong comparative advantage, there is a 60.49% probability that they will move backwards to the medium comparative advantage group; however, it is impossible for these products to become products with a comparative disadvantage or a weak comparative advantage. The M index is 0.783, which represents a high degree of mobility for the EU’s agricultural products trade specialization.

From the Chinese point of view, there are on average 197 products, 21 products, 19 products and 7 products in group 1, group 2, group 3 and group 4 respectively (see Table 5). Although the number of products in group 1 is smaller than in the case for the EU, it still takes the biggest portion compared to the numbers of products in other groups.

Table 5 The group classification of China’s NRCA index

As the probabilities highlighted in Table 6 show, products with a comparative disadvantage will stay in the same group with a high probability (84.2%), while as the in the case of the EU, products in group 2 have 0 probability to still stay in the same group 2, but there is a 100% probability for the products with a weak comparative advantage of moving backwards to group 1. Products in group 3 have a 31.3% probability of staying in the same group 3, while products initially in group 4 will have a 46.8% chance of still having a strong comparative advantage.

Table 6 The Markov transition probability matrix of the NRCA index (China)

There is a 10.92%, 4.72% and 0.16% probability respectively for the products initially in the comparative disadvantage group (group 1) to move forward to group 2, group 3 and group 4. Note that products with an initially weak comparative advantage have no chance of moving forward to both group 3 and group 4. Products in group 3 will have a 49.74% chance of moving backwards to the comparative disadvantage group and a 18.97% chance of moving forward to the strong comparative advantage group. Moreover, products in the strong comparative advantage group will have a 53.24% chance of moving backwards to the medium comparative advantage group. Finally, the M index for China is 0.792 which is slightly higher than the EU, and it also implies a high degree of mobility in terms of China’s agricultural products trade specialization.

For the third type of trade specialization dynamics, in the EU, there are 6 agricultural product groups showing a trend according to which they will gain a comparative advantage, and this trend can be proved by the comparison between the NRCA in 2017 and the NRCA in 2001 (see the corresponding positive number in the last column in Table 7). These product groups are meat, dairy products, animal originated products, preparations of vegetables, beverages and wool. The result for HS50 which is silk is very close to 0 which indicates an unchanged pattern in the future. The rest of the products show a downward trend in the future which implies a loss of comparative advantage vis-à-vis China.

Table 7 Trend analysis results for the EU at a 2-digit level between 2001 and 2017

In China, there are only two product groups showing an upward trend in terms of obtaining a comparative advantage in the future, and this is also proved by the positive value of the dispersion between the NRCA in 2017 and the NRCA in 2001 (see the last column in Table 8). These two product groups are lac, gums, resins (HS13) and vegetables plaiting materials (HS14). The rest of the product groups show that they are tending to lose their comparative advantage in the future vis-à-vis the EU.

Table 8 Trend analysis results for China at a 2-digit product level between 2001 and 2017

However, in the EU, the degree of trade complementarity tends to decrease in edible vegetables (HS07), meat preparations (HS16), sugar (HS17), silk (HS50), wool (HS51), cotton (HS52) and vegetable textile fibres (HS53); in China, fish (HS03), dairy products (HS04), cereals (HS10), vegetable plaiting materials (HS14), animal or vegetable fats (HS15), meat preparations (HS16) and sugar (HS17) are the product groups for which China trend to lose trade complementarity vis-à-vis the EU’s demand (see Table 9).

Table 9 Trend analysis of TCI for both the EU and China at a 2-digit product level between 2001 and 2017

Conclusion

This paper has analysed the EU and China’s comparative advantages and trade complementarity in selected agricultural products both qualitatively and quantitatively. The fact is that the EU highly produces a variety of crops which include cereals, potatoes, sugar beet, vegetables (tomatoes, carrots and onions) and fruits (apples, oranges and grapes); livestock and meat which includes cattle (veal and beef meat), sheep and goats (sheep and goat meat) and pig (pig meat); and milk and dairy products such as whole milk, skimmed milk and dairy products which are processed from the raw milk product. Tables 10, 11, 12, and 13

The national and international trade policies are playing an important role in EU-China agricultural trade. The agricultural products have a comparative advantage mainly because of policy support or protection. For example, China’s comparative advantage on rice can be traced back to the strong protection, whereas EU’s comparative advantage in dairy products is largely influenced by the abolition of milk quotas in the EU.

China focuses its production on cereal products which are rice, wheat, corn millet and sorghum; beans such as soy beans; and tuber crops like potatoes. Also, China highly produces oil products such as peanuts, rapeseeds and sesame. Besides, cotton, red and yellow flax, sugarcane, sugar beet, tobacco, vegetables and fruits are also the main agricultural products of China. These different products in the EU and China are determined by each country/region’s natural endowments and quality of labour force, as well as its agricultural policies.

Results of the quantitative analysis show that the EU’s comparative advantage in the international agricultural products trade is in meat products, dairy products, animal originated products, preparations of cereals or milk products, beverages, wool and vegetable textile fibres. Moreover, the EU will retain or even increase its comparative advantage for the EU in these products. However, the EU should be aware of those product groups which also will lose comparative advantage in the future, for example, edible vegetables, coffee, tea, sugar, cocoa and food waste.

From the Chinese viewpoint, fish, animal originated products, edible vegetable, lac, gums, resins, vegetable plaiting materials, preparations of vegetables, silk, wool and vegetable textile fibres are the products for which China has a comparative advantage in the world agricultural product markets. However, apart from lac, gums, resins and vegetable plaiting materials, China tends to lose its comparative advantage in the rest of these product categories in the future. Therefore, it is very important for China to explore how to maintain its comparative advantage in some products and also to exploit its new comparative advantage through structural change in the Chinese agricultural sector along with the reform of its agricultural policy.

Since China’s accession to the WTO and the implementation of the Belt and Road Initiative (BRI), its bilateral trade in the agricultural sector with the EU has increased remarkably. Fang and Shakur (2018) find that China’s demand for food imports continues unabated, and trade cost involving agricultural products between China and EU have been decreasing but still remain noticeably high. The situation brings both challenges and opportunities. On the one hand, globalization and the implementation of the BRI encourage China-EU bilateral trade. On the other hand, as EU is not the only economy that trades with China, the agricultural trade between EU and China is in danger of being diverted to other countries along the BRI. According to our research, to keep and increase the competitiveness in agricultural products, more attention should be placed on dairy products, coffee, tea, cereals and animal or vegetable fats for the EU because these products are only having strong trade complementarity on the EU side and highly fit into the Chinese import demand. However, they may lose competitiveness in the future. China should highly value products such as edible vegetables, milling products, meat preparations and wool which show a strong trade complementarity on only the Chinese side. In addition, negotiation of an FTA between China and EU may also protect agricultural trade to both EU and China and avoid trade diversion.

Appendix

Table 10 China’s exports to EU28 from 2001 to 2017 in million US dollar

Table 11 China’s imports from EU28 from 2001 to 2017 in million US dollar

Table 12 EU’s exports to China from 2001 to 2017 in million US dollar

Table 13 EU’s imports from China from 2001 to 2017 in million US dollar