1 Introduction

China’s foreign trade policy has played an essential role in the APs exports. China has maintained a fast growth of APs export since entering the World Trade Organization (WTO) in 2001. Statistics show that the total Chinese APs export expanded almost four times with an average annual growth rate of 7.99%, from $23.36 billion to $87.07 billion during 2002–2020. China has become a significant driving force for developing APs trade globally. Its share (world ranking) of global APs exports grew from 4.90 to 5.32% (5th–4th) over the same period in the world of APs exporters (COMTRADE, 2022).

However, since 2017, China’s economy has entered a new paradigm, emphasizing the need for a better quality of development rather than faster growth (Xi, 2017). The quality of exported goods became more important (Li, 2018), especially while the low labour costs advantage of “made in China” no longer exists and environmental regulation has been constantly strengthened. Chinese government and manufacturers should prioritize upgrading the quality of export products, and one way to improve the export quality is to gain access to advanced technology (Anwar & Sun, 2018).

In economics, whether a product is of high technology depends on how much technology contributes to its value addition. Hence, export technology can be measured as value added by the technology component (Guan, 2002; Lall et al., 2006). This technology value-added refers to the incremental value due to the use of advanced technology during the production process of the exported products. From the perspective of sustainable trade patterns, increases in export volume should bring significant improvements in the share of advanced technology used in export-oriented products (Du & Wang, 2007). For this reason, we need to explore the export technology structure deeply. In the Chinese context, it is particularly important to understand the distribution, overall level, and evolutionary trends of the technology structure of APs export and its performance compared to key global exporting countries. Exploring these issues will help to clarify the relative position of China’s agriculture sector and provide evidence for strategy formulation to improve its international competitiveness. This strategy may also provide useful insights about how China can avoid APs export going down-market and gain higher benefits in the international markets.

Theoretically, the export technology structure is inseparable from international trade theories. According to Ricardo (1817), production technology differences are the basis of comparative advantage, and countries should export those products where they have a comparative advantage. Neoclassical economic theories further suggest that the comparative advantage does not originate from technology but from different resource endowments (Heckscher, 1919; Ohlin, 1993). New trade theories show that countries with similar endowments have bilateral trade, and countries lacking natural resources can still perform outstanding in global trade. These observations brought serious challenges to the old concept and gave birth to a new concept of competitive advantage, suggesting that competitiveness is based on comparative advantage (Attila & Babu, 2017). Factor endowments do not simply determine national export technology structure but are largely determined by industry innovation and upgrading capacity (Porter, 1990). Based on the cost discovery theory, Hausmann et al. (2007) further proposed a theoretical assumption: the more technical the exported products coming from high-income countries, the higher the sophistication of the products. Overall, the conclusions that comparative advantage is the source of export technology structure and is related to the development level of the national economy have been widely recognized. And previous studies have focused on the change in the export technology structure (Cao & Hanson-Rasmussen, 2018; Deng & Hou, 2017; Duan, 2017) and its international comparison among countries(Schott, 2007; Z. Wang & Wei, 2010). However, few studies(He et al., 2012) pay close attention to the comparison between China and other countries in APs' export structures and evolutionary trends. He et al. (2012) found the competitive advantage complementarity among China, Japan, and South Korea by examining the overall technology structure of APs exports. However, this study compared China’s APs export with only two major global economies. It failed to apply the technical height to further study the upgrading trend of export technology structure compared to other economies. To this end, this paper will empirically measure the changes in China’s APs export technology with both technical complexity and technical height and compare the results with those of all the world’s major exporters of agricultural products.

