# Value Chain and Technology-Based Complexity Analysis of China’s Manufacturing Exports

Authors: Xiguang Zhang
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

In recent years, China’s manufacturing industry has developed rapidly, but numerous studies have shown that China is not a manufacturing powerhouse. In recent years, the division of labor in global value chains has been booming, and the Chinese manufacturing industry has been actively integrated into it with its labor cost advantage and achieved rapid development. There is an urgent need to transform and upgrade to high value-added links in the value chain. This paper measures the upstream degree of China’s manufacturing industry through the world input-output table and uses it as an indicator to measure the global value chain status, focusing on the heterogeneous impact of different types of productive services on the upgrading of China’s manufacturing industry’s global value chain status. This study examines the impact of global value chain boundary lessness and institutional quality on the technological complexity of China’s equipment manufacturing exports, using panel data of six types of Chinese equipment manufacturing industries from 2007 to 2018 as a sample. The findings show that intermediate manufactured goods from intermediate imports and intermediate products from developing economies make the strongest contribution to imported intermediate manufactured goods from developing economies. By industry, the impact areas of Graph Visualization (GV) embedding and institutional quality are different. GV embedding has a stronger contribution to the manufacturing of communication, computer, and other electronic equipment, while institutional quality has a stronger contribution to the manufacturing of special office machinery, and the interaction between the two is the same as that of institutional quality.


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## Body

## 1. Introduction

Under the global production network system, a country’s position in the global value chain is reflected in the level of technological sophistication of the products it produces and exports. In general, developed countries specialize in the more technologically complex segments of the production value chain, such as Research and Development (R&D) and design and the production of key components, while developing countries concentrate on the less technologically complex processes, such as the supply of raw materials, assembly and processing, and the production of simple components. According to the logic that the trade structure reflects the production structure, the export complexity of a country or region can reflect the position of that country or region in the global value chain. As an advanced production factor input with higher knowledge content, innovation capability, and technology intensity, more and more scholars agree that productive services can significantly improve a country’s Global Value Chains (GVC) position. This paper is concerned with what stage the global value position of China’s manufacturing industry is currently at and how productive services can influence the global value chain position of China’s manufacturing industry. These studies will be of great relevance to the accelerated climb of China’s manufacturing value chain position. Upstream Ness is the average distance that an industry’s products reach end-use, which determines the relative position of each specific industry in the value chain. Usually, the greater the upstream degree of industry, the farther its products are from final consumption and the closer it is to the upstream R&D stage of the global value chain. This paper measures the upstream degree of China’s manufacturing industry for two purposes: first, to use the upstream degree to calculate the global value chain position of each manufacturing sector in China and analyze its change over time; second, to use the upstream degree as an important indicator to measure the upgrading of China’s manufacturing industry, and to empirically analyze the impact of productive service industries on the global value chain position of China’s manufacturing industry, focusing on the difference of different productive service industries on the value chain of manufacturing industry The study focuses on the difference of the role of different productive service industries on the value chain of manufacturing industry, which is more relevant. With the rise of intra-industry division of labor, global value chains (GVCs) have become a global system of cross-firm networks.In recent years, China has actively integrated into GVCs with its abundant labor and resource endowments [1]. Undoubtedly, China’s participation in GVCs will lead to an increase in the scale of foreign trade and market share, and the degree of a country’s participation in GVCs reflects its degree of external integration [2]. With the further advancement of technology and trade liberalization, the global production network dominated by multinational corporations is rapidly developing and deepening. On the one hand, global production networks provide opportunities for developing countries to integrate into the global economy, achieve technological progress and eventually upgrade their value chain status. The successful transformation of the “Four Little Dragons” in Asia in history is strong proof that lagging countries and regions can gradually achieve technological upgrading and value chain upgrading by participating in global production networks. On the other hand, developing countries also face the risk of “consolidation” and “impoverishment growth” of their value chain status in the process of participating in global production networks. Compared with the developing countries in East Asia and Latin America, Pakistan, Bangladesh, Sri Lanka and other countries in South Asia rely too much on textile exports and remain at the lower end of the global value chain. The search popularity of Made in China in Google is shown in Figure 1, and it can be found that the program is growing year by year.Figure 1
Made in China search popularity in Google.As China’s economic development enters a new normal, the growth rate changes from high speed to medium speed, the development mode changes from scale and speed to intensive quality and efficiency, the structural adjustment changes from incremental capacity to deep stock incremental adjustment, and the development momentum changes from traditional growth to new growth. Therefore, clarifying the mechanism of the role of global value chain embedding and institutional quality on China’s exports in various industries will help the country and enterprises to improve the scale and quality of experts across the board [3]. Therefore, it is of great practical significance to explore in-depth the impact of global production networks on the technological upgrading and value chain enhancement of Chinese industries. From the existing literature, some scholars have studied the changes in China’s position in the global value chain since the reform and opening sup, e.g. found that China’s export complexity has remained at a stable and high level during 1992–2003. From the perspective of the technology level of export products, China’s export complexity relative to developed countries rose from 0.54 in 1992 to 0.73 in 2005, which indicates that China’s position in the global value chain is rising rapidly.At present, the technical complexity of exports, as one of the important indicators reflecting the technical structure of a country’s export trade, has become a key element in measuring the competitiveness of China’s exports, and the equipment manufacturing industry has accounted for 49.75% of the total value of China’s exports. From 2007 to 2018, there is no doubt about the importance of this industry for China’s exports [4]. In summary, this paper attempts to explore the impact of global value chain embedding and institutional quality on the technological complexity of China’s equipment manufacturing exports and, on this basis, propose corresponding policy recommendations to promote the optimization of China’s industrial structure [5]. However, at present, the research on the relationship between global production networks and value chain status is still limited to the level of theoretical analysis and lacks rigorous large-sample empirical tests. Although there is a lot of literature that classifies a country’s trade goods by the technological content of its products1 and indirectly reflects its position in the global production network through the export structure, this approach is inevitably limited by the broad classification criteria. Based on a sample of 40 developing countries, the impact of intra-product division of labor on developing countries’ value chain upgrading is empirically studied for the first time by measuring a country’s value chain position in terms of the similarity of its exports to selected developed countries. Unlike previous studies based on the overall country level, the value chain status of China’s manufacturing industries is measured by the export complexity index, and on this basis, the role played by global production networks on the value chain upgrading of China’s manufacturing industry and the possible industry heterogeneity of this role are explored, while group tests are conducted to further reveal the impact of participation in global production networks at different value chain levels on China’s manufacturing industry At the same time, the differences in the impact of participation in global production networks on the upgrading of China’s manufacturing value chain at different value chain levels are further revealed through group tests.Trade-in intermediate good is an important manifestation of the division of labor in global value chains. A large number of empirical studies have shown that a country’s trade in intermediate goods can not only be rapidly integrated into the global value chain division of labor system, but also improve the technology level of related industries and enterprises in the importing country through technology spillover [6]. Although GVC embedding is increasingly seen as an effective way to promote development, there is no automatic causal relationship between embedding and development [7], i.e., not all countries benefit from the process of embedding in GVCs [8], and a country's imports of intermediate goods and its import source countries are related to the long-term economic growth of the importing country [9]. [10] Pointed out that imported intermediate inputs do not equate to high-quality inputs and that many producers may choose to import intermediate inputs simply because they are cheaper and not because they have higher technological content and quality compared to domestic intermediates. [11] argued that imported intermediate services contain more advanced knowledge, information, and technology than imported intermediate manufacturers and that any exporting manufacturing firm is in a dual GVC embedding of product and functional architecture, thus it is necessary to distinguish between product embedding and service embedding in studying GVC embedding.Existing studies still only examine the heterogeneity of importing intermediate goods in one dimension. This paper takes the equipment manufacturing industry as the research object and distinguishes them into imported intermediate manufacturing goods from developed economies, imported intermediate service goods from developed economies, imported intermediate manufacturing goods from developing economies, and imported intermediate service goods from developing economies according to their sources, focusing on The heterogeneity effect of imported intermediate goods is examined, so as to provide policy guidance for the selection of imported intermediate goods and even trading partners for China’s equipment manufacturing industry [12].The research is organized as follows: The introduction is presented in Section1. Section 2 analyzes some related works that are being used in this research. Section 3 discusses the method and its types along with examples. Section 4, discusses the experimentation and evaluation. Finally, in Section 5, the research work is concluded.


