The Influence of GVC Participation and Division of Labor Status on the Comparative Advantage of China’s Wood-Based Panel Industry

Abstract

This article innovatively proposes a new display comparative advantage index (NB*). Based on the characteristics of existing comparative advantage indices and practical research needs, the NB* index can more accurately measure whether a country’s products have comparative advantages among other countries within the selected region, which to some extent expands the research boundary of industrial international competitiveness evaluation indicators. Meanwhile, this article takes China’s wood-based panel industry as an example to calculate the comparative advantage level of China’s wood-based panel industry among Regional Comprehensive Economic Partnership (RCEP) member countries and further explores the impact of global value chain participation and division of labor status on the international competitiveness of the industry. Research has found that, firstly, although China’s wood-based panel industry has a relatively lower level of comparative advantage among RCEP member countries, it still has a certain level of competitiveness in some developed and developing countries’ markets; Secondly, in terms of the wood-based panel industry, a higher level of global value chain participation can effectively improve the international comparative advantage level of the domestic wood-based panel industry. Thirdly, the level of division of labor in the industrial chain has a significant positive impact on the comparative advantage level of China’s wood-based panel industry in RCEP member countries. Therefore, in order to enhance the international competitiveness of the domestic wood-based panel industry, it is necessary to actively participate in the global value chain and continuously move towards a higher level of division of labor in the industry chain.

1. Introduction

Since the 1980s, the vigorous development of multinational corporations has led to the continuous refinement of international division of labor, and the world economy has entered an era of global value chain division of labor dominated by enterprises in international division of labor and trade [1,2]. Countries around the world have utilized their factor endowment advantages to achieve large-scale production, promoting the vigorous development of intra- and inter-industry trade [3]. However, since the 2008 international financial crisis, the global economy has been constantly hit to varying degrees in the recovery, trade protectionism has gradually risen worldwide, and the impact of political frictions such as Sino-US trade frictions, the Russia-Ukraine conflict, the COVID-19 epidemic, and other global crises has had a greater impact on the world division of labor order [4,5]. The economic structure of Regional Comprehensive Economic Partnership (RCEP) member countries is highly complementary, with complete capital, technology, and labor factors. The signing of the agreement can effectively promote the free flow of economic factors within the region, strengthen production division and cooperation among members, and promote the further development of the regional industrial chain, value chain, and supply chain [6,7]. The signing of trade agreements is of great significance for improving China’s level of opening-up, releasing economic growth pressure in the Asia–Pacific region, and promoting a new round of economic globalization.

The global value chain is the expansion and extension of a company’s value creation process from internal division of labor to global division of labor. In the process of global trade, each stage of trade is a process of value creation, and the added value generated at different stages varies greatly. The wood-based panel industry is a traditional advantageous industry in China’s wooden forest product trade, and it is also a renewable and environmentally friendly material [8]. It not only has a wide range of exports in the international market but also has a large export volume, making it an important component of the global wooden forest product market. However, China’s wood-based panel industry has a relatively low position in the division of labor in the global value chain. Therefore, accurately assessing the comparative advantage level of China’s wood-based panel industry within the framework of the RCEP free trade agreement and exploring the impact of global value chain (GVC) participation and division of labor on China’s comparative advantage of forest products is of great practical significance for solving the challenges of forest product trade in the anti-globalization trend and post-pandemic era.

The main research content of this article includes the following points: Firstly, by comparing the differences, advantages, and disadvantages of international comparative advantage indices in existing research and making necessary improvements on the basis of existing measurement methods. The improved international comparative advantage index has a clearer basis for judgment and can more intuitively reflect whether a country’s products have comparative advantages in the markets of other countries. Secondly, the newly constructed index can more accurately measure the comparative advantage level of China’s artificial board industry in RCEP countries. Thirdly, explore the impact of China’s division of labor position in the global value chain on the comparative advantage of the artificial board industry using the newly constructed comparative advantage evaluation indicators. Finally, based on the research findings, targeted policy recommendations are proposed to enrich the existing research content on the trade competitiveness of forest products while also providing useful references for the export trade of Chinese wood-based panels.

The marginal contribution of this article mainly includes the following two points:

Firstly, the level of participation and division of labor in the global value chain is not determined by a single factor in a short period of time, but the division of labor in the international value chain of a certain industry is difficult to change after its formation. Taking the overall value chain division of labor status of the industry as the starting point, analyzing the changes and impacts of China’s comparative advantage of forest products in RCEP countries can provide a more in-depth exploration of the impact of global value chain division of labor status on segmented industries. And to a certain extent, this article expands the relevant research on global value chain issues in traditional manufacturing and also supplements the exploration of the internal reasons that affect the international comparative advantage of forest products.

Secondly, further improvements have been made to the measurement method for the international comparative advantage of industries. Based on the Multinational Indicative Comparative Advantage Index ($B_i^{*k}$) and combined with the actual research needs of this article, it has been improved into a targeted Multinational Indicative Comparative Advantage Index ($N{B}^{*}$). Although the $B_i^{*k}$ index can more specifically explore the comparative advantage level of each country in a selected region for a particular product compared to the traditional RCA index, its comparison is still based on the entire international market, and the improved $N{B}^{*}$ index can more specifically compare the comparative advantage level of a country’s specific product in a specific country. By using the newly constructed comparative advantage index to determine the level of comparative advantage of products in the international market, it has a higher reference value for the decision-making level of enterprises and even the entire industry when developing new product markets or adjusting business strategies.