The existing measures on technology structure consist of two categories, i.e. the technology classification method and the index method. The first is to measure the technology structure based on technology classification standards (Hatzichronoglou, 1996; Lall, 2000; Pavitt, 1984). However, this classification relies on the experts’ experience, tends to be subjective, and generates overlaps between categories (Zhu & Chen, 2010). The index method (primarily rooted in estimating technology value-added) is the most frequently used way of technology structure analyses. Relevant literature has put various technology value-added measures forward and built a generalized consensus. The technology value-added value of a product is closely related to the income level of its producer, and the product produced by high (low) income economies also features higher (lower) technology value-added value (Felipe et al., 2013). Based on this fundamental principle, Lall et al. (2006) proposed a complexity (sophistication) index to measure the technology value-added of a product. It took a weighted average of the per-capita gross domestic product (GDP) of the countries exporting a product, where the weights are the world share of each country’s exports. However, this assignment of weights may overestimate the role of leading powers and ignore the exports of products with comparative advantages among small countries (Zhu & Fu, 2013). Du and Wang (2007) revised this index by adjusting the weights to each country's world production share in a particular product and constructed a technical height index (\(THI\)) to map out the upgrading trend of export technology structure in one country compared to other economies. However, the data on world share in production is not available directly. Hausmann et al. (2007) firstly constructed a “product-relevant income index” (\(PRODY\)) to embody an associated productivity level for each good, using the revealed comparative advantage (\(RCA\)) as the weights. Then, the productivity level that corresponds to a country’s export basket (\(EXPY\)) was constructed, by calculating the export-weighted average of the \(PRODY\) for that country. By contrast, this method (also known as the technical complexity index) is more commonly used, which has a clear advantage in data accessibility and can overcome the possible deviations in the calculation process (Fan et al., 2006). Besides, this [index] method is like the revealed technology value-added (RTV) method, each of which presents its solid theoretical basis to prove the rationality of taking RCA as weights. Therefore, this article chooses the technical complexity index method to empirically measure the \(PRODY\) and \(EXPY\) of China’s APs export, and then refers to the practice of Du and Wang (2007) to construct the technical height index for further analysing its evolutionary trends.

Although many classification methods for \(PRODY\) values are available, that leads to distinct conclusions under certain limitations. They mainly include fixed technical classification (Zhu et al., 2009), experience sorting method (Tang, 2012), optimal segmentation method (Wei, 2015), equal worldwide share method (Sun & Li, 2016), and equilibrium technology method (Cao et al., 2018). In comparison, the equilibrium technology method (\(ETM\)) can ensure the \(PRODY\) value differences equal for products of adjacent technology grade (high to low technological levels) and avoid the classification results being limited and influenced by time change and human experiences (Cao & Hanson-Rasmussen, 2018). Thereby, we adapt the \(ETM\) for APs technical complexity classification.

Previous studies have also explored the technology structure of China’s exports mainly at the macro level (Jarreau & Poncet, 2012) or sector level within the economy, e.g. industrial manufacturing (Gao et al., 2020; Zhang, 2022), services (Lu & Fu, 2018) and agriculture (Bai, 2020; He et al., 2012; Sun & Li, 2015; Yin & Tian, 2013) levels. However, some studies in the agriculture sector only defined the exported APs with fewer categories based on the Harmonized Commodity Description and Coding System (HS) and sample countries (Bai, 2020; He et al., 2012; Yin & Tian, 2013). The studies of (Sun & Li, 2016) and Bai (2020) concluded that the technical structure of China’s APs export had upgraded significantly. These results might not be reliable without using a more objective classification method—instead of considering an equal worldwide share—and combined analysis of \(EXPY\) and \(THI\) together. Therefore, the present paper is designed to revisit the technology structure of China’s APs export with (1) extended scope of the traded APs; (2) inclusion of the maximum countries; (3) improved classification method and comprehensive indices. The key novelty is a comparative analysis that scientifically judges the trade pattern and upgrading trend of technology structure of China’s APs.

The remainder of this paper is structured as follows. We begin in Sect. 2 with our measure of export technology structure and the dataset used. We then discuss our results in Sect. 3 and the conclusion and policy implications in Sect. 4.

2 Methodology

2.1 Contextualizing APs

There is no universal standard for the definition and classification of APs. We have defined the APs based on HS, a multipurpose international product nomenclature developed by the World Customs Organization. The HS (2002 version) comprises over 1200 items at the 4-digit level and over 5,000 items at the 6-digit level. The system is used worldwide to apply customs tariffs and collect global trade statistics. The present study defines the exported APs by 26 different commodities (HS codes), including HS01-HS24 and HS51-HS52 (see Table 1 for the product description).