## 2. Related Work

Under the framework of global value chain analysis, the growth path of the manufacturing value chain mainly includes the increase of manufacturing product added value and the improvement of production efficiency. The influence mechanism of developing the service industry and promoting the upgrading of the manufacturing value chain mainly contains the following three points: the separation of productive services from the manufacturing industry and the formation of the core competitiveness of manufacturing enterprises. As shown in Figure2, according to the value chain theory, the interconnection of basic and auxiliary activities in manufacturing enterprises forms the complete value chain of enterprises. However, with the increasing global competition, multinational enterprises need to carry out global industrial layout and allocation according to the principle of comparative advantage, and service outsourcing is one of the important links. The following two aspects can explain how outsourcing of productive services can contribute to the formation of the core competitiveness of manufacturing industries: transferring risks while reducing costs. A country’s manufacturing enterprises can outsource non-core business to countries with lower wage or material costs, and then invest the saved capital and resources in the core business sector of the enterprise to cultivate core competitiveness. Today’s evolving Internet technology and modular production reduce external coordination costs between companies, further reducing transaction costs in the process of outsourcing non-core businesses. At the same time, service outsourcing can transfer part of the risk to service enterprises, which helps enterprises develop smoothly. Bringing comparative advantage to high value-added links. The upgrading of the global value chain is mainly reflected in the transfer of comparative advantage in its product value chain. Through service outsourcing, a country’s manufacturing enterprises can focus on production activities that can generate high value-added and transfer low value-added activities to other countries, prompting the upgrading of a country’s international competitiveness and global value chain status. The social division of labor is conducive to the formation of economies of scale and the improvement of manufacturing production efficiency. The development of productive services makes the social division of labor finer, which makes economies of scale possible and leads to increasing returns to scale. Specialized services can enhance production efficiency through the following two mechanisms:Figure 2
Global industry pyramid.Generating economies of scale and enhancing production efficiency. According to Adam Smith’s theory of division of labor, productive services are knowledge-intensive, so they require more investment in the initial period, while their marginal costs are greatly reduced once know-how is developed. The cost of codable and standardized service activities will be continuously reduced with the expansion of scale, thus reducing the input cost of the manufacturing industry and improving production efficiency. Productive services and manufacturing industries are synergistically positioned for incremental returns to scale. Based on the input-output relationship between manufacturing and service industries, these two have a co-location effect in spatial distribution. The dense regional clustering of productive service industries and manufacturing industries will bring positive externalities and enhance production efficiency in three aspects: the proximity of service industries to manufacturing industries will lead to more informal contacts and labor mobility, and knowledge spillover will occur in the process of knowledge transfer; the regional clustering of service industries and manufacturing industries will make the local labor market specialized and intensive, reducing the risk of labor shortage The success of an industry depends on the development of related and supporting industries, and the productivity of enterprises can be greatly enhanced if intermediate inputs are shared in the region. As shown in Figure3, the process of upgrading manufacturing GVCs by production services can be summarized as a “two-step process”: one is to upgrade manufacturing processes, and the other is to shift to design and marketing. The influence process of the production service industry on the manufacturing global value chain can be briefly described as simple assembly - parts production - product development - brand marketing services. Specifically, it can be summarized as the following two steps: In the first step, starting from the production process of the manufacturing industry, productive services promote the improvement of production efficiency and the upgrading of the manufacturing process by providing production factors (such as knowledge, design, etc.) to the manufacturing industry. In addition, the human capital and intellectual capital embedded in productive services help enterprises master the core technology of product components, which helps them transform from entrusted processing to independent design and achieve the climb of global value chain status. In the second step, the productive service industry can participate in design and R&D activities, leading to the transformation of the manufacturing industry’s comparative advantage. At this point, each productive service plays a different role in the upgrading of the manufacturing global value chain.Figure 3
Global industry structure.The more developed transportation industry is conducive to reducing the storage and transportation costs in international trade, thus reducing the production costs and transaction costs of enterprises; the more convenient information and communication service industry enable enterprises to accurately obtain and constantly update real-time market information, thus reducing information asymmetry and transaction costs, and realizing the innovative construction of marketing channels through dynamic market information; at the same time, the more mature capital market and financial services At the same time, the more mature capital market and financial services enable enterprises to invest more capital in product development and design, so as to enhance the core technology control ability and realize the transformation and upgrading of China’s manufacturing value chain. In studying the embedding effect of GVC, scholars have measured it more in terms of the degree of embedding. [13] proposed Vertical Specialization Share (VSS) index to represent the share of import value in a country’s exports, which have become the most common indicator of GVC embedding. Both the industry and firm levels [14]. On this basis, some scholars have studied the heterogeneity of the value-added of imports from different sources, such as the heterogeneity of product embedding versus service embedding [15], and the heterogeneity of developed versus developing country embedding, among others.Regarding the calculation of the technical complexity of exports, with the increasing trade in intermediate goods, scholars have gradually realized that the traditional method of measuring the technical complexity of exports based on export value proposed by [16] ignores the effect of imported intermediate goods, thus making the measured technical complexity of Chinese exports higher than the actual level [17]. Therefore, [18] all attempted to remove the influence of imported intermediate goods from the production perspective, however, due to data limitations or methodological limitations, they could not completely remove the imported intermediate goods from exports, thus making the measurement results deviate from the actual situation [19].


