Structural Upgrade of China’s Commodity Trade during 1987-2014
Liu Zuanshi (刘钻石) and Zhang Juan (张娟) School of Business, East China University of Science & Technology (ECUST), Shanghai, China Institute of International Business, Shanghai University of International Business and Economics (SUIBE), Shanghai, China
Abstract: This paper investigates the structural upgrade of China’s commodity trade over the past two decades from the perspectives of commodity categories, technical value-added and quality level. Based on the analysis of commodity categories, technical value-added and quality, this paper arrives at the following findings: High technology manufactures accounted for a growing share of China’s commodity export, the overall technical level of Chinese exports significantly upgraded, and most of Chinese commodities upgraded from low quality to medium- and high-quality levels. As can be seen from the structure of China’s bilateral trade with its five major trading partners, China’s exporting goods remained inferior to importing goods in terms of technology and quality despite their quality upgrades.
Keywords: trade structure, commodity categories, technical level, quality level JEL Classification Codes: F14; C33 DOI: 1 0.19602/j .chinaeconomist.2018.09.07
Over the past four decades, China’s growing trade has become an important part of its economy with great international significance. By constant 2005 prices in US dollars, China’s export totaled USD 2.11 trillion in 2014, accounting for 22% of China’s GDP. In the same year, world total export amounted to USD 19 trillion, of which China accounted for 13%, contrasting to its share of 1% in 19821. With China’s rapid export growth, its trade structure upgrade has also become an important topic of research.
Some researchers believe that China’s export remains at the medium- and low- end level in terms of technology and structure despite its substantial trade growth in quantity. Using manufacturing enterprises’ questionnaire data, Liu and Zhang ( 2009) argues that technical innovation, human capital and capital intensity as emphasized in classical trade theories did not become determinants of local Chinese firms’ exports. They believe that local Chinese firms may be locked up by major international sellers at the low-end processes in the global value chain, which prevents them to improve export competitiveness. According to Shao and Xu (2009), China’s trade structure remained relatively stable since the 1990s, and no significant change occurred in the trade equilibrium state for most commodities at the beginning and ending of the sample period. Shi et al. (2009, 2010) considers that with medium- and low-end products accounting for 70% of
China’s exports, China’s trade structure did not improve significantly, and fell behind economic development. However, other studies reveal a significant improvement in China’s trade structure. Using panel data, Lu and Li (2007) tests the stability of China’s trade structure and comparative advantage during 1987-2005. Their findings suggest a change in China’s comparative advantages without any sign of lock- up effect from comparative advantage trap. Using a quality upgrade multidimensional model, Sun et al. (2014) and Liu et al. (2015) estimate empirically the quality upgrade of Chinese exporting goods to the international market from absolute and relative quality perspectives. Their studies find a significant quality upgrade of Chinese exporting goods, whose quality exceeds world average level.
These studies drew different conclusions because they adopted different time dimensions and estimation methods. In order to address such inconsistency, this paper employs trade data from various countries over the past two decades to investigate China’s trade structure upgrade from commodity category, technical value-added and quality perspectives. This paper’s main contributions are estimation of China’s trade structure across a long timeframe from 1987 when China’s trade data is available for the first time in the UNComtrade database, to 2014, and comparative analysis using three estimation methods leading to comprehensive and robust conclusions. The paper focuses on China’s commodity trade, which is the subject of research for the three methods and stands a dominant status in international trade. It is divided into five parts as follows.
2. Structure of China’s Commodity Trade 2.1 Commodity Classification
According to the UNComtrade database, there are three types of commodity classification: Harmonized System (HS), Standard International Trade Classification (SITC) and Broad Economic Categories (BEC) 2. SITC method is often employed in the research on international commodity trade structure, and has four versions of statistics (Rev.1-Rev.4) developed in different periods of time; 1-5 digit classified commodity trade data can be obtained from the UNComtrade database. SITC one-digit or two-digit code products are classified into primary products and manufactured product according to the level of processing, and high-tech, medium-tech and low-tech products according to the level of technology density (Worz, 2005). SITC one to two-digit code classification is too rough to accurately reflect commodities’ hierarchy. A more common approach is three- digit code for the analysis of commodities. For three-digit SITC code products, Lall (2000) classifies commodities into primary products, resource based manufactures, low technology manufactures, medium technology manufactures and high technology manufactures, as detailed in Table 1. Numbers in the table are SITC-Rev.2 threedigit codes.