Table 1 The technology structure distribution of the world’s APs
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2.2 Sample selection and data sources

This study selected the world’s 178 APs exporting countries from the developed and developing regions from 2002 to 2020 (please see A1 in Appendix A). During this period, these economies' cumulated APs export volume accounted for over 99.52% of the world’s total APs exports. This strong contribution makes a comprehensive and robust representative sample compared to previous studies conducted in agriculture (Bai, 2020; He et al., 2012; Sun & Li, 2016; Yin & Tian, 2013).

The export data were retrieved from the same HS code system to ensure data consistency and reduce measurement errors in product classification. We used the HS 2002 for commodity codes and data derived from the COMTRADE database. The annual GDP per capita (in constant 2017 international $) of each exporter was retrieved from the World Development Indicator (WDI) database (The n.d.) of the World Bank (A2 in Appendix B).

2.3 Methods

We need to (1) measure the technical complexity level of each type of APs export and exporting country using the technical complexity method (TCM). There are two steps involved in calculating the technical complexity level. Initially, each year, the annual GDP and export value of different products are used to construct a product-relevant income index PRODY. Then, weighted averages are calculated based on each country's export basket, called the overall technical level (\({\text{EXPY}}\)). (2) Classify APs into five technological levels based on the annual average of PRODY values using the ETM; 3) Calculate the technical height index to further analyse the evolutionary trend of the technology structure of different countries. Each of the steps was explained in detail in respective sections.

2.4 Technical complexity measure

We applied the Hausmann et al. (2007) measure of the technical complexity of a country’s export basket. In this measure, each type (HS classification) \(k\) that a country can potentially produce and export in year \(m\) has an intrinsic level of technical complexity (\({PRODY}_{mk}\)), which is a weighted average of the income level of countries exporting \(k\), where the weights correspond to the \(RCA\) of each country \(j\) in type \(k\). Exporting more type \(k\) product from rich countries, the higher its \(PRODY\).

$$PRODY_{mk} = \mathop \sum \limits_{j = 1}^{n} \frac{{x_{mjk} /X_{mj} }}{{\mathop \sum \nolimits_{j = 1}^{n} \left( {x_{mjk} /X_{mj} } \right)}} \times Y_{mj}$$
(1)

where \(n\) = number of countries, \({Y}_{mj}\) = GDP per capita of the country \(j\) in year \(m\), \({x}_{mjk}\)=export value of type \(k\) APs of the country \(j\) in year \(m\), \({X}_{mj}\)=total APs export of the country \(j\) in year \(m\).

The technical complexity level of country \(j\)'s APs export in year \(m\), denoted by \({EXPY}_{mj}, i\) s then calculated as the average level of the technical complexity of its APs export basket. This level is a weighted average of the \({PRODY}_{mk}\) for country \(j\), the weights are the value share of the \(k\) APs in that country’s total APs exports.

$$EXPY_{mj} = \mathop \sum \limits_{k = 1}^{s} \frac{{x_{mjk} }}{{X_{mj} }} \times PRODY_{mk}$$
(2)

2.5 Equalization technology classification method

The basic principle of this method is to ensure the equality of the \(PRODY\) value difference of products from adjacent technological levels. This method provides a relatively objective way to cluster the same level of products into similar categories based on \(t\) grades (high to low technological levels), and there is no restriction on the number of products owned by each level. This method consists of three steps. These are,

  1. (1)

    Sorting the \(PRODY\) of \(s\)-type APs, i.e. {\({PRODY}_{1}, {PRODY}_{2}, \cdots ,{PRODY}_{n}\)} are arranged in ascending order.

  2. (2)

    Orderly \(n\) sampled was divided into the number \(q(1, 2, 3, \dots , t )\) level, denoted by\(p(n,q)\). The \(PRODY\) value grading of APs is\(D\). Whereas \(D\) is equal to\(({a}_{n}-{a}_{1})/t\).