## 3. Method

### 3.1. Variable Selection and Model Construction

This paper combines previous research and constructs a model as in (1) based on the analysis of the relationship between the explanatory variables and the explained variables:(1)EXPY1=α+βGVC1+γINST1+θGVC1∗INST1+ϕX1+ε1.EXPYt represents the technological complexity of China’s equipment manufacturing exports in timet. Its measurement is based on the method of Aston [1] and the specific formula is as follows:(2)PRODYkt=XktX¯t∑Xkt/XtYt.PRODYk denotes the technical complexity of exports ofk types of equipment manufacturing goods, Xkt and ∑Xkt denote the k types of equipment manufacturing goods and their total exports in China at time t, respectively; denotes the total exports of goods in China at time t, and denotes the GDP per capita in China at time t. The technical complexity of exports of k types of PRODY is weighted as follows the technical complexity of a country’s exports at time t can be obtained by weighting the k PRODY categories as follows:(3)EXPYt=∑Xkt∑XktPRODYk.Xkt/∑Xkt Denotes the share of k types of equipment manufacturing goods in China’s total equipment manufacturing exports in time t.The explanatory variables mainly include global value chain embeddedness and institutional quality. The former can be drawn from the study of [20], which defines the degree of global value chain embeddedness of category k equipment manufacturing goods as the export value of category k and intermediate goods worldwide in time t. The specific formula is as follows.(4)GVCtk=XtkXtw.Regarding the selection of indicators of institutional quality, specifically, they can be divided into three broad segments, namely the political, economic, and legal systems. The political system covers six indicators: quality of regulation, corruption control, and government integrity; the economic system includes six indicators of freedom: commercial, fiscal, monetary, trade, investment, and financial; and the legal system includes two indicators: the legal system and the property rights system. The arithmetic average of each sub-indicator is the quality score of each system in China each year; the arithmetic average of the three types of system quality score is the total system quality in China each year.The control variables are a combination of scholarly studies, and four variables are initially selected: capital-labor ratio (KL), land area per capita (LAND), R&D expenditure (RD), and infrastructure (INF). RD is expressed as the share of R&D expenditure to GDP [21].


#### 3.1.1. Data Sources

The technical complexity and GVC unboundedness of equipment manufacturing exports are obtained from the UN Comrade database, and the export value of finished goods and intermediate goods of each industry in the equipment manufacturing industry is filtered out by comparing the National Economic Classification with the corresponding codes in SITC Rev.3 and SITC Rev.3 with the corresponding codes in BEC, and then substituted into the above formula to calculate the technical complexity and GVC unboundedness of equipment manufacturing exports in each year. The political and legal indicators of institutional quality were obtained from the World Bank’s WGI database and The Heritage Foundation database; the data of economic indicators were all obtained from The Heritage Foundation database. The RD and INF data in the control variables are mainly from the Global Competitiveness Report of each year, while the rest of the variables are obtained from the World Bank WDI database, and all variables are taken as logarithms in the regression, as shown in Table1 [22].Table 1
Results of regression tests for fixed effects in the communications, instrumentation, and manufacturing industries.
Project6a6b6c7a7b7cGVC2.5841.2711.8050.752INST1.0560.1572.0251.467GVC∗INST0.5781.197RD0.467−0.3440.120.64INF0.4510.6970.370.212


#### 3.1.2. Model Setting and Indicator Calculation

In order to analyzes the impact of imported intermediate goods from different sources, this paper constructs the following econometric regression model on the basis of the relevant literature of [23]:(5)lnETit=αa+βalnvssit+γaXit+φaIi+εita,lnETit=αc+β1clnvss−edit+β2clnvss−ingit+γcXit+φcIi+εitc,lnETit=αv+β1vlnvss−mit+β2vlnvss−sit+γvXit+φvIi+εitv,lnETit=αo+β1olnvaedmit+β2olnvaedsit+β3o,lnvaingmit+β4olnvaingsit+γoXit+φoIi+εito,Where the subscript i denotes the economy, t denotes time, εit denotes the random error term, and the superscripts a, c, v and o denote the regression equations of the overall GVC embedding, the GVC embedding by the economy, the GVC embedding by industry and the GVC embedding by economy and industry, respectively. The main explanatory variables are the different decompositions of the degree of GVC embedding, where VSS denotes the overall GVC embedding calculated by vertical specialization rate, vss_ed and vss_ing denote the developed economy GVC embedding and the developing economy GVC embedding respectively, vss_m and vss_s denote vaedm, vaeds, vainingm and vaings denote the manufacturing GVC embedding in developed economies, the services GVC embedding in developed economies, the manufacturing GVC embedding in developing economies and the services GVC embedding in developing economies respectively. x denotes a set of control variables including R&D intensity (RD), capital-labor ratio (kl), market X denotes a set of control variables, including R&D intensity (RD), capital-labor ratio (kl), market level (market), technology spillover from foreign direct investment (FDI), etc., which represent individual control variables of the economy. This paper examines the impact of different types of imported intermediate goods on the technological complexity of China’s equipment manufacturing exports in terms of the overall GVC embedding, the GVC embedding of different economies and GVC embedding of different industries, and the GVC embedding of different economies and different industries through models (1)–(4).This paper presents a measure of the decomposition of domestic and foreign value-added of exports for the GVC embedding. The specific formula is as follows:(6)VTBE=VT1B11E1VT1B12E2⋯VT1B1cEc⋯VT1B1NENVT2B21E1VT2B22E2⋯VT2B2cEc⋯VT2B2NEN⋮⋮⋮⋮⋮⋮VTNBN1E1VTNBN2E2⋯VTNBNcEc⋯VTNBNNEN.The above equation represents the industry-level value-added matrix resulting from exports of the world economies. Where VT denotes the diagonal matrix consisting of the value-added rate by the industry for each economy, B is the classical Leontief inverse matrix andE is the diagonal matrix consisting of the value of exports by the industry for each economy. Let c represent China, then the cth block column matrix in VTBE shows the sources of value-added in China’s exports in each industry. Taking the WIOT published in 2013 as an example, specifically columns 13 to 15 of the c-block column matrix show the sources of value-added in China’s equipment manufacturing industry exports by sub-sector. Thus, the value-added from imports in the exports of a particular industry t in China’s equipment manufacturing industry can be calculated using (7), where i and k denote the economy and industry, respectively:(7)FVAct=∑i=1N∑ii≠cSvtikbicktect.The focus of this paper is to obtain the value-added of imports from different sources and types of China’s equipment manufacturing exports, so after deriving (7), the value-added of imports from China’s equipment manufacturing exports is decomposed to obtain (8):(8)FVAct=VAed+VAing=VAedm+VAeds+VAingm+VAings.In (8), the subscripts ed and ing denote developed and developing economies respectively, and the superscripts m and s denote intermediate manufactures and intermediate services respectively. The definition of developed and developing economies in this paper is based on the Human Development Index (HDI) developed by the United Nations Development Program (UNDP). The HDI is a quantitative indicator of the economic and social development of each UN member state, which not only represents its own economic development level, but also includes the life expectancy, education level, and quality of life of its inhabitants, and the data is updated annually, which is more authoritative and timelier. In this paper, countries with HDI > = 0.8 are defined as developed economies and the rest as developing economies. To simplify the calculation, it is assumed that an economy’s category has not changed during the sample period and that its category is the probability category during the sample period [24–26].This paper calculates the technological complexity of exports in each sub-sector of China’s equipment manufacturing industry using the method of [27] for measuring the technological content of exports, which is based on the idea that the domestic technological content of a country’s exports is equal to the difference between the technological content of the product output and the technological content contained in imported intermediate goods, and uses input-output analysis to calculate the technological content of a country’s exports at the sectoral level. The total output of a sector in a country or region is decomposed in terms of production processes and can be expressed in the form of a matrix as:(9)V′=VT#TSI′I−A−1.In equation (9), V is the vector of all technology content at the sectoral level of each economy; (I − A)−1 is the Lyon (I − A)−1 is the inverse Lyon matrix, often denoted by B when using input-output tables for calculations.(9) calculates the technological content of output at the sectoral level in a country or region, but in a global division of production system, the output of a country or region includes not only the value-added created by its own or regional factors of production but also the value-added from intermediate goods used in other economies. For this reason, when calculating the technical complexity of exports at the sectoral level in a country or region, it is also necessary to exclude the technical complexity of imported intermediate goods [28].