2. Literature Review

2.1. The Impact of Global Value Chain Participation and Division of Labor Status on the International Competitiveness of Industries

Global events such as COVID-19 and the Russia-Ukraine conflict have had a serious negative impact on the continuous deepening of the global value chain, and the restructuring of the global value chain has also been constantly adjusted [9,10,11]. The level of concern among people about the global value chain is also constantly increasing. The focus of existing literature on the impact of global value chains on the international competitiveness of industries is mainly at a broader industry level, including the manufacturing industry [12], service industry [13], information industry, and high-tech industry [9]. The consensus among scholars is to improve the competitiveness of an industry and avoid “low-end lock-in” when it comes to the impact of its division of labor position in the global value chain on its competitiveness in global trade [14]. Actively participating in the global value chain division of labor and corresponding policy support can effectively improve the international competitiveness of China’s agricultural products [15]. Some scholars have found in their analysis of the competitiveness of national manufacturing industries based on the factor income of the global value chain that promoting the specialization process of high-value-added “activities” in the manufacturing industry can effectively improve the division of labor positions of some manufacturing industries in the global value chain [16,17]. Improving independent innovation capabilities and opening up to the outside world by participating in the research, development, and design of high-end manufacturing industries can not only enhance the international competitiveness of China’s high-tech industry but also change the awkward situation of low-value-added processing and assembly [18].

2.2. Exploration of Evaluation Methods for Industrial Comparative Advantage

Revealed comparative advantages (RCA) proposed by American economist Balassa has opened a new chapter for further research and application of industry international competitiveness [19]. Compared to international market share and other indices, the RCA index eliminates the impact of fluctuations in national and global aggregates and can better reflect the difference between a country’s exports of a certain industry and the world’s average export level. It is considered an important indicator to reflect the comparative advantage of a country’s industry [20]. However, with the continuous deepening of practical research, there are still some unresolved issues when measuring and evaluating the international competitiveness of industries using the RCA index, including whether products from one country are competitive in another country’s market when it is not possible to obtain them with certainty [21], the calculated index value does not have symmetry, and there is a lack of a clear mathematical basis when determining whether an industry has a comparative advantage [22].

Therefore, Dalum et al. divided the index value of RCA by adding and subtracting 1 to achieve a symmetrical distribution of values and also provided a basis for determining whether the industry has a comparative advantage [23]. Proudman and Redding proposed the Indicative Comparative Advantage Contribution Index (WRCA) in 2010 [24], which averages the revealed comparative advantage index of participating countries across countries. The expected value of the revealed comparative advantages index of participating countries is used as a reference standard for evaluating whether a country’s industry has a comparative advantage, further narrowing the scope of comparison from the entire international trade market to the selected countries participating in the comparison [25]. Luo Yiyi constructed the Indicative Comparative Advantage Weighting Index (WRCA), which proposes the impact of product import trade on product comparative advantage [26]. In addition, to explore the differences in comparative advantages between the two countries, the French proposed the RRCA index based on the RCA index, which limits the total export volume of a country’s specific products to a specific country, allowing for a more direct exploration of the differences in comparative advantages of specific products between the two countries in practical research [21].

2.3. Research on the Impact of the Signing of a Regional Comprehensive Economic Partnership Agreement on China’s Forest Products Trade

The signing of RCEP is an important measure for China to enhance its level of opening-up and promote market integration in the Asia Pacific region, which is of great significance for promoting trade growth and economic development in the Asia Pacific region and even globally [27,28,29]. Therefore, the economic and trade theme based on RCEP is a hot topic of concern for scholars. The relevant research on the signing of the RCEP agreement on China’s forest product trade mainly includes the following aspects: Firstly, in exploring the changes in the trade pattern of forest products [30], Li Yuxin et al. pointed out in their analysis of the trade pattern of forest products between China and other member countries of RCEP through social network analysis methods that the signing of RCEP has an important driving role in optimizing the trade structure of forest products among member countries [31]. The second is to explore the growth of forest product exports between China and RCEP member countries. Yang Jun et al. analyzed the growth of China’s forest product exports through a ternary marginal analysis. The study found that increasing the export price of China’s forest products and expanding the export scope are important growth points for improving the export trade of China’s forest products in RCEP member countries [32]. Thirdly, in an exploration of the sustainability of China’s forest product trade with RCEP member countries [33], research has pointed out that there are significant differences in the duration of trade between different types of forest products and RCEP member countries. Improving the international competitiveness of capital- and technology-intensive forest products can effectively improve the duration of forest product trade between China and RCEP member countries [34]. In addition, scholars have also explored the complementarity of forest product trade between China and RCEP member countries, fully affirming the positive impact of RCEP on China’s international trade of forest products [35,36].

2.4. Research Review

In summary, existing literature has explored the impact of global value chain participation on industrial competitiveness from multiple perspectives, and the evaluation methods for international comparative advantage of industries have been continuously improved, becoming closer to practical research needs and providing many useful references for this study. However, there is still room for further in-depth research on the segmentation of global value chains in the international comparative advantage of industries, as well as the measurement methods for the international comparative advantage of industries. Firstly, in the field of research object segmentation, there are many specific industry classifications within the same industry category, and there are significant differences among different industries. The impact of changes in the participation status of the global value chain on specific industries still needs further analysis. Secondly, wooden forest products are important green products, and different forest products have been fully integrated into various links of the global value chain. However, in the context of the global value chain, further research on the international trade of forest products still needs to be supplemented. Finally, in terms of the measurement methods for international comparative advantage of industries in existing studies, the traditional RCA index cannot focus the research objectives on the scope of RCEP agreement countries, and there is a lack of basis for determining whether a certain industry has a comparative advantage based on the index value. Therefore, in order to more accurately evaluate the comparative advantage of China’s forest products in RCEP countries, it is necessary to improve and revise the existing indicative comparative advantages.