  3. (3)

    Finally, \({\text{n}}\)-ordered and \({\text{t}}\)-graded levels of \(PRODY\) values are classified with the help of the following technical classification criteria.

e.g. if \({a}_{1}\le {a}_{1}+D\), \({a}_{2}\le {a}_{1}+D\), \(\cdots\), \({a}_{k}\le {a}_{1}+D\), \({a}_{k+1}>{a}_{1}+D\), \(\cdots\), \({a}_{n}>{a}_{1}+D\), then \(p\left(n,1\right)=\left\{{a}_{1},{a}_{2},\cdots ,{a}_{k}\right\}\).

2.6 Technical height measure

The technical complexity of good \(k\) may be relatively high or low with the widespread improvement of the world’s technological level over time. In other words, whether type \(k\) is a high-technology or low-technology product depends on the technical complexity of all products in the same period. Therefore, we first construct an index called \(THI\) to represent the technical height level of each type of exported APs.

$$THI_{mk} = \left( {PRODY_{mk} - PRODY_{mmin} } \right)/\left( {PRODY_{mmax} - PRODY_{mmin} } \right)$$
(3)

Here, \({PRODY}_{mmin}\) and \({PRODY}_{mmax}\) represent the minimum and maximum technical complexity of all APs in year \(m\), respectively.

The technical height level of country \(j\)’ APs export in year \(m\), denoted by \({ETHI}_{mj}\), is the weighted sum of the technical height levels associated with each exported good \(k\). Therefore, \({THI}_{mk}\) is the weighted value shares of each type of APs in the country’s total exports.

$$ETHI_{mj} = \mathop \sum \limits_{k = 1}^{s} \frac{{x_{mjk} }}{{X_{mj} }} \times THI_{mk}$$
(4)

Excluding the upgrading of technology structure caused by the world’s common technological advancement, the calculated \(ETHI\) values changing over time represent the evolutionary trend of a country’s technology structure of APs export, compared with the other economies in the world.

3 Results and discussion

3.1 Technical complexity structure determination of APs

The technical complexity of various APs worldwide is calculated using Eq. (1). Overall, the export technical complexity of different APs shows an upward trend, with an average increase of 5% from 2002 to 2020 (A3, in Appendix C). Among a variety of APs, HS01-02, HS04, HS19, and HS21-22 show higher technical complexity (the annual average \(PRODY\)>29,000$), while HS07-09, HS12, HS14, HS17-18, and HS52 are that of lower technical complexity (the annual average \(PRODY\)<17,000$).

Then, the equalization technology classification method was employed to divide the annual average of \(PRODY\) values of 26-type into five grades, i.e. high technical complexity products (\(PRODY>\) 31,690$), medium–high technical complexity products (26,155$\(<PRODY\le\) 31,690$), medium technical complexity products (20,620$\(<PRODY\le\) 26,155$), medium–low technical complexity products (15,085$\(<PRODY\le\) 20,620$), and low technical complexity products (\(PRODY\le\) 15,085$). The results of APs into five classification categories are reported in Table 1.

The result reveals that all 26 types of commodities are distributed into five different classifications of the technical complexity of products, with the frequency of high and medium–high (3 each), medium (8), medium–low (10), and low (2) in each classification. The classification of 85% APs is like that of He et al. (2012), where HS04, HS06-10, HS13-15, HS17, HS19, and HS21-22 are classified, respectively, in the same groups. These categorization differences are mainly due to the extended scope of APs, and five-scaled classification resulted in different \(PRODY\) values.

3.2 Dynamic distribution of the technology structure

Table 2 shows how China’s APs export technology structure varied from 2002–2020. This overall distribution of technology structure characterized by “big in the middle, small at both ends” indicates a similar trade pattern with high input resources rather than high technology value-added, to that in He et al. (2012) and Bai (2020). China’s APs export is concentrated in the products of low technical content (which require more land or labour input), and high-technology products are far from becoming a dominant role. From 2002 to 2020, the aggregate average export share of medium–low and low technical complexity products was ~ 46.11%, while the yearly average export proportions of high and medium–high technical APs reached 5.62% and 4.40%, respectively. This result is consistent with Du and Wang (2007), who found that China’s technology structure of APs export conforms to its resource endowment. A similar message comes from considering the share of specific APs export. Statistics show that China’s main exported APs were centred on the following items (HS code), i.e. 52 (cotton), 03 (fish and crustaceans, etc.), 16 (meat, etc.), 07 (edible vegetables, certain roots, and tubers), 20 (preparations of vegetables, fruits, etc.), 08 (edible fruit and nuts, etc.), with an aggregate average export share of 66.33% during 2002–2020, which are of medium or medium–low technical complexity (see Table 1 for the classification of APs).