## 3.1. Variable Selection and Model Construction

This paper combines previous research and constructs a model as in (1) based on the analysis of the relationship between the explanatory variables and the explained variables:(1)EXPY1=α+βGVC1+γINST1+θGVC1∗INST1+ϕX1+ε1.EXPYt represents the technological complexity of China’s equipment manufacturing exports in timet. Its measurement is based on the method of Aston [1] and the specific formula is as follows:(2)PRODYkt=XktX¯t∑Xkt/XtYt.PRODYk denotes the technical complexity of exports ofk types of equipment manufacturing goods, Xkt and ∑Xkt denote the k types of equipment manufacturing goods and their total exports in China at time t, respectively; denotes the total exports of goods in China at time t, and denotes the GDP per capita in China at time t. The technical complexity of exports of k types of PRODY is weighted as follows the technical complexity of a country’s exports at time t can be obtained by weighting the k PRODY categories as follows:(3)EXPYt=∑Xkt∑XktPRODYk.Xkt/∑Xkt Denotes the share of k types of equipment manufacturing goods in China’s total equipment manufacturing exports in time t.The explanatory variables mainly include global value chain embeddedness and institutional quality. The former can be drawn from the study of [20], which defines the degree of global value chain embeddedness of category k equipment manufacturing goods as the export value of category k and intermediate goods worldwide in time t. The specific formula is as follows.(4)GVCtk=XtkXtw.Regarding the selection of indicators of institutional quality, specifically, they can be divided into three broad segments, namely the political, economic, and legal systems. The political system covers six indicators: quality of regulation, corruption control, and government integrity; the economic system includes six indicators of freedom: commercial, fiscal, monetary, trade, investment, and financial; and the legal system includes two indicators: the legal system and the property rights system. The arithmetic average of each sub-indicator is the quality score of each system in China each year; the arithmetic average of the three types of system quality score is the total system quality in China each year.The control variables are a combination of scholarly studies, and four variables are initially selected: capital-labor ratio (KL), land area per capita (LAND), R&D expenditure (RD), and infrastructure (INF). RD is expressed as the share of R&D expenditure to GDP [21].


### 3.1.1. Data Sources

The technical complexity and GVC unboundedness of equipment manufacturing exports are obtained from the UN Comrade database, and the export value of finished goods and intermediate goods of each industry in the equipment manufacturing industry is filtered out by comparing the National Economic Classification with the corresponding codes in SITC Rev.3 and SITC Rev.3 with the corresponding codes in BEC, and then substituted into the above formula to calculate the technical complexity and GVC unboundedness of equipment manufacturing exports in each year. The political and legal indicators of institutional quality were obtained from the World Bank’s WGI database and The Heritage Foundation database; the data of economic indicators were all obtained from The Heritage Foundation database. The RD and INF data in the control variables are mainly from the Global Competitiveness Report of each year, while the rest of the variables are obtained from the World Bank WDI database, and all variables are taken as logarithms in the regression, as shown in Table1 [22].Table 1
Results of regression tests for fixed effects in the communications, instrumentation, and manufacturing industries.
Project6a6b6c7a7b7cGVC2.5841.2711.8050.752INST1.0560.1572.0251.467GVC∗INST0.5781.197RD0.467−0.3440.120.64INF0.4510.6970.370.212