3. Research Method

3.1. Introduction to the Multinational Indicative Comparative Advantage Index

Although the RCA index proposed by Balassa can rank the comparative advantages of a specific industry in the international market, countries with higher comparative advantages in specific industries may not have a higher influence among those ranked lower. Therefore, considering the limitations of the RCA index, Amadoretal borrowed the idea of the Indicative Comparative Advantage Weighting Index (WRCA) proposed by Proudman et al. [24]. He averaged the display comparative advantage index of participating countries and proposed the multi-country display comparative advantage index ($B_i^{*k}$) [37]. This index uses the expected value of the indicative comparative advantage index of the participating countries as a reference standard to evaluate whether a country’s industry has a comparative advantage. Therefore, it can to some extent narrow the comparative range, that is, determine whether a country’s products have a comparative advantage compared to other selected countries during the same period within the established comparative range. Compared to traditional RCA indices, the definition of research scope and the comparison of product comparative advantages between countries have improved the accuracy of comparison. The specific calculation formula is as follows:

(1)$B_i^{*k}=\frac{\frac{X_i^k/X_i}{X_w^k/X_w}}{\frac{1}{N_c}{{\displaystyle \sum }}_{i=1}^{N_c}\frac{X_i^k/X_i}{X_w^k/X_w}}$

After simplification:

(2)$B_i^{*k}=\frac{\mathrm{RCA}}{\frac{1}{N_c}{{\displaystyle \sum }}_{i=1}^{N_c}\mathrm{RCA}}$

In this formula, the numerator represents the displayed comparative advantage index of the k industry in country i, while the denominator represents the average value of the displayed comparative advantage index of the k industry in the participating countries. Among them, N_c represents the number of countries participating in the comparison, X_i^k represents the export volume of country i’s k industry, X_i represents the total export volume of country i, X_w^k represents the total export volume of global k products, and X_w represents the total global export volume. At this point, the average of the $B_i^{*k}$ index is 1. When $B_i^{*k}$ is greater than 1, it indicates that the k industry in country i is higher than the average comparative advantage of the k industry in the participating countries. On the contrary, when $B_i^{*k}$ is less than 1, it indicates that the k industry of country i does not have a comparative advantage in the participating countries. The commonality between the $B_i^{*k}$ index and WRCA index lies in the fact that they can have a definite mean of 1. Therefore, it is more reasonable to use 1 as a reference standard to evaluate whether industries in different countries have comparative advantages.

The core idea of the Multinational Display Comparative Advantage Index $B_i^{*k}$ lies in the application of the expected value of the Display Comparative Advantage Index, which determines whether a country has a comparative advantage within the range of participating countries by determining whether its Display Comparative Advantage Index is greater than the expected value. Although this index can narrow the scope of comparison from the entire international trade market to the selected country range and compare which country’s products have a higher comparative advantage in the same period within the established research scope, it still cannot specifically explore whether a country’s products have a comparative advantage in another country’s market. Therefore, in order to further explore the comparative advantage differences of China’s wood-based panel industry in RCEP countries, necessary revisions are made to the $B_i^{*k}$ index.

3.2. Improvement of the Multinational Display Comparative Advantage Index

The Multinational Display Comparative Advantage Index $B_i^{*k}$ reduced the scope of comparison from the world to selected countries and improved the accuracy of measuring product comparative advantages between countries. However, it did not improve the RCA index’s lack of product focus when measuring comparative advantages. Therefore, this article revised the index to measure accuracy.

The RRCA index measures the proportion of a country’s imports of a specific product from another country to the global imports of that product and compares it with the ratio of the total imports of all products from that country to the total imports of all products from the world. Compared to the traditional RCA index, it has higher specificity when exploring the differences in industrial comparative advantages between countries. The specific calculation formula is expressed as:

(3)$RRCA=\left(M_{ij}^k/M_{jw}^k\right)/\left(M_{ij}/M_j\right)$

Among them, M_ij^k represents the import amount of k products imported by country j from country i; M_jw^k represents the total import amount of product k by country j; M_ij represents the total import amount of products imported by country j from i; M_j represents the total import volume of country j. On this basis, the RRCA index is combined with the B* index idea and subjected to averaging processing to obtain a targeted multi-country comparative advantage index, which is expressed by the formula:

(4)$N{B}^{*}=\frac{\left(M_{ij}^k/M_{jw}^k\right)/\left(M_{ij}/M_j\right)}{\frac{1}{N_c}{{\displaystyle \sum }}_{i=1}^{N_c}\left[\left(M_{ij}^k/M_{jw}^k\right)/\left(M_{ij}/M_j\right)\right]}$

In the formula, M_ij^k represents the import amount of k products imported by country j from country i; M_jw^k represents the total import amount of product k by country j; M_ij represents the total import amount of products imported by country j from country i; M_j represents the total import volume of country j; and N_c represents the number of countries participating in the comparison. The improved index, where the numerator represents the indicative comparative advantage index of the k industry in country i in country j, and the denominator represents the expected value of the comparative advantage of the k industry in country i in the selected country. The average of the NB* index is 1, which means that when the comparative advantage of country i’s k-industry in country j is greater than the expected value of regional comparative advantage, the NB* index is greater than 1, indicating that country i’s k-industry has a comparative advantage in country j. When the NB* index is less than 1, it indicates that country i’s k industry does not have a comparative advantage over country j.

By combining the specificity of the RRCA index in comparison with the B* index to provide a basis for judgment and focus on the advantages of the research scope, the improved index can more specifically explore whether a country’s product has a comparative advantage in another country within the selected research scope.