Table 2 The dynamic distribution in technology structures of China’s APs export
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However, the dynamic change in the technical structure of China’s APs export presents a peculiarity of “decrease in the middle, increase at both ends,” i.e. the export proportions of high (1.13%) and low (0.67%) technical complexity products, respectively, increased substantially. In comparison, the export share of medium–low (− 0.70%) and medium–high (− 1.12%) technical complexity products reported a reduction in their contribution. This result is the direct opposite of the views of Du and Wang (2007) and He et al. (2012). Possible reasons can be seen from the different classification methods for APs and the study periods. From 2002 to 2020, the export proportion of China’s APs of high and low technical complexity jumped from 4.75% and 2.55% to 8.21% and 4.81%, with an increase of 72.84% and 88.63%, respectively. Correspondingly, the export share of medium–low, medium, and medium–high technical complexity products declined from 44.57%, 41.26%, and 6.87% to 42.66%, 40.49%, and 3.83%, with a decrease of 4.29%, 1.87%, and 44.25%, respectively. Note that the export proportions of low and high technical complexity products, although increasing, respectively, are constantly less than 9% and 5% during the study period, demonstrating a limited effect on export structure.

In summary, as a developing country and a large agricultural economy, China has shown a relatively stable export structure dominated by medium-level technology products during the study period. A slow, gradual increase in exports is shifting towards high and low technical complexity products. However, these products have no determining influence on export structure. On the one hand, China’s APs export has long been predominant in original processed raw materials and processing trade embedded in the global value chain. Hence, its advantage still falls in the land- and labour-intensive products with relatively low technical content. On the other hand, since trade liberalization in 2001, China’s APs export begun to transform, although slower, to exporting products with high technology and good quality, especially reflected in exporting processed products of grain or milk (HS19) and edible preparations (HS21), due to the relative comparative advantage, technology spillover and domestic investment into R&D. Though, China’s APs export lacks in implementing stringent international quality standards, especially on meat, egg, honey, and dairy products. This non-compliance was seriously affected by the issues and events concerning food security and animal diseases (such as “Sanlu milk powder” Shuanghui “lean” events) in the previous years (Bradley, 2008; Shao & Cai, 2016).

3.3 Comparison of technology structure of APs export in major economies

This article compares the technology structure of APs export of the world’s top ten APs exporters (see A1 for the average annual export value) during 2002–2020.

Table 3 shows how the technology structure of APs export and its change varied across countries in 2002 and 2020. The major exporting countries (apart from China and Brazil), including the USA, the Netherlands, Germany, France, Spain, Italy, Canada, and Belgium, have a relatively higher comparative advantage in exporting technology-intensive products worldwide. Unsurprisingly, compared to these developed countries, the overall technology structure of China’s APs export is still at a lower level. From 2002 to 2020, we also found a new leader (higher growth rate) in each technology structure, ranging from higher to lower. For high technology products, Italy shows a ~ 50% growth rate (23% less than China) in the export of APs compared to other international players. While Spain emerges as a leading contributor in medium–high technology APs export with a ~ 51% growth rate. The rest of the top ten exporting countries experienced a declining trend in their growth, where China reported a ~ 44% decline in the growth of medium–high technology APs export. All the international players were experiencing the same declining trend for medium-level technology products except Belgium (0.82% growth rate). China experienced ~ 2%, while Brazil reported the highest ~ 55% decline during this period. Canada (46%) and France (~ 326%), respectively, emerge as the largest contributors to medium–low and low technology APs export growth.