### 3.1.2. Model Setting and Indicator Calculation

In order to analyzes the impact of imported intermediate goods from different sources, this paper constructs the following econometric regression model on the basis of the relevant literature of [23]:(5)lnETit=αa+βalnvssit+γaXit+φaIi+εita,lnETit=αc+β1clnvss−edit+β2clnvss−ingit+γcXit+φcIi+εitc,lnETit=αv+β1vlnvss−mit+β2vlnvss−sit+γvXit+φvIi+εitv,lnETit=αo+β1olnvaedmit+β2olnvaedsit+β3o,lnvaingmit+β4olnvaingsit+γoXit+φoIi+εito,Where the subscript i denotes the economy, t denotes time, εit denotes the random error term, and the superscripts a, c, v and o denote the regression equations of the overall GVC embedding, the GVC embedding by the economy, the GVC embedding by industry and the GVC embedding by economy and industry, respectively. The main explanatory variables are the different decompositions of the degree of GVC embedding, where VSS denotes the overall GVC embedding calculated by vertical specialization rate, vss_ed and vss_ing denote the developed economy GVC embedding and the developing economy GVC embedding respectively, vss_m and vss_s denote vaedm, vaeds, vainingm and vaings denote the manufacturing GVC embedding in developed economies, the services GVC embedding in developed economies, the manufacturing GVC embedding in developing economies and the services GVC embedding in developing economies respectively. x denotes a set of control variables including R&D intensity (RD), capital-labor ratio (kl), market X denotes a set of control variables, including R&D intensity (RD), capital-labor ratio (kl), market level (market), technology spillover from foreign direct investment (FDI), etc., which represent individual control variables of the economy. This paper examines the impact of different types of imported intermediate goods on the technological complexity of China’s equipment manufacturing exports in terms of the overall GVC embedding, the GVC embedding of different economies and GVC embedding of different industries, and the GVC embedding of different economies and different industries through models (1)–(4).This paper presents a measure of the decomposition of domestic and foreign value-added of exports for the GVC embedding. The specific formula is as follows:(6)VTBE=VT1B11E1VT1B12E2⋯VT1B1cEc⋯VT1B1NENVT2B21E1VT2B22E2⋯VT2B2cEc⋯VT2B2NEN⋮⋮⋮⋮⋮⋮VTNBN1E1VTNBN2E2⋯VTNBNcEc⋯VTNBNNEN.The above equation represents the industry-level value-added matrix resulting from exports of the world economies. Where VT denotes the diagonal matrix consisting of the value-added rate by the industry for each economy, B is the classical Leontief inverse matrix andE is the diagonal matrix consisting of the value of exports by the industry for each economy. Let c represent China, then the cth block column matrix in VTBE shows the sources of value-added in China’s exports in each industry. Taking the WIOT published in 2013 as an example, specifically columns 13 to 15 of the c-block column matrix show the sources of value-added in China’s equipment manufacturing industry exports by sub-sector. Thus, the value-added from imports in the exports of a particular industry t in China’s equipment manufacturing industry can be calculated using (7), where i and k denote the economy and industry, respectively:(7)FVAct=∑i=1N∑ii≠cSvtikbicktect.The focus of this paper is to obtain the value-added of imports from different sources and types of China’s equipment manufacturing exports, so after deriving (7), the value-added of imports from China’s equipment manufacturing exports is decomposed to obtain (8):(8)FVAct=VAed+VAing=VAedm+VAeds+VAingm+VAings.In (8), the subscripts ed and ing denote developed and developing economies respectively, and the superscripts m and s denote intermediate manufactures and intermediate services respectively. The definition of developed and developing economies in this paper is based on the Human Development Index (HDI) developed by the United Nations Development Program (UNDP). The HDI is a quantitative indicator of the economic and social development of each UN member state, which not only represents its own economic development level, but also includes the life expectancy, education level, and quality of life of its inhabitants, and the data is updated annually, which is more authoritative and timelier. In this paper, countries with HDI > = 0.8 are defined as developed economies and the rest as developing economies. To simplify the calculation, it is assumed that an economy’s category has not changed during the sample period and that its category is the probability category during the sample period [24–26].This paper calculates the technological complexity of exports in each sub-sector of China’s equipment manufacturing industry using the method of [27] for measuring the technological content of exports, which is based on the idea that the domestic technological content of a country’s exports is equal to the difference between the technological content of the product output and the technological content contained in imported intermediate goods, and uses input-output analysis to calculate the technological content of a country’s exports at the sectoral level. The total output of a sector in a country or region is decomposed in terms of production processes and can be expressed in the form of a matrix as:(9)V′=VT#TSI′I−A−1.In equation (9), V is the vector of all technology content at the sectoral level of each economy; (I − A)−1 is the Lyon (I − A)−1 is the inverse Lyon matrix, often denoted by B when using input-output tables for calculations.(9) calculates the technological content of output at the sectoral level in a country or region, but in a global division of production system, the output of a country or region includes not only the value-added created by its own or regional factors of production but also the value-added from intermediate goods used in other economies. For this reason, when calculating the technical complexity of exports at the sectoral level in a country or region, it is also necessary to exclude the technical complexity of imported intermediate goods [28].


## 3.1.1. Data Sources

The technical complexity and GVC unboundedness of equipment manufacturing exports are obtained from the UN Comrade database, and the export value of finished goods and intermediate goods of each industry in the equipment manufacturing industry is filtered out by comparing the National Economic Classification with the corresponding codes in SITC Rev.3 and SITC Rev.3 with the corresponding codes in BEC, and then substituted into the above formula to calculate the technical complexity and GVC unboundedness of equipment manufacturing exports in each year. The political and legal indicators of institutional quality were obtained from the World Bank’s WGI database and The Heritage Foundation database; the data of economic indicators were all obtained from The Heritage Foundation database. The RD and INF data in the control variables are mainly from the Global Competitiveness Report of each year, while the rest of the variables are obtained from the World Bank WDI database, and all variables are taken as logarithms in the regression, as shown in Table1 [22].Table 1
Results of regression tests for fixed effects in the communications, instrumentation, and manufacturing industries.
Project6a6b6c7a7b7cGVC2.5841.2711.8050.752INST1.0560.1572.0251.467GVC∗INST0.5781.197RD0.467−0.3440.120.64INF0.4510.6970.370.212