3.3. Calculation of Global Value Chain Participation and Division of Work Status Index

Assessing the level of participation and division of labor of countries in the global value chain has important reference value for their participation in international trade. To this end, Koopman et al. proposed the “GVC Participation Index” and “GVC Division Status Index” by decomposing and calculating a country’s total output, making important contributions to measuring a country’s participation and division status in the global value chain. According to Koopman’s definition, the higher the indicator value of a country’s GVC Participation Index ($GVC\_Participatio{n}_r$), the higher the country’s participation in the global value chain, while the GVC Position Index ($GVC\_positio{n}_r$) indicates a country’s specific position in the value chain. The higher the value of the GVC Participation Index, the higher the country’s upstream position in the global value chain. The proportion of added value obtained in the domestic production process is higher than that of foreign added value, and vice versa. The calculation formula for the GVC participation index and the GVC division of labor status index is as follows:

(5)$GVC\_Participatio{n}_r=\frac{I{V}_r}{E_r}+\frac{F{V}_r}{E_r}$

(6)$GVC\_positio{n}_r=\ln \left(1+\frac{I{V}_r}{E_r}\right)-\ln \left(1+\frac{F{V}_r}{E_r}\right)$

Formulas (5) and (6) respectively represent the GVC participation index and the GVC division of labor status index, where $I{V}_r$ represents the added value of intermediate goods exported by country r, which is the domestic production added value included in country r’s exported products, $F{V}_r$ represents the foreign added value of country r’s exports, which is the foreign added value included in country r’s exported products, and $E_r$ represents the total export value added of country r.

4. Analysis of the Comparative Advantages of China’s Wood-Based Panel Industry in RCEP Countries

4.1. The Comparative Advantage Level of China’s Wood-Based Panel Industry in RCEP Countries under the $B_i^{*k}$ Index

According to Formula (2), it can be seen that when the $B_i^{*k}$ index is greater than 1, it indicates that a country’s wood-based panel industry is higher than the average comparative advantage level of RCEP member countries. For example, the $B_i^{*k}$ value of China’s wood-based panel industry in 2014 was 1.17, indicating that the comparative advantage of China’s wood-based panel industry was higher than the average comparative advantage level of RCEP member countries in 2014. Conversely, it indicates that China’s wood-based panel industry is weaker than the average level of RCEP member countries. The specific calculation results are shown in Table 1.

According to Table 1, among RCEP member countries, Indonesia, Malaysia, New Zealand, and Thailand have the highest level of international competitiveness in the wood-based panel industry, with Indonesia having the highest level of comparative advantage and an average comparative advantage index of 4.97. The panel industry in Australia, the Philippines, South Korea, Laos, Myanmar, Japan, Brunei, Singapore, and Vietnam has a relatively high level of competitiveness among RCEP member countries. In addition, although China is a major manufacturing country in the wood-based panel industry, its comparative advantage in the entire RCEP region is not significant. From the data, it can be seen that China’s wood-based panel industry has gone through a process of increasing and then decreasing, reaching its highest value in 2008 and continuously decreasing thereafter. By 2020, its comparative advantage level was 0.67, and during the data review period, its average comparative advantage level was only 0.17. Therefore, when calculating the international comparative advantage level of the wood-based panel industry of RCEP member countries using the $B_i^{*k}$ index, only the comparative advantage strength of the wood-based panel industry within the region can be obtained, and it is not clear whether a country’s wood-based panel industry has a comparative advantage level in the artificial board market of each member country.

4.2. $N{B}^{*}$ Index Evaluation of the Comparative Advantage Level of China’s Wood-Based Panel Industry in Other RCEP Member States

The ${NB}^{*}$ index obtained by improving the $B_i^{*k}$ index can clarify the comparative advantage level of a country’s products in other countries and calculate the comparative advantage level of China’s wood-based panel industry in other RCEP member countries. The specific values are shown in Table 2.

According to Table 2, there are significant differences in the comparative advantage level of China’s wood-based panel industry in the markets of RCEP member countries. Firstly, for developed countries, China’s wood-based panel industry does not have a comparative advantage in markets such as Australia, Japan, South Korea, and New Zealand. One possible reason is that the production and manufacturing of wood-based panels is gradually shifting from labor-intensive to capital- and technology-intensive industries, and these countries have a higher participation in the global value chain of the wood industry. The import sources of wood-based panels are more diversified, and developed countries are more pursuing their quality and lower prices for wood-based products such as wood-based panels.

Secondly, although Singapore is a developed country, the industrial chain of primary product manufacturing, such as wood-based panels, is not complete, and it holds a high position in the global value chain. Moreover, due to its small land area and relatively low total forest resources, China’s artificial panel industry can have a high level in its domestic market. Thirdly, for developing countries, China’s wood-based panel industry has high competitiveness in the domestic markets of the Philippines, Cambodia, Laos, and Thailand. The possible reason is that these developing countries have a relatively low level of participation in the global value chain, while China’s wood-based panel industry has a higher level of participation in the global value chain, and its abundant forest resources have natural advantages for the development of the wood-based panel industry. Finally, although Vietnam and Indonesia are also developing countries, with China’s labor costs increasing year by year, Chinese wood-based panel production enterprises are constantly relocating to Southeast Asia, South Asia, and other countries in search of lower production and operation costs. Therefore, the comparative advantage level of China’s wood-based panel industry in these countries generally shows a trend of first increasing and then decreasing, which is in line with the actual development of China’s wood-based panel industry.

5. Models, Variables, and Data Sources

5.1. Model Determination

The impact of the global value chain on the comparative advantage of China’s wood-based panel industry in RCEP countries includes two aspects: One is the level of participation in the global value chain, and the other is the division of labor positions in the global value chain. Therefore, referring to the econometric model methods for multiple explanatory variables in existing literature, the sample data are analyzed using multiple linear regression. The specific econometric model is shown in Equation (7):

(7)$N{B}^{*} = {\alpha }_0 + {\alpha }_1{\mathit{GVCPAT}}_{\mathit{it}} + {\alpha }_2{\mathit{GVCPOS}}_{\mathit{it}} + {\alpha }_3\mathit{Control} + \epsilon$

In the formula, $N{B}^{*}$ represents the comparative advantage of China’s wood-based panel industry in country k in the i-th year, which is the revised mean index of the displayed comparative advantage of multiple countries. α₀ is a constant term, GVCPAT_it represents the GVC participation index, and GVCPOS_it represents the GVC division of labor status index. Control represents a control variable.