Table 3 The technology structure of APs exports of the world’s top-10 APs exporters in 2002 and 2020 (%)
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Regarding the changes in technology structure across countries, China’s APs export exhibits a transition pattern of “decrease in the middle, increase at both ends” to the dynamic changes of its technical structure, remarkably like the transition pattern in France. In contrast to the “both ends,” except for Belgium (only at a low level) and Brazil, the remaining exporters show a significant transition in high and low technical complexity products, which vary between ~ 50% and ~ 8% (high) and ~ 326% and ~ 8% (low), respectively. It is worth noting that the positive change in the technology structure of China’s APs export, slowly driven by the rise of high technology products, resulted in a very slight upgrading of the export structure. Additionally, the export growth rate bringing this positive change is the highest among the world’s top ten APs exporters. In the further evolutionary trend analysis, we will consider this via a specific vision.

3.4 Overall technical level of China’s APs export and its international comparison

Figure 1 shows the \(EXPY\) of China’s APs export basket and its annual growth rate from 2002 to 2020. The bar chart of \(EXPY\) represents, unsurprisingly, given the technology structure of China’s APs export, there was not much fluctuation in value contribution, ranging from 19,000 to 24,100 (US$). However, the annual growth rate showed abrupt changes during the same period. There were robust spillover effects of the global financial crisis during 2007–2009, which down poured the growth of China’s APs export (Schmalz & Ebenau, 2012). Another dip in the annual growth rate was visible during 2015–2016 when the Chinese RMB faced the pressure of expected depreciation against US$ due to gradual reforms (Das, 2019). The overall technical level of China’s APs export basket declined further, falling into the lower grade from 2009–2017. Still, the differences between China’s \(EXPY\) and medium technical complexity baseline only varied between 396$ (the year 2017) and 1564$ (the year 2012). Since October 2017, China has entered a new era of economic growth, which has induced more rapid technology adoption. Its overall technical level of APs export returned to the grade of medium technical complexity over the 2018–2020 period.

Fig. 1
figure 1

Source The authors’ calculation is based on the UN COMTRADE and WDI database

The overall technical level and annual growth rate of China’s APs from 2002 to 2020. Note(s) The graphics were drawn according to the \(EXPY\) and its growth rate.

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The technical level of China’s APs export basket was sensitive to the export policies and the international market. As a result, despite a slump in the first four years, the 2007–2012 period and the 2015–2016 period, China’s \(EXPY\) jumped to 21,844$ in 2006, 19,364$ in 2013, and 20,224$ in 2017, respectively, 4.87%, 1.62% and 3.96% up year-on-year basis. Overall, it indicates that the technical level of China’s APs export basket hovered around medium technical complexity grade but showed a slight downtrend. The \(EXPY\) of China’s exported APs dropped by 10.18% between 2002 and 2020, from 24,093$ to 21,640$.

For a comparative analysis of \(EXPY\), out of 178 countries, 13 countries from developed and developing regions with a large export volume of APs were selected for further analysis. Figures 2 and 3 show how \(EXPY\) and its average annual growth rate across sample countries vary between 2002 and 2020. The overall technical level of the sample countries in the developed region rose to varying degrees from 2002 to 2020 (Fig. 2). Over the same period, China fell behind by 0.52% on average, so the gap between China and developed countries’ \(EXPY\) more than tripled from 1590$ to 5769$. All the developed countries in our sample continuously held a leading position in the export technology structure, indicating a significant comparative advantage of developed countries over China.

Fig. 2
figure 2

Source The authors’ calculation is based on the UN COMTRADE and WDI database

The comparison of China’s EXPY and its growth rate with sample countries in developed countries. Note(s) The graphics were drawn according to the \(EXPY\) and its growth rate.

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Fig. 3
figure 3

Source The authors’ calculation is based on the UN COMTRADE and WDI database

The comparison of China’s EXPY and its growth rate with sample countries in developing countries. Note(s) The graphics were drawn according to the \(EXPY\) and its growth rate.

Full size image

Figure 3 shows China’s leading position as APs export powerhouse in developing regions has diminished between 2002 and 2020. In 2002, China, which had the highest export share in the developing region, exhibited a significant advantage at the overall technical level among our sample countries. By 2020, China, although still ranking first in APs export among developing countries, even up one spot from 2002 in the world, has dropped to the lower middle in terms of \(EXPY\), which demonstrates a real difference between the performance of "quantity” and “quality” of China’s APs export in the period 2002–2020. Overall, the exported APs in China were losing competitiveness.