2.2 Export Share and Comparative Advantages of Chinese Goods
Using commodity classification designed by Lall (2000) in Table 1, we may conduct an analysis of China’s trade structure. Referencing to Balassa’s (1965) trade share and comparative advantage
In international trade, sovereign states have developed trade classifications and codes of various versions, resulting in the poor comparability of statistics. From the beginning of the 19th century, the international community started to develop an international unified commodity classification catalogue. In 1948, the United Nations Statistics Division formulated the Standard International Trade Classification (SITC); in 1950, the Economic Commission for Europe formulated the Customs Co-operation Council Nomenclature (CCCN). The World Customs Organization (WCO) adopted the International Convention for Harmonized Commodity Description and Coding System at the 61st Meeting of 1983 for "harmonized" coverage of CCCN and SITC classification code systems, which was implemented as of January 1, 1988. Prior to 1996, Chinese customs adopted SITC codes. As of January 1, 1992, Chinese customs officially adopted HS. On January 1996,HS codes became officially implemented for China's trade classification.
specifications, we may provide the following two indicators to measure trade structure variable:
Herein, denotes the export value of goods category of country based on SITC-Rev2 three-digit trade data from the UNComtrade database. The earliest available trade data of China in this database dates back to 1987. In order to develop a long-term perspective, the timeframe of data selected for this paper’s is 1987-2014. Based on equations (1) and (2), we may calculate Table 2:
Table 2 provides ratio value and rca value of sectors in China in terms of technology during 19872014. First, ratio value analysis is done. In order to reduce the impact of data volatility, we may use the five-year mean value of ratio to analyze trade structure change. As can be seen from Table 2, the mean value of ratio for primary products had a significant downward trend, which is 23.9% during 19871991 and only 3.15% during 2010-2014. The mean value of ratio for resource based manufactures was more stable, which is 11.24% during 1987-1991 and 8.34% during 2010-2014. The ratio value of low technology manufactures was higher than 30% over the past two decades, with an declining trend in
general. The ratio value of medium technology manufactures increased, slowly with an average 19.17% during 1987-1991 and 23.98% during 2010-2014. The share of high technology manufactures in China’s trade structure increased rapidly, which begins at 3.65% and reaches 32.13% in 2014 and is higher than 30% over the past decade.
Then, rca value analysis is done. According to the rca indicator designed by equation ( 2), when rca> 1, China’s exporting product has a comparative advantage in the international market; when rca< 1, China’s exporting product does not have a comparative advantage in the international market. During 1987-1993, China had a comparative advantage for primary products. In 1994, such a comparative advantage diminished. China failed to develop comparative advantage for resource based manufactures, which is consistent with China’s scarce resources per capita. China had significantly a comparative advantage for low technology manufactures, and its value is greater than 2. For medium technology manufactures, its rca value had been less than 1 despite of its increase. For high technology manufactures, its rca value increased significantly from less than 1 before 2000 to greater than 1 afterwards which stands comparative advantage.
As Table 2 shows, China’s trade structure significantly upgraded as calculated based on Lall’s (2000) commodity classification. With the growing share of high technology manufactures, China’s comparative advantage tended to upgrade from primary products to high technology manufactures. However, Chinese exports and comparative advantage are still dominated by low technology manufactures.