## 3.1.2. Model Setting and Indicator Calculation

In order to analyzes the impact of imported intermediate goods from different sources, this paper constructs the following econometric regression model on the basis of the relevant literature of [23]:(5)lnETit=αa+βalnvssit+γaXit+φaIi+εita,lnETit=αc+β1clnvss−edit+β2clnvss−ingit+γcXit+φcIi+εitc,lnETit=αv+β1vlnvss−mit+β2vlnvss−sit+γvXit+φvIi+εitv,lnETit=αo+β1olnvaedmit+β2olnvaedsit+β3o,lnvaingmit+β4olnvaingsit+γoXit+φoIi+εito,Where the subscript i denotes the economy, t denotes time, εit denotes the random error term, and the superscripts a, c, v and o denote the regression equations of the overall GVC embedding, the GVC embedding by the economy, the GVC embedding by industry and the GVC embedding by economy and industry, respectively. The main explanatory variables are the different decompositions of the degree of GVC embedding, where VSS denotes the overall GVC embedding calculated by vertical specialization rate, vss_ed and vss_ing denote the developed economy GVC embedding and the developing economy GVC embedding respectively, vss_m and vss_s denote vaedm, vaeds, vainingm and vaings denote the manufacturing GVC embedding in developed economies, the services GVC embedding in developed economies, the manufacturing GVC embedding in developing economies and the services GVC embedding in developing economies respectively. x denotes a set of control variables including R&D intensity (RD), capital-labor ratio (kl), market X denotes a set of control variables, including R&D intensity (RD), capital-labor ratio (kl), market level (market), technology spillover from foreign direct investment (FDI), etc., which represent individual control variables of the economy. This paper examines the impact of different types of imported intermediate goods on the technological complexity of China’s equipment manufacturing exports in terms of the overall GVC embedding, the GVC embedding of different economies and GVC embedding of different industries, and the GVC embedding of different economies and different industries through models (1)–(4).This paper presents a measure of the decomposition of domestic and foreign value-added of exports for the GVC embedding. The specific formula is as follows:(6)VTBE=VT1B11E1VT1B12E2⋯VT1B1cEc⋯VT1B1NENVT2B21E1VT2B22E2⋯VT2B2cEc⋯VT2B2NEN⋮⋮⋮⋮⋮⋮VTNBN1E1VTNBN2E2⋯VTNBNcEc⋯VTNBNNEN.The above equation represents the industry-level value-added matrix resulting from exports of the world economies. Where VT denotes the diagonal matrix consisting of the value-added rate by the industry for each economy, B is the classical Leontief inverse matrix andE is the diagonal matrix consisting of the value of exports by the industry for each economy. Let c represent China, then the cth block column matrix in VTBE shows the sources of value-added in China’s exports in each industry. Taking the WIOT published in 2013 as an example, specifically columns 13 to 15 of the c-block column matrix show the sources of value-added in China’s equipment manufacturing industry exports by sub-sector. Thus, the value-added from imports in the exports of a particular industry t in China’s equipment manufacturing industry can be calculated using (7), where i and k denote the economy and industry, respectively:(7)FVAct=∑i=1N∑ii≠cSvtikbicktect.The focus of this paper is to obtain the value-added of imports from different sources and types of China’s equipment manufacturing exports, so after deriving (7), the value-added of imports from China’s equipment manufacturing exports is decomposed to obtain (8):(8)FVAct=VAed+VAing=VAedm+VAeds+VAingm+VAings.In (8), the subscripts ed and ing denote developed and developing economies respectively, and the superscripts m and s denote intermediate manufactures and intermediate services respectively. The definition of developed and developing economies in this paper is based on the Human Development Index (HDI) developed by the United Nations Development Program (UNDP). The HDI is a quantitative indicator of the economic and social development of each UN member state, which not only represents its own economic development level, but also includes the life expectancy, education level, and quality of life of its inhabitants, and the data is updated annually, which is more authoritative and timelier. In this paper, countries with HDI > = 0.8 are defined as developed economies and the rest as developing economies. To simplify the calculation, it is assumed that an economy’s category has not changed during the sample period and that its category is the probability category during the sample period [24–26].This paper calculates the technological complexity of exports in each sub-sector of China’s equipment manufacturing industry using the method of [27] for measuring the technological content of exports, which is based on the idea that the domestic technological content of a country’s exports is equal to the difference between the technological content of the product output and the technological content contained in imported intermediate goods, and uses input-output analysis to calculate the technological content of a country’s exports at the sectoral level. The total output of a sector in a country or region is decomposed in terms of production processes and can be expressed in the form of a matrix as:(9)V′=VT#TSI′I−A−1.In equation (9), V is the vector of all technology content at the sectoral level of each economy; (I − A)−1 is the Lyon (I − A)−1 is the inverse Lyon matrix, often denoted by B when using input-output tables for calculations.(9) calculates the technological content of output at the sectoral level in a country or region, but in a global division of production system, the output of a country or region includes not only the value-added created by its own or regional factors of production but also the value-added from intermediate goods used in other economies. For this reason, when calculating the technical complexity of exports at the sectoral level in a country or region, it is also necessary to exclude the technical complexity of imported intermediate goods [28].


## 4. Experimentation and Evaluation

The goal of experimental evaluation is to see if a program or intervention is more effective than the current method. It entails allocating individuals to a treatment or control group at random. Fully experimental designs are uncommon in rural community health program assessment research, but they may be possible.


### 4.1. Analysis of Baseline Regression Results

The regression coefficients of the variables in column (4) of Table2 shows that this result is consistent with the findings in the first three columns. Secondly, this result is theoretically reasonable, as intermediate manufactures from developed economies generally represent a higher level of technology and are easier to be learned and reinforced by importing enterprises than intermediate services, so imported intermediate manufactures from developed economies have a facilitating effect on the technological content of China’s equipment manufacturing exports; compared to intermediate manufactures from developed economies Compared with intermediate manufacturing goods from developed economies, intermediate manufacturing goods imported from developing economies tend to reduce the production costs of domestic enterprises through the advantage of low costs, thus concentrating more production resources on high technology production, and thus promoting the improvement of the technology level of domestic enterprises, thus intermediate manufacturing goods from developing economies have a stronger role in promoting the technological sophistication of China’s equipment manufacturing exports; Both imports of intermediate services from developed economies and imports of intermediate services from developing economies inhibit the increase in the technological sophistication of China’s equipment manufacturing exports, which confirms the reliability of the results in column (3).Table 2
Baseline regression results.
Variable1234InETInETInETInETInvss0.160Invss_ed−0.014Invss_ing0.110Invss_m0.888Invss_s−0.706Invaedm0.519Invaeds−0.687Invaingm0.628Invaings−0.341Inrd−0.500−0.507−0.307−0.266Inkd−0.083−0.1320.1060.110Infdi−0.041−0.0360.1520.259Inmarket−0.682−0.687−0.494−0.392Constant0.5750.6790.9091.342Economy fixed effectYesYesYesYesObs780780780780R20.9870.9790.9800.984