These findings bring new insights to the upgrading debate regarding China’s APs export structure. Some work has suggested that the overall technical level of APs export in China was significantly improved (Bai, 2020; Sun & Li, 2016) or gradually increased (He et al., 2012). Our results differ from this trend, as shown by the corresponding period observation of line charts on \(EXPY\) (Fig. 1). Given the weakness of \(EXPY\) measure which is sensitive to the size of the countries under consideration (Kumakura, 2007) and the choice of product nomenclature (Yao, 2009), possible reasons include: (1) selecting a larger sample size which might reduce the effect of excluding some countries of a small share in APs export on \(PRODY\) values; (2) taking additional types of Aps, i.e. HS51 and HS52 (average combined export share of 22.59% between 2002 and 2020) into consideration which may generate a significant variation in \(EXPY\) value. These reasons receive a strong support from a comparison of \(EXPY\) in the four scenarios (“top 50 exporters and HS01-24”, “178 exporters and HS01-24”, “178 exporters and HS01-24 + HS5101-5103 + HS5201-5203” versus “178 exporters and HS01-24 + HS51-52”) over the study period (Fig. 4). The average \(EXPY\) values in our current study (scenario 4) are 30.8%, 3.7% and 5.23% lower than that in the scenario 1–3, respectively, during 2002–2020. It is worth noting that the overall upward trends of \(EXPY\) in scenario 1–2 are fully in line with previous literatures (Bai, 2020; He et al., 2012; Yin & Tian, 2013) provided the same APs scope (HS01-24), which confirms the validity and compatibility of the results using the larger sample. Additionally, except for HS5101-5103 and HS5201-5203, the other types of HS51 and HS52 belong to processing products (R. Wang and Xiao 2021). Hence, the overall downward trend of \(EXPY\) values in our current study can be further accounted for by taking the processing-trade of HS51-52 into account. In the general case, once considering China’s processing-trade regime, Chinese exports look similar to those in other countries with similar level of development (Wang & Wei, 2010). However, this is not consistent with the reality in terms of China’s APs export (see Fig. 3), thereby indirectly indicating that the overall technical level of the above cotton textile processing in China during 2002–2020 somewhat declined, or relatively declined compared to the other international APs exporters.

Fig. 4
figure 4

Source: The authors’ calculation is based on the UN COMTRADE and WDI database

The EXPY of China’s APs export ($) in four scenarios during 2002–2020. Notes Scenarios 1–4, respectively, refer to “top 50 exporters and HS01-24”, “178 exporters and HS01-24”, “178 exporters and HS01-24 + HS5101-5103 + HS5201-5203” and “178 exporters and HS01-24 + HS51-52”.

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3.5 Technical height of China’s APs export and its international comparison

We further analyse the relative change in the overall technical level of APs export across China and sample countries. These countries exhibit great differences in APs' technological development, which significantly translate into distinction in APs' export structure and overall technical level. One important question for our analysis is how much variation in overall technical level is accounted for by different levels of APs' technological development across countries. The \(ETHI\) was used to represent the global relative position of a country’s overall technical level. This index is a good proxy index to depict the upgradation of technology structure caused by a country's technological advancement.

Figure 5 shows how \(ETHI\) of China’s APs export varies from 2002 to 2020. This line chart of \(ETHI\) indicates a similar trend to that in \(EXPY\), i.e. the overall technical height of China’s APs export basket exhibits squiggly patterns—visible with a polynomial trend During 2002–2012, the \(ETHI\) gradually dropped from 0.582 to 0.371 (36.33% drop reported). This graph trend section is quite similar to the findings of Sun and Li (2016) based on 77 major exporting countries over the 1995–2012 period. The main reason for the drop in China’s technical height of APs exports was trade policy adjustment and a substantial increase in imports, resulting in a mammoth trade deficit since 2004, negatively impacting China’s agriculture. Later, the Belt and Road Initiative gradually improved the quality of economic growth in China (Kong et al., 2021) and enhanced China’s APs' technological development level since 2013. This pattern can be seen in the \(ETHI\) upward trend from 0.34 in 2014 to 0.393 in 2020, with an average growth rate of 2.84%. Overall, the technology structure of China’s APs export appeared a tendency towards a more downmarket trade pattern from 2002 to 2012 compared with other countries but has gradually shown some improvement since 2013. A more obvious message comes from the consideration of the \(ETHI\) of high technical complexity products as a measure of the evolutionary trend of export technology structure.