2.3 Share of High Technology manufactures in Trade for China and Its Major Trading Partners
To further analyze China’s trade structure upgrade using Lall’s (2000) classification of export commodities, this paper examines the bilateral trade data of China’s top five trading partners in 2014 comprised with the U.S., Japan, South Korea, Germany and Australia. China’s bilateral trade value with these five countries accounted for 33% of China’s total trade value. Table 3 provides the share of high technology manufactures in total bilateral trade with the five trading partners during 1987-2014.
As can be seen from Table 3, high technology manufactures represented a growing share in China’s exports to the U.S. over the past two decades, up from 2.54% in 1987 to 36.48% in 2014, which reflects an upgrade in Chinese exports to the U.S. There was an insignificant increase in the share of high technology manufactures in U.S. exports to China. In 1987, high technology manufactures accounted for 27.86% of U.S. exports to China, and this ratio only increased to 30.29% in 2014. Over the past two decades, the share of China’s high-tech exports increased and overtook that of China’s high-tech imports, which further implies an upgrade in China-U.S. trade structure.
High technology manufactures represented a growing share of China’s exports to and imports from Japan, up from 0.95% and 19.21% in 1987 to 29.66% and 30.93% in 2014 respectively, reflecting more significant upgrade in export than in import. Export upgrade was also more significant in China’s bilateral trade with South Korea. There were less high technology manufactures in China’s exports to South Korea than in China’s imports from South Korea, which reflect that South Korea’s exports to China were more tech-intensive than China’s exports to South Korea. China and Germany’s bilateral trade data is available since 1991, with the share of high technology manufactures in total imports and exports peaking around 2005 and decreasing thereafter. High technology manufactures represented a growing share of China’s exports to Australia, but a tiny share in China’s imports from Australia which barely increased over the past two decade.
The following conclusion can be drawn from the share of high technology manufactures in China’s bilateral trade with the five countries: China’s exports to these countries became increasingly tech-
Data source includes Comtrade database. China's top 10 trading partners also include Chin’s Hong Kong and other regions of Asia. Since Hong Kong is primarily engaged in transit trade and the trading entities of other regions of Asia are not clear, they are omitted in this paper.
intensive, while the level of technology barely changed in its imports. However, China’s exports is still less tech-intensive compared with its imports.
3. Technical Value-Added of China’s Commodity Trade
Lall’s (2000) product classification is a rough classification based on the manufacturing method or final use of goods. Given the subjectivity of product classification, there can be some deviations in the estimation of China’s trade structure. In this section, China’s trade structure is further estimated using technical value-added indicator.
3.1 Technical Value-Added of Goods
Technical value-added of a product is determined by technology‘s contribution to the product’s value-added. Guan (2002) assumes that “products with higher value-added are more likely to come from high-income countries.” Product technical value-added is denoted as the GDP per capita-weighted value of exporting countries, and the weight is the world market share of the product’s exports by each country. Fan (2006) develops the revealed technical value-added assignment principle to calculate the revealed technical value-added of a product using two variables . The first variable is the revealed comparative advantage of each country in exporting the product, and the second variable is the technology factor abundance of each country . Equation for calculating revealed technical value-added is as follows:
Herein, denotes the revealed technical value-added of product type j; denotes the export value of product type j of country i; gdpper denotes the per capita GDP of country i, and all variables are measured as 2005 constant price US dollar; n and m respectively denote the numbers of countries and sectors.
On the basis of technical value-added method, Liu and Zhang (2010) estimates the technical valueadded indexes of various commodities during 1995-2006 excluding temporal variation trend, which enables the intertemporal and cross-regional comparison of commodities and trade structure. This marks an improvement of technical value-added method. This paper utilizes the technical value-added index developed by Liu and Zhang (2010) to estimate the technical level of commodities. The technical valueadded index of product type j can be expressed as:
According to the revealed technical value-added assignment principle, "for a country with relative advantage in a certain product, the product’s technical value-added will increase with the abundance and the extensive application of such technology; in this case, the product can be assigned with a higher technical value-added index.”