5.2. Variable Selection

Therefore, this article selects the improved New Multinational Indicative Comparative Advantage Index ($N{B}^{*}$) as the dependent variable, and the specific calculation formula is shown in Equation (4). Based on the calculation process of the index, the comparative advantage of China’s wood-based panel industry in RCEP markets can be measured and calculated in a targeted manner, which meets the practical research needs of this article. The global value chain participation index and the division of labor status index of the global value chain are used as explanatory variables.

For the selection of control variables, this article refers to the relevant research results on the international competitiveness of the wood-based panel industry and selects corresponding control variables from three aspects: production factors, demand factors, and relevant technical support. From the perspective of production factors, the forest resource reserves of various countries are an important foundation for the production and development of the wood-based panel industry. Therefore, the forest area of each country is chosen to measure its forest resource endowment. According to the development pattern of the wood-based panel industry structure, the wood-based panel industry in various countries will undergo a transformation from labor-intensive industries to technology- and capital-intensive industries [38,39]. Therefore, the proportion of agricultural and forestry workers in RCEP countries is selected to reflect their labor resource endowment [40]. Referring to existing literature, the number of patent applications is used as a proxy variable for wood-based panel production technology [41,42]. From the perspective of demand factors, the level of demand for wood-based panels in the domestic market is more reflected in the population and economic development level of each country. Therefore, the proportion of urban population and per capita GDP in each country are selected as the characterization variables of demand factors [43,44]. The volume and weight of artificial boards are relatively large, and they rely more on shipping in international trade. Therefore, the throughput of container terminals in various countries is chosen to reflect the convenience of international trade [45]. The names and abbreviations of each variable are shown in Table 3.

5.3. Data Sources and Descriptive Statistics

The RCEP countries referred to in this article include Australia, the Philippines, South Korea, Cambodia, Laos, Malaysia, Myanmar, Japan, Thailand, Brunei, Singapore, New Zealand, Indonesia, Vietnam, and China.

The data used in this article to calculate the $N{B}^{*}$ index of wood-based panels in China and RCEP countries is all taken from the UN Comtrade Database. The forest product coding adopts the 2002 version of the International Convention on the Harmonized System of Commodity Names and Coding, where the HS code for artificial boards is HS4410-4413.

The existing GVC participation index and division status index are calculated based on the International Input-Output Table (ICIO), and the specific ICIO includes the input-output database of the organization for economic cooperation and development (OECD-ICIO), the input-output database of the Asian Development Bank (ADB-ICIO), the global trade analysis database (GTAP-ICIO), and the world input-output table (WIOT) in the world input-output database (WIOD). However, due to the lack of time continuity, too stringent assumptions, and less coverage of economies in the above database, it is difficult to meet the actual research needs of this paper. Therefore, this paper selects the UIBE GVC Indicators database constructed by the University of International Business and Economics after processing the above ICIO table to measure the participation and division of labor status of RCEP countries in the global value chain. The relevant data for the control variables in this paper are all from the World Bank database. The descriptive statistics of each variable are shown in Table 4.

6. Empirical Test

Correlation test: Determine whether there is an exact correlation between the dependent variable and the explanatory variable, and perform correlation analysis on the variables included in the model. According to the test results, at the 5% significance level, the correlation coefficients between the comparative advantage index of the dependent variable and the GVC participation index and division of labor status index are greater than 0.3, indicating a moderate correlation. Therefore, the empirical analysis in the following text has a certain correlational significance. The specific inspection results are shown in Table 5.

Multiple collinearity tests. Considering the large number of variables used in the article and the possibility of multicollinearity between variables, the variance inflation factor (VIF) was used for testing. According to the test results, the VIF value of the GVC participation index is 3.340, the VIF value of the GVC division of labor status index is 4.560, and the overall mean is 3.930, both of which are smaller than the VIF threshold of the variance inflation factor test of 10, indicating that there is no serious multicollinearity problem between variables and regression can be conducted.

Heteroscedasticity test. Due to the reduced explanatory power of the model regression results caused by heteroscedasticity, the White test was used to test the model regression results. According to the test results, the p-value was 0.0009, which rejected the null hypothesis of the same variance, and the model results had heteroscedasticity. To ensure the explanatory power of empirical test results, robust standard errors are added to subsequent econometric regression to address the issue of variable explanatory power decline caused by heteroscedasticity in the model.

Horseman test. To determine whether to choose a fixed effects model or a random effects model in model regression, perform a Hausman test on them [46]. According to the test results, the null hypothesis was rejected at a significance level of 1%, so fixed effects were selected for regression analysis of the data. According to the model setting, NB* is used as the dependent variable, and the GVC participation index and GVC division status index are used as the explanatory variables. The specific regression results are shown in Table 6.

According to the regression results, the GVC participation index of RCEP countries has a significant negative impact on the comparative advantage of China’s wood-based panel industry in RCEP countries. That is, the deeper the participation of countries in the global value chain of wood-based panels, the weaker the attractiveness of China’s wood-based panels in their domestic market. Possible reasons include the following two aspects: Firstly, compared to complex industrial products, artificial panel products have fewer production processes, and product appreciation mainly occurs in the production of semi-finished and finished products. This process uses labor resource endowment intensively, so the deeper a country participates in the value chain, the more abundant its labor resource endowment is, which coincides with China’s advantageous production factors. Therefore, it is not conducive for China to leverage its labor advantage in the global value chain to enhance the comparative advantage of wood-based panel products. This empirical research result is also consistent with the comparative advantage of China’s wood-based panel industry among RCEP member countries. Secondly, according to the research of Han Feng et al. [47], the interaction between wood-based panels as the midstream market of forest products and the raw material market such as wood is relatively large, while the interaction with the end market such as home decoration is relatively small. The deeper a country’s participation in the global value chain of wood-based panels, the more stable it can establish a long-term cooperative dependency relationship with the raw material market due to production stability considerations. China lacks raw materials for wood-based panels such as logs, forming a competitive relationship with other countries in raw material procurement. Therefore, the deeper the participation of each country in GVC, the more stable the supply channels of raw materials, which is not conducive to improving the comparative advantage of China’s wood-based panels in the country.

