Previous studies have demonstrated that enhancements in various aspects of trade facilitation indicators (such as infrastructure, port efficiency, institutional environment, e-commerce, and marketization process) can directly foster the export technological complexity of manufacturing enterprises. Improvements in infrastructure can effectively reduce enterprises’ transportation costs during bilateral trade, thereby enhancing their profitability and subsequently boosting the export technological complexity of manufacturing enterprises ( Yu & Li, 2016 ). Enhanced port efficiency can significantly reduce customs clearance times for enterprises, thereby decreasing export product losses and increasing export frequency, thus positively impacting the export technological complexity of manufacturing enterprises ( Hornok & Koren, 2015 ). Improvements in the institutional environment can effectively enhance the business environment for enterprises, thereby stimulating technological innovation in manufacturing enterprises. The development of e-commerce has notably lowered barriers to technology transmission and technology spillover among enterprises, expediting the transfer of technology from enterprises with high technological complexity to those with low technological complexity. Building upon these findings, this paper proposes Research Hypothesis 1:

Hypothesis 1: Improvement in trade facilitation has a positive effect on the export technological complexity of manufacturing enterprises.

Previous studies have demonstrated that trade facilitation can enhance the export technological complexity of manufacturing enterprises through various pathways, including the cost reduction effect, technological innovation effect, competition effect, and productivity improvement.

From the perspective of the cost reduction effect, cost reduction can effectively enhance the efficiency of enterprise resource allocation, bolster enterprises’ ability to withstand market risks, and optimize factors of production, consequently improving enterprises’ export technological complexity ( Lei & Lang, 2020 ). Improvement in trade facilitation can effectively reduce transportation costs, cross-border transaction costs, and information acquisition search costs during bilateral trade, thereby lowering the cost of innovation and knowledge transfer. This reduction in costs breaks down trade barriers for enterprises, fosters economies of scale, and thus promotes the enhancement of enterprises’ technological complexity ( Yao, 2022 ). As trade facilitation improves, the cost of production segmentation for enterprises decreases continuously, leading to deeper production segmentation among enterprises. When the cost of outsourcing auxiliary production activities to downstream production enterprises is lower than the cost of in-house production, enterprises will reduce production costs by outsourcing. This allows enterprises to focus on research and development and the production of core products, thus facilitating continuous improvement in technological complexity ( Dong, 2022 ). Additionally, improvement in trade facilitation can enhance export technological complexity by shortening procurement lead times, reducing safety inventory, and lowering inventory costs for enterprises ( Duan & Jing, 2021 ).

From the perspective of the technological innovation effect, improvements in trade facilitation can effectively reduce consumer product prices, indirectly increase consumer income levels, and stimulate demand for diverse imported products. The influx of numerous new products expands learning opportunities for enterprises. Enterprises enhance the technological complexity of exports through learning effects and technology spillovers ( Xu et al., 2017 ). As the value chain embedment deepens, linkages between enterprises at different positions in the value chain become more frequent. Enhanced trade facilitation accelerates industrial agglomeration formation. To enable downstream industries to effectively produce intermediate products for upstream industries, the latter may share some non-core key technologies with the former, thus improving the technological complexity of downstream industries ( Wang & Zheng, 2019 ). Improved trade facilitation is conducive to breaking down trade barriers and promoting technological innovation. Specifically, e-commerce development in trade facilitation effectively reduces inter-enterprise technology transmission and spillover costs, fostering more frequent inter-enterprise technology exchange and enhancing technology sharing and diffusion capabilities. This leads to improved innovation capability, efficiency, and export technological complexity of enterprises ( Lei & Lang, 2020 ; Lu & Jin, 2020 ).

From the perspective of the competition effect and enterprise productivity, improved trade facilitation encourages more enterprises to engage in multilateral trade, leading to a rapid increase in the number of enterprises within the same industry. According to market competition theory, more enterprises lead to fiercer market competition. To cope with this competition, enterprises