Countries with more abundant technology factor also boast higher total factor productivity (TFP). Based on data availability, labor productivity and per capita GDP per capita can be used to replace technology abundance. Calculation method is as follows: For a specific product, all countries with RCA greater than 0 are selected, and the per capita GDP per capita of these countries is used to denote the product’s revealed technical value-added; weight is the ratio between a country's such product and the sum of RCAsof all countries..
The technical value-added index of SITC three-digit commodities can be estimated using equation (4). Since the results are standardized, the value of product type j in different years is comparable.
3.2 Estimation of Technical Value-Added
This section estimates the technology level of commodities using technical value-added method. According to equation (4), the SITC-Rev2 three-digit trade data in the UNComtrade database and GDP per capita in the World Bank WDI database (2005 constant prices in US dollar) are employed to calculate the TCI value of commodities. The Greater the TCI value, the higher the technical value-added of commodities. Based on technical value-added calculated according to equation (4), the equation for a country’s overall trade technology level can be created as follows:
Equation (5) can be used to calculate the export technology levels of various countries. Table 4 identifies the top 10 countries ranked by exlevel1 value in 1994, 2004 and 2014, and their exlevel1 value, share of export value in world total export as well as GDP per capita (2005 constant price in 10,000 US dollars). In order to facilitate comparative analysis, Table 4 provides China’s relevant indicator values, and the values in parentheses are the world rankings of China’s exlevel1 values: In 1994, China’s export technology ranked the 39th in the world. By 2004, this ranking increased to the 30th in the world. By 2014, it jumped to 25th in the world. Over the past two decades, some changes occurred in the list of top 10 countries by the technology level of export. For instance, exlevel1 values of the U.S., Japan and Germany ranked respectively the 8th, the 1st and the 4th in 1994. By 2014, the U.S. fell outside the top 10, while Japan and Germany ranked the 4th and the 5th respectively.
Among the top 10 countries in Table 4, many are small trading nations. For instance, Switzerland, whose value ranked the 2nd in the world in 1994, only accounted for 1.87% of world total exports. Ireland, whose value ranked the 1st in the world in 2004 and 2014, represented 1.18% and 0.67% of world total exports in the two years respectively. Most countries in Table 4 are rich countries with GDP per capita above 20,000 US dollars. As can be seen from equation (3) and Table 4, trade technology level calculated using technical value-added method is not closely related to the size of an economy. Instead, it is highly correlated with the level of its economic development.
To give a clearer picture of China’s trade structure upgrade over the past two decades, this paper uses exlevel1 values of China, the U.S., Japan and India to respectively simulate the curve of change in
their export technology level, which is detailed in Figure 1. As can be seen from Figure 1, the relative positions of these four countries in the export technology level over the past two decades remained unchanged. Japan’s level of technology is the highest, followed by the U.S., China and India. Figure 1 shows that the export technology levels of the four countries decreased firstly and increased later, which is probably due to a change in the commodity statistics in a long period. Take the data after 2000 for instance, China’s exlevel1 value has been increasing, and almost approached the U.S., which reflects a growing technical value-added of China’s trade structure.
3.3 Technical Value-Added of Bilateral Trade between China and Its Major Trading Partners
This section estimates China’s bilateral trade structure using technical value- added method. Referencing to the specification of exlevel1 value in equation (5), we may calculate the technology level of bilateral trade between China and its five major trading partners shown in Table 5 (note: for clarity, numbers in the table are scientific notations E-05).
Table 5 identifies the export and import technology levels of China, the U.S., Japan, South Korea, Germany and Australia during 1987-2014 using technical value-added method. Table 5 shows that based on technical value-added method, there was some volatility but no significant improvement in the technology level of Chinese exports to the U.S. over the past two decades. But China’s imports from the U.S. became less tech-intensive. China’s bilateral trade with Japan, South Korea, Germany and Australia shared a similar tech-intensive nature with China-US bilateral trade. China’s exports to Japan, South Korea and Germany were all less tech-intensive than its imports from them. However, China and Australia’s export technology levels were higher than their import technology levels. As can be seen from Table 5, China’s bilateral trade upgrade estimated using technical value-added method was not significant. China’s exports were less tech-intensive than its imports in general.