The division of labor status index among RCEP countries has a significant positive impact on the comparative advantage index of Chinese wood-based panels in each country; that is, the closer RCEP countries are to the upstream link in the value chain, the more market share China’s wood-based panels have in their countries. The reasons for this result may include the following: One is that the higher the division of labor status index of each country, the more it tends to be in a relative upstream link, while China is in a relatively downstream link of the artificial board value chain. Therefore, being in a relatively upstream link is more conducive to the connection and cooperation between China and the artificial board market in that country, forming an interdependent upstream and downstream market, thereby improving the comparative advantage of Chinese artificial boards in that country. Secondly, according to the definition of the global value chain, the upstream of the artificial board value chain mainly includes the procurement and transportation of raw materials, as well as the production and distribution of semi-finished and finished products. The more a country is in a relative upstream position, the greater the export volume of artificial board raw materials or semi-finished products. China is a major importer of artificial board raw materials, and the wood-based panel industry belongs to a forest resource-dependent industry. Rich forest resources are the foundation of artificial board product production. Therefore, the higher the division of labor status index of RCEP countries, the more favorable it is for China to expand its raw material supply channels, make up for the shortage of China’s forest resource endowment, and improve the comparative advantage of China’s wood-based panel industry in various countries by reducing production costs.

Robust Test: From the calculation process of the comparative advantage index of Chinese wood-based panels in various countries mentioned earlier, it can be seen that the average of the displayed comparative advantage index in multiple countries within the RCEP country range is 1. The value of the comparative advantage of Chinese wood-based panels in a certain country is influenced by the comparative advantage values of other RCEP agreement countries; that is, the dependent variable is limited to the dependent variable. Therefore, this article uses the truncated regression method to analyze the robustness of the measurement results. Use Stata(version: StataMP 16.0) software to tabulate the data of the dependent variable, namely the comparative advantage index, and take the corresponding comparative advantage values at 10% and 90% of the cumulative distribution probability as the left and right tails of the truncated regression. The specific regression results are shown in Table 7.

According to the robustness test results, the impact and significance of the key explanatory variables discussed in the article on the dependent variable are basically consistent with the results in the benchmark regression model, indicating that the empirical test results have high reliability.

7. Discussion

This article calculates the comparative advantage level of the wood-based panel industry in RCEP member countries using the $B_i^{*k}$ comparative advantage index and the comparative advantage level of China’s wood-based panel industry in other RCEP member countries using the ${NB}^{*}$ comparative advantage index. What can be seen is that the $B_i^{*k}$ index calculates the comparative advantage level of the wood-based panel industry, clarifying the differences in the competitiveness level of the wood-based panel industry among RCEP member countries. For example, according to the calculation results, Indonesia’s wood-based panel industry has extremely high industrial competitiveness within the RCEP region, but there is no more effective guidance for the future development of Indonesia’s wood-based panel industry, as it cannot be determined in which markets the wood-based panel industry in Indonesia is more popular and in which countries there is insufficient competitiveness. At the same time, it is not possible to conduct in-depth research on the underlying reasons for the insufficient level of comparative advantage in certain countries. On the contrary, taking China’s wood-based panel industry as an example, calculating the comparative advantage level of China’s wood-based panel industry in the markets of RCEP member countries through the $N{B}^{*}$ index is a better reference for studying the future development of China’s wood-based panel industry. In addition, the empirical test of this article further verifies the impact of the participation level in the global value chain and the division of labor status on the comparative advantage level of China’s wood-based panel industry in RCEP member countries and further confirms that the ${NB}^{*}$ index can more accurately reflect the comparative advantage level of a country’s industry in other countries within the selected range.

8. Conclusions

Based on the research results of this article, the following research conclusions are summarized:

Firstly, the newly constructed ${NB}^{*}$ index provides a new approach for studying the international competitiveness of industries. Although China’s wood-based panel industry has a relatively low level of comparative advantage among RCEP member countries, it still has a relatively high level of comparative advantage in specific national markets. Therefore, simply ranking the international comparative advantage level of each country’s wood-based panel industry has low reference value for the future development of the industry; clarifying the comparative advantage level of China’s wood-based panel industry in exports to various countries has important reference value for China’s wood-based panel export trade.

Secondly, a higher level of industry chain participation has a significant positive impact on the comparative advantage level of specific industries in the international market. Therefore, in order to continuously improve the international comparative advantage level of China’s wood-based panel industry chain, it is necessary to further participate in the entire industry chain of the wood-based panel industry. On the one hand, it can ensure the supply of raw materials and access to cutting-edge production technologies in the wood-based panel industry, and on the other hand, it can also enhance the diversification of China’s wood-based panel industry exports.

Thirdly, the division of labor in the industrial chain has a significant positive impact on the international comparative advantage of exporting countries. The industrial chain of wood-based panel production is relatively short, and the higher division of labor status in the industrial chain reflects a higher level of production technology and specialization. Therefore, in order to further expand the comparative advantage level of China’s wood-based panel industry in the markets of RCEP member countries, it is still necessary to continuously improve the production technology and specialization level of China’s wood-based panel industry.