### 4.2. Further Empirical Analysis

In the baseline regression, the robustness of the question is tested using unbalanced panel data using the available data. The results of the robustness tests are shown in columns (1)–(4) of Table3, from which it can be seen that the sign of the coefficients of the main explanatory variables remain constant and the regression results remain robust, given the maximum access to the sample.Table 3
Robustness tests for replacing sample data types and considering changes in the dynamics of economic development.
Variable12345678InETInETInETInETInETInETInETInETInvss0.1550.160Invss_ed−0.0140.009Invss_ing0.1070.098Invss_m0.9100.888Invss_s−0.734−0.706Invaedm0.5510.480Invaeds−0.727−0.579Invaingm0.6340.604Invaings−0.346−0.365Constant0.5750.6790.9091.3431.359Economy fixed effectYesYesYesYesYesYesYesYesObs825825825825780780780780R20.9780.9790.9810.9840.9780.9790.980.985In the baseline regressions, the level of development is defined according to the broad probability category to which the economy belongs in order to simplify the calculations.Given the potential problems of between-group heteroskedasticity, within-group autocorrelation and between-group contemporaneous correlation, the panel is re-estimated using a more comprehensive feasible generalized least squares (FGLS) method that corrects for all three problems. Table4 shows the results of the regression of the original data using the FGLS method, from which it can be seen that the regression results are still robust.Table 4
Robustness tests for changing estimation methods.
Variable1234InETInETInETInETInvss0.085Invss_ed−0.065Invss_ing0.067Invss_m1.297Invss_s−1.245Invaedm0.215Invaeds−0.387Invaingm0.939Invaings−0.594Constant0.5590.6621.0931.808Economy fixed effectYesYesYesYesObs780780780780Considering the possible endogenous issues due to bi-directional causality and to further demonstrate the reliability of the above results, this paper introduces one period lags of the main explanatory variables as its instrumental variables and performs the regression again using two-stage least squares (2SLS) [24]. For simplicity, only the results of the second stage regressions are reported in Table 5. As can be seen from the regression results, the signs of the main explanatory variables in the regression results are still consistent with the baseline regression results after removing the two-way causality.Table 5
Estimation results of the 2SLS model.
Variable1234InETInETInETInETInvss0.248Invss_ed0.061Invss_ing0.112Invss_m0.857Invss_s−0.588Invaedm1.244Invaeds−1.471Invaingm0.476Invaings−0.175Constant0.5590.6910.9691.162Economy fixed effectYesYesYesYesObs700700700700R20.9770.9790.980.982It is worth pointing out that the only difference in the above regressions is that in model (2) the sign of the coefficient on imports of intermediate goods from developed economies (vss_ ed) is prone to change and is often insignificant. The paper suggests that this is because imports of intermediate goods from developed economies tend to have different effects at the same time. Theoretically, the representative technology spillover and “low-end lock-in” phenomena are the most pronounced and have opposite effects, and the combined effect of the different effects is difficult to determine, so their impact needs to be further tested.The above study shows that GVC embedding significantly contributes to the increase in technological sophistication of China’s equipment manufacturing exports. The question that needs to be further consideration is how does GVC embedding contribute to the increase in export technological sophistication? To this end, this paper uses a mediating effects model to examine the possible channels of influence. Based on the theoretical analysis presented in the previous paper, and taking into account the limitations of data availability, the technology spillover effect is selected as the mediating variable to be tested in this paper.Firstly, the total factor productivity of the equipment manufacturing industry in different economies from 1995 to 2014 was calculated using data from the socio-economic accounts in the WIOD according to the method of calculating industry-level total factor productivity proposed by [25]. The specific calculation formula is:(10)lnTFPit=lnYit−1N∑ilnYit−12σit+1N∑iσitlnLit−1N∑ilnLit−1−12σit+1N∑iσitlnKit−1N∑ilnKit.In (10), Y denotes industry value-added, σ denotes labor compensation share, K denotes capital stock, L denotes labor force, N denotes the number of economies, and the subscripts i and t denote economies and years, respectively. After calculating the logarithm of total factor productivity in China and other economies, this paper uses the logarithmic difference between the productivity of other economies and that of China to represent the technology spillover, i.e.:(11)Inspillt=InTFPxt−InTFPct=InTFPxtTFPct,Where lnTFPct denotes the logarithm of the productivity of the equipment manufacturing industry in year t in economies other than China and lnTFPct denotes the logarithm of the productivity of the equipment manufacturing industry in year t in China. The above equation shows that technology spillover is more likely to occur when the productivity gap between China and the source of imports of intermediate goods is larger. Using technology spillover as an intermediate variable, the results of the mediating effect test on how the GVC embedding affects the technological sophistication of China’s equipment manufacturing exports using the feasible generalized least squares (FGLS) method are shown in Table 6.Table 6
Results of tests on the mediating channel of technology spillover effects.
Variable123InETInETInETInvss0.009−1.2930.013Inspill0.003Inrd−0.4280.01−0.423Inkd−0.071−1.604−0.058Infdi0.0390.3930.053Inmarket−0.752−0.475−0.742Constant0.5681.5650.583Economy fixed effectYesYesYesObs740740740Among them, column (1) shows that GVC embedding significantly contributes to the technological complexity of China’s equipment manufacturing exports, while column (2) indicates that GVC embedding has a significant inhibitory effect on the technological spillover effect in this paper. Since the technology spillover effect in this paper is expressed as the log difference between the total factor productivity of other economies and that of China, this suggests that the GVC embedding helps to narrow the productivity gap between China and other economies in the equipment manufacturing industry, i.e., the GVC embedding significantly increases the total factor productivity of China’s equipment manufacturing industry. The regression results in column (3) are obtained by introducing the technology spillover effect into the regression equation on the basis of column (1). On the one hand, it shows that the GVC embedding can directly promote the technological complexity of China’s equipment manufacturing exports, on the other hand, as the coefficient of lnsi is significantly positive, it indicates that the larger the gap between China and other economies’ total factor productivity in equipment manufacturing, the more likely the technology spillover effect On the other hand, since the coefficient of lenis is significantly positive, it indicates that the greater the gap between China and other economies in terms of total factor productivity in equipment manufacturing, the more likely it is to generate technology spillover effects, and thus the more conducive to the increase in technological sophistication of China’s equipment manufacturing exports. Thus, the existence of the technology spillover effect as a mediating channel is corroborated.


## 4.1. Analysis of Baseline Regression Results

The regression coefficients of the variables in column (4) of Table2 shows that this result is consistent with the findings in the first three columns. Secondly, this result is theoretically reasonable, as intermediate manufactures from developed economies generally represent a higher level of technology and are easier to be learned and reinforced by importing enterprises than intermediate services, so imported intermediate manufactures from developed economies have a facilitating effect on the technological content of China’s equipment manufacturing exports; compared to intermediate manufactures from developed economies Compared with intermediate manufacturing goods from developed economies, intermediate manufacturing goods imported from developing economies tend to reduce the production costs of domestic enterprises through the advantage of low costs, thus concentrating more production resources on high technology production, and thus promoting the improvement of the technology level of domestic enterprises, thus intermediate manufacturing goods from developing economies have a stronger role in promoting the technological sophistication of China’s equipment manufacturing exports; Both imports of intermediate services from developed economies and imports of intermediate services from developing economies inhibit the increase in the technological sophistication of China’s equipment manufacturing exports, which confirms the reliability of the results in column (3).Table 2
Baseline regression results.
Variable1234InETInETInETInETInvss0.160Invss_ed−0.014Invss_ing0.110Invss_m0.888Invss_s−0.706Invaedm0.519Invaeds−0.687Invaingm0.628Invaings−0.341Inrd−0.500−0.507−0.307−0.266Inkd−0.083−0.1320.1060.110Infdi−0.041−0.0360.1520.259Inmarket−0.682−0.687−0.494−0.392Constant0.5750.6790.9091.342Economy fixed effectYesYesYesYesObs780780780780R20.9870.9790.9800.984