4. Quality of China’s Commodity Trade Structure
Part 2 and Part 3 provide an analysis of China’s export commodity categories and technical valueadded under a basic assumption consistent with SITC three-digit commodity trade. The reality is that for commodities under the same names, their quality varies significantly across countries. In estimating a country’s trade structure, we should take into account such quality differences. This section estimates the quality level of China’s commodity trade structure.
4.1 Method for Estimating Commodity Quality
The popular method to estimate commodity quality level is to study commodity quality differences. Commodity quality is measured by the unit export price of product (Schott, 2004; Hummels and Klenow, 2005). Commodity quality differences method is used to measure the commodity quality differences between a country and world average level. This method can be classified into relative quality method and frontier quality method.
If a country’s commodity price difference with world average price indicates the quality level of the country’s such commodity, the method for estimating commodity quality is called relative quality difference method. In order to ensure the price comparability of different types of products, Shi (2010) makes an improvement to the relative quality difference method of Mulder et al. (2009). Using Shi’s method, this paper creates the relative commodity quality difference variable with the following method:
Herein, denotes the quality difference of country i’s commodity j with world average level;
is the price of product j exported by country i, which denotes the quality of this country’s export product j; is the average price of export product j for all countries, and represents the average quality level of product j. The more the value than zero, the higher the quality of the country’s export product j than the average quality of comparable products in the international market.
is characterized by its boundedness, which is in the range of (-1, 1). In this manner, all products can be analyzed and compared with the same standard. Referencing to Azhar and Elliott (2006), is divided into the following three ranges: If ≥ 0.15, product export price is considered to be significantly higher than world average price, and the country’s products are in the high quality category. If < -0.15, product export price is significantly lower than world average price, and the country’s products are in the low quality category. If -0.15≤ <0.15, the product’s export price is similar to world average export price, and the country’s products are in the medium quality category. Using value, a country’s commodities can be classified into high, medium and low categories for the analysis of the country’s export structure.
If the difference between a country’s commodity price with world maximum price indicates the quality level of the country’s such commodity, the method is called frontier quality difference method. Commodity frontier quality difference variable is created referencing to Khandelwal and Fajgelbaum’s (2013) method, and the calculation method is as follows:
Herein, denotes the quality difference of country i’s commodity j with the world’s maximum; is the price of product j exported by country i, which denotes the quality of this country’s export product j; is the maximum price of export product j for all countries, and represents the frontier quality level of product j. With the above equations, this paper is able to examine the difference of country i’s product j with internationally highest quality. Closer value of to 1 suggests a smaller difference in the quality of the country’s product j with international frontier quality of the same type of exporting product. Closer value of to 0 suggests a broader difference in the quality of the country’s product j with international frontier quality of the same type of exporting product.
4.2 Quality Structure of China’s Export Commodities
UNComtrade database provides not only each country’s export value of SITC-Rev2 three-digit commodities, but also their export quantities expressed in net kilograms. This paper uses the ratio between each category of commodities’ export value and export quantity to calculate price of unit exporting goods in equation (6). Considering the existence of statistical errors for some countries, this paper does not include samples with the highest 1% prices and the lowest 1% prices in the calculation to avoid ’s extreme value.
After calculating the export prices P of products from various countries, this paper firstly conducts a comparative analysis of changes in the quality level of various commodities. According to the Lall’s (2000) commodities classification in Table 1, the product quality ratio of each commodity category can be calculated using equation (6). In order to avoid the impact of volatility, this paper calculates the share of export value for each commodity category in every four years, as detailed in Table 6. As can be seen from the table, lowquality, medium-quality and high-quality products accounted for certain ratio of China’s exporting primary products over the past two decades. However, the ratio of low-quality products has been falling, and the ratio of medium-quality products has been rising. The quality of resource based manufactures has been stable, but the overall level of quality remains low.