Through the research findings of this article, we have found that in order for traditional manufacturing industries to improve their competitiveness in the international market, they need to continuously climb their division of labor position in the global value chain and increase their participation in the global value chain. This has a certain reference value for the development of national industries. At the same time, we found that there is room for further improvement in the traditional international comparative advantage evaluation methods for industries. Calculating whether a country’s products are competitive in other countries’ markets through the NB* index is something that traditional comparative advantage evaluation indexes cannot achieve. Therefore, the new index will also be more valuable for the development decisions of enterprises and even industries.

Author Contributions

Conceptualization, Y.L. and W.Z.; methodology, Y.L.; software, C.T.; validation, Y.L., W.Z. and C.T.; formal analysis, Y.L.; investigation, Y.L.; resources, W.Z.; data curation, W.Z.; writing—original draft preparation, Y.L.; writing—review and editing, Y.L.; visualization, C.T.; supervision, B.C.; project administration, B.C.; funding acquisition, B.C. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

Data are available upon request.

Conflicts of Interest

The author declares no conflict of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Tables

Table 1

Comparative Advantage Level of China’s Wood-based Panel Industry among RCEP Member States $B_i^{*k}$ Index).

	AUS	PHL	SKO	CAM	LAO	MAS	MMR	JPN	THA	BRU	SGP	NZL	IDN	VIE	CHN
2000	0.20	0.02	0.09	1.43	0.00	2.48	0.00	0.01	0.31	0.00	0.11	3.06	7.09	0.04	0.17
2001	0.26	0.02	0.07	1.09	0.00	2.63	0.00	0.01	0.36	0.00	0.09	2.95	7.28	0.05	0.20
2002	0.30	0.06	0.05	0.29	0.00	2.76	0.00	0.01	0.46	0.00	0.09	3.27	7.33	0.07	0.30
2003	0.31	0.05	0.05	0.16	0.00	2.93	0.00	0.01	0.44	0.00	0.06	3.52	7.08	0.11	0.28
2004	0.35	0.12	0.06	0.08	0.00	3.28	0.00	0.01	0.57	0.00	0.08	3.65	6.18	0.07	0.55
2005	0.37	0.13	0.04	0.14	0.00	3.47	0.00	0.01	0.70	0.00	0.04	3.78	5.40	0.09	0.82
2006	0.26	0.06	0.02	0.01	0.00	3.98	0.00	0.01	0.83	0.01	0.04	3.36	5.28	0.12	1.02
2007	0.21	0.10	0.02	0.00	0.00	4.11	0.00	0.01	0.99	0.01	0.07	3.43	4.71	0.15	1.22
2008	0.17	0.15	0.02	0.00	0.00	3.80	0.00	0.01	1.15	0.02	0.06	3.68	4.49	0.22	1.23
2009	0.15	0.22	0.02	0.01	0.00	4.26	0.00	0.01	1.22	0.04	0.05	3.62	4.00	0.33	1.08
2010	0.09	0.10	0.01	0.01	0.37	3.73	0.85	0.01	1.19	0.07	0.04	3.23	3.84	0.41	1.04
2011	0.05	0.19	0.02	0.03	0.21	3.59	1.02	0.01	1.20	0.01	0.04	3.35	3.72	0.41	1.16
2012	0.05	0.09	0.03	0.05	0.22	3.59	0.41	0.01	1.14	0.00	0.03	3.59	4.13	0.47	1.20
2013	0.04	0.18	0.03	0.23	0.09	3.52	0.29	0.01	1.20	0.00	0.03	2.93	4.63	0.69	1.14
2014	0.05	0.05	0.03	0.00	0.08	3.11	0.79	0.01	1.32	0.03	0.03	2.64	5.03	0.66	1.17
2015	0.05	0.01	0.02	0.01	0.12	2.55	1.39	0.02	1.21	0.00	0.03	2.88	5.20	0.54	0.99
2016	0.04	0.00	0.02	0.25	0.04	2.28	2.01	0.03	1.15	0.00	0.02	2.84	4.78	0.59	0.95
2017	0.03	0.32	0.02	1.74	0.23	2.09	1.78	0.03	1.27	0.00	0.02	2.61	3.33	0.65	0.89
2018	0.03	0.53	0.01	2.47	0.14	1.90	0.89	0.03	1.18	0.00	0.02	2.42	3.65	0.90	0.83
2019	0.03	0.65	0.01	2.75	0.94	1.59	0.17	0.03	1.33	0.00	0.01	2.35	3.49	0.92	0.72
2020	0.04	0.69	0.01	3.48	0.00	1.29	0.68	0.03	1.43	0.00	0.01	2.00	3.76	0.91	0.67
Mean	0.15	0.18	0.03	0.68	0.12	3.00	0.49	0.01	0.98	0.01	0.05	3.10	4.97	0.40	0.84

Table 2

Comparative Advantage Level of China’s Wood-based Panel Industry among RCEP Member States ($N{B}^{*}$ Index).