Fig. 5
figure 5

Source The authors’ calculation is based on the UN COMTRADE and WDI database

The dynamic change in the overall technical height index of China’s exported Aps. Note(s) The graphics were drawn according to the \(ETHI\).

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The comparison of China’s \(ETHI\) with that of the representative developed and developing countries is summarized in Table 4. The overall technical height index analysis shows a decline in the typical developed (0.06 drop) and developing (~ 0.08 drop) countries from 2002 to 2020. The average annual index value in developed countries was 0.58 compared to 0.426 in developing countries. By contrast, China’s annual average \(ETHI\) was lower than that of these sample countries in the developed/developing region, except for Brazil, Colombia, Ecuador, Malaysia, Morocco, and Peru (developing), which shows that the upgrading of the technology structure of China’s APs export was slower and requires the attention of the policymakers.

Table 4 The overall technical height index of exported APs of major sample countries
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4 Conclusions

We have conducted an empirical analysis of China’s APs export technology structure based on a sample of 178 countries/districts across the globe during 2002–2020. We have adopted the equalization technology classification method to divide all APs into five technological levels. We have found that China’s technology structure of APs export exhibits an overall characteristic of “big in the middle, small at both ends,” in which the medium-technology products predominate. The change in China’s APs export shows a “decrease in the middle, increase at both ends.” Compared to the other top ten APs exporters (except Brazil), the overall technology structure of China’s APs export is significantly lower.

Regarding the overall technical level of China’s APs export basket, although there is a slight fluctuation near the medium technical complexity grade, it shows a small decline. We conclude that the evolutionary trend of the technology structure of China’s APs export differed from that of its export scale during 2002–2020, i.e. the exporting APs in China were losing competitiveness overall. Compared with other countries, the technology structure of China’s APs export tended towards a more downmarket trade pattern from 2002 to 2012 but has gradually improved since 2013. We have also found that upgrading the technology structure of China’s APs export falls behind all the sample countries in the developed region but is faster than some sample countries (Brazil, Colombia, Ecuador, Malaysia, Morocco, and Peru) in the developing region.

Therefore, in China, where the APs export is experiencing the transition to medium–high and high technology fields, we suggest that the government should further raise standards for APs quality, concentrate on the export of higher technical complexity products and promote the advances and innovations in agricultural science and technology, to improve the international competitiveness of APs export. Although advantages in land-intensive and labour-intensive products in China’s agriculture still exist, the limited natural resources determine the critical role of value increment in the APs processing towards China’s APs export. In this respect, long-term policies to promote the domestic APs manufacturers to strengthen investment in talent and technology research would be advisable; for instance, intensifying cooperation in agriculture with developed countries such as New Zealand, Denmark, UK, France, Germany, Australia, Poland, Italy, Belgium, and the Netherlands would help transform China’s endangered resource-based competitive advantage into long-term economic saliency. In addition, we suggest that the government adapts to local conditions and further explores the trade potential hidden behind the differences in APs’ export technology structure.

However, one should also be aware of the diversity in the scope and classification of APs and the limitations of the export technical complexity index. Currently, the scope of APs mainly includes three standards of WTO, European Union and their extended versions (Wang et al., 2022). These are all defined based on the HS system or Standard International Trade Classification (SITC), which greatly impacts the research conclusion. Researchers must pay close attention to the research objectives and the industry classification of different countries. Moreover, the technical complexity index has been criticized for not considering the processing trade factor, the change in export structure and the implementation of the technology export restriction policy (Cao & Hanson-Rasmussen, 2018). Therefore, future research might explore this method's improvements and application studies, analyse the country/ district with various technical complexity indices or compare the results among different countries/ districts.