## 4.2. Further Empirical Analysis

In the baseline regression, the robustness of the question is tested using unbalanced panel data using the available data. The results of the robustness tests are shown in columns (1)–(4) of Table3, from which it can be seen that the sign of the coefficients of the main explanatory variables remain constant and the regression results remain robust, given the maximum access to the sample.Table 3
Robustness tests for replacing sample data types and considering changes in the dynamics of economic development.
Variable12345678InETInETInETInETInETInETInETInETInvss0.1550.160Invss_ed−0.0140.009Invss_ing0.1070.098Invss_m0.9100.888Invss_s−0.734−0.706Invaedm0.5510.480Invaeds−0.727−0.579Invaingm0.6340.604Invaings−0.346−0.365Constant0.5750.6790.9091.3431.359Economy fixed effectYesYesYesYesYesYesYesYesObs825825825825780780780780R20.9780.9790.9810.9840.9780.9790.980.985In the baseline regressions, the level of development is defined according to the broad probability category to which the economy belongs in order to simplify the calculations.Given the potential problems of between-group heteroskedasticity, within-group autocorrelation and between-group contemporaneous correlation, the panel is re-estimated using a more comprehensive feasible generalized least squares (FGLS) method that corrects for all three problems. Table4 shows the results of the regression of the original data using the FGLS method, from which it can be seen that the regression results are still robust.Table 4
Robustness tests for changing estimation methods.
Variable1234InETInETInETInETInvss0.085Invss_ed−0.065Invss_ing0.067Invss_m1.297Invss_s−1.245Invaedm0.215Invaeds−0.387Invaingm0.939Invaings−0.594Constant0.5590.6621.0931.808Economy fixed effectYesYesYesYesObs780780780780Considering the possible endogenous issues due to bi-directional causality and to further demonstrate the reliability of the above results, this paper introduces one period lags of the main explanatory variables as its instrumental variables and performs the regression again using two-stage least squares (2SLS) [24]. For simplicity, only the results of the second stage regressions are reported in Table 5. As can be seen from the regression results, the signs of the main explanatory variables in the regression results are still consistent with the baseline regression results after removing the two-way causality.Table 5
Estimation results of the 2SLS model.
Variable1234InETInETInETInETInvss0.248Invss_ed0.061Invss_ing0.112Invss_m0.857Invss_s−0.588Invaedm1.244Invaeds−1.471Invaingm0.476Invaings−0.175Constant0.5590.6910.9691.162Economy fixed effectYesYesYesYesObs700700700700R20.9770.9790.980.982It is worth pointing out that the only difference in the above regressions is that in model (2) the sign of the coefficient on imports of intermediate goods from developed economies (vss_ ed) is prone to change and is often insignificant. The paper suggests that this is because imports of intermediate goods from developed economies tend to have different effects at the same time. Theoretically, the representative technology spillover and “low-end lock-in” phenomena are the most pronounced and have opposite effects, and the combined effect of the different effects is difficult to determine, so their impact needs to be further tested.The above study shows that GVC embedding significantly contributes to the increase in technological sophistication of China’s equipment manufacturing exports. The question that needs to be further consideration is how does GVC embedding contribute to the increase in export technological sophistication? To this end, this paper uses a mediating effects model to examine the possible channels of influence. Based on the theoretical analysis presented in the previous paper, and taking into account the limitations of data availability, the technology spillover effect is selected as the mediating variable to be tested in this paper.Firstly, the total factor productivity of the equipment manufacturing industry in different economies from 1995 to 2014 was calculated using data from the socio-economic accounts in the WIOD according to the method of calculating industry-level total factor productivity proposed by [25]. The specific calculation formula is:(10)lnTFPit=lnYit−1N∑ilnYit−12σit+1N∑iσitlnLit−1N∑ilnLit−1−12σit+1N∑iσitlnKit−1N∑ilnKit.In (10), Y denotes industry value-added, σ denotes labor compensation share, K denotes capital stock, L denotes labor force, N denotes the number of economies, and the subscripts i and t denote economies and years, respectively. After calculating the logarithm of total factor productivity in China and other economies, this paper uses the logarithmic difference between the productivity of other economies and that of China to represent the technology spillover, i.e.:(11)Inspillt=InTFPxt−InTFPct=InTFPxtTFPct,Where lnTFPct denotes the logarithm of the productivity of the equipment manufacturing industry in year t in economies other than China and lnTFPct denotes the logarithm of the productivity of the equipment manufacturing industry in year t in China. The above equation shows that technology spillover is more likely to occur when the productivity gap between China and the source of imports of intermediate goods is larger. Using technology spillover as an intermediate variable, the results of the mediating effect test on how the GVC embedding affects the technological sophistication of China’s equipment manufacturing exports using the feasible generalized least squares (FGLS) method are shown in Table 6.Table 6
Results of tests on the mediating channel of technology spillover effects.
Variable123InETInETInETInvss0.009−1.2930.013Inspill0.003Inrd−0.4280.01−0.423Inkd−0.071−1.604−0.058Infdi0.0390.3930.053Inmarket−0.752−0.475−0.742Constant0.5681.5650.583Economy fixed effectYesYesYesObs740740740Among them, column (1) shows that GVC embedding significantly contributes to the technological complexity of China’s equipment manufacturing exports, while column (2) indicates that GVC embedding has a significant inhibitory effect on the technological spillover effect in this paper. Since the technology spillover effect in this paper is expressed as the log difference between the total factor productivity of other economies and that of China, this suggests that the GVC embedding helps to narrow the productivity gap between China and other economies in the equipment manufacturing industry, i.e., the GVC embedding significantly increases the total factor productivity of China’s equipment manufacturing industry. The regression results in column (3) are obtained by introducing the technology spillover effect into the regression equation on the basis of column (1). On the one hand, it shows that the GVC embedding can directly promote the technological complexity of China’s equipment manufacturing exports, on the other hand, as the coefficient of lnsi is significantly positive, it indicates that the larger the gap between China and other economies’ total factor productivity in equipment manufacturing, the more likely the technology spillover effect On the other hand, since the coefficient of lenis is significantly positive, it indicates that the greater the gap between China and other economies in terms of total factor productivity in equipment manufacturing, the more likely it is to generate technology spillover effects, and thus the more conducive to the increase in technological sophistication of China’s equipment manufacturing exports. Thus, the existence of the technology spillover effect as a mediating channel is corroborated.


## 5. Conclusion

Overall, the results of the fixed-effects regression test suggest that both deeper embedding in GVCs and better institutional quality can significantly increase the technological sophistication of China’s exports, a finding that Zis is also consistent with expectations. This suggests that China’s participation in GVCs and the improvement of institutional quality can not only increase the size of China’s market on the surface but also improve the quality of its exports on the inside.Specifically, the two areas of action are similar but different. The results show that the increase in GVC unboundedness has the strongest positive impact on the electrical manufacturing sector, while it is also more effective in the general and communications manufacturing sectors; institutional quality mainly affects the specialized equipment manufacturing sector, while it also significantly increases the technical complexity of exports in the instrumentation sector. The interaction between the two is consistent with the area of institutional quality promotion, suggesting that the improvement of China’s export quality is inseparable from the improvement of China’s institutional quality, in addition to the active integration into the wave of globalization.



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*Source: 8116336-2022-03-31.xml*