	AUS	PHL	SKO	CAM	LAO	MAS	MMR	JPN	THA	BRU	SGP	NZL	IDN	VIE
2000	0.11	3.57	1.77	0.50	0.00	1.75	0.00	0.28	0.60	0.00	4.22	0.02	0.98	0.18
2001	0.13	1.26	1.74	1.07	0.00	0.87	0.00	0.40	1.73	1.37	4.07	0.13	0.88	0.35
2002	0.13	1.00	1.40	0.16	0.00	0.37	0.00	0.49	2.74	2.13	4.14	0.48	0.71	0.27
2003	0.30	0.39	1.37	0.10	0.00	0.64	0.00	0.56	1.59	1.29	5.43	0.89	0.93	0.49
2004	0.39	0.28	1.09	0.78	0.00	0.50	0.00	0.32	2.53	1.33	3.13	1.32	1.42	0.90
2005	0.48	0.21	1.02	1.53	0.00	1.05	0.00	0.36	2.95	0.00	3.02	1.30	1.02	1.06
2006	0.42	0.38	0.93	0.92	0.00	1.40	0.00	0.30	2.73	0.83	2.49	1.14	1.59	1.15
2007	0.52	0.36	0.90	1.42	0.00	1.07	0.00	0.37	2.63	1.14	2.15	0.90	1.63	1.49
2008	0.60	0.36	0.71	1.51	0.00	1.53	0.00	0.46	2.80	1.02	2.44	0.82	0.84	1.70
2009	0.79	0.31	0.82	1.31	0.00	0.87	0.00	0.44	2.91	0.83	2.86	1.06	0.92	0.89
2010	0.56	0.45	0.63	0.77	2.11	0.75	1.66	0.34	2.09	0.53	1.92	0.68	0.80	0.72
2011	0.53	0.44	0.97	0.98	1.18	0.73	0.93	0.33	2.20	1.26	2.04	0.71	1.00	0.68
2012	0.62	0.42	1.07	0.85	2.15	0.70	0.87	0.33	1.96	0.75	2.08	0.54	0.95	0.70
2013	0.79	1.30	1.19	0.39	1.35	0.58	1.00	0.37	2.22	0.58	2.05	0.66	0.76	0.75
2014	0.77	1.73	0.93	0.92	1.11	0.45	1.05	0.35	1.85	0.62	2.03	0.70	0.75	0.75
2015	0.83	2.16	0.84	1.19	0.96	0.47	0.92	0.34	1.55	0.47	1.86	0.86	0.70	0.84
2016	0.81	1.79	0.65	1.21	1.28	0.54	1.15	0.35	1.52	0.61	1.70	0.91	0.55	0.92
2017	0.94	1.69	0.49	1.23	1.11	0.56	1.41	0.38	1.61	0.55	1.56	0.91	0.55	0.82
2018	0.90	1.59	0.35	1.32	1.41	0.51	1.34	0.35	1.56	0.65	1.37	0.94	0.69	0.71
2019	0.95	1.30	0.29	1.24	1.65	0.72	1.26	0.37	1.39	0.71	1.56	0.89	0.76	0.92
2020	1.09	1.64	0.27	1.43	1.75	0.93	1.48	0.44	1.45	0.78	1.68	0.89	0.78	0.83
Mean	0.60	1.08	0.93	0.99	0.76	0.81	0.62	0.38	2.03	0.83	2.56	0.80	0.91	0.81

Table 3

Variable names and their abbreviations.

	Type	Var	Abbreviation
Explained Variable	the index of comparative advantage	Mean Index of Indicative Comparative Advantage in New Countries	NB*
explanatory variable	GVC	GVC-Participation	GVCPAT
		GVC-Position	GVCPOS
control variable	production factors	The logarithmic value of the number of patent applications	LNPAT
		Logarithmic value of forest area	LNFRT
		Ratio of agricultural workers to total employment	LAB
	Requirement Elements	Urban population ratio	URB
		Per capita GDP logarithm	LNPGDP
	Related and supporting industries	Logarithmic value of container terminal throughput	LNPRT

Table 4

Descriptive statistical analysis of the data used in the model.

Var	Samples	Mean	SD	Min	Max
NB*	154	1.000	0.546	0.000	2.908
GVCPAT	132	0.497	0.177	0.191	0.845
GVCPOS	132	0.046	0.161	−0.399	0.431
LNPAT	124	10.558	11.350	0.000	12.596
LNFRT	154	12.529	12.824	5.059	17.453
LAB	154	24.290	21.910	0.030	72.660
URB	154	61.250	25.601	19.930	100.000
LNPGDP	154	10.045	10.174	6.604	11.951
LNPRT	143	16.236	16.132	11.357	17.451

Table 5

Results of the correlation test.

	NB*	GVCPAT	GVCPOS	LNPAT	LNFRT	LAB	URB	LNPGDP
NB*	1
GVCPAT	0.314 *	1
	0.000
GVCPOS	0.3298 *	0.078	1
	0.000	0.3720
LNPAT	−0.380 *	−0.398 *	−0.492 *	1
	0	0	0
LNFRT	−0.197 *	−0.159	0.475 *	−0.118	1
	0.015	0.069	0	0.191
LAB	0.210 *	−0.263 *	0.3173 *	−0.349 *	−0.056	1
	0.009	0.002	0.0002	0.000	0.488
URB	−0.155	0.274 *	−0.186 *	0.391 *	0.088	−0.935 *	1
	0.055	0.002	0.033	0	0.280	0
LNPGDP	−0.095	0.334 *	−0.174 *	0.269 *	0.161 *	−0.540 *	0.592 *	1
	0.239	0.000	0.046	0.003	0.047	0	0

Note: * represents p < 0.1.

Table 6

Regression Results of the Benchmark Model.

Var	Coef	Std	p-Value
GVCPAT	−0.637	0.341	0.065
GVCPOS	4.590	0.798	0.000
LNPAT	0.080	0.050	0.114
LNFRT	−0.269	0.027	0.000
LAB	0.009	0.010	0.337
URB	−0.039	0.156	0.000
LNPGDP	0.540	0.101	0.001
LNPRT	0.020	2.295	0.846
Cons	0.693	2.295	0.763

Table 7

Regression results of the tail docking model.

Var	Coef	Std	p-Value
GVCPAT	−1.297	0.479	0.007
GVCPOS	3.077	1.326	0.020
LNPAT	0.005	0.073	0.000
LNFRT	−0.251	0.051	0.450
LAB	−0.009	0.013	0.000
URB	−0.0711	0.017	0.000
LNPGDP	0.279	0.263	0.001
LNPRT	0.279	0.151	0.065
Cons	−3.577	3.451	0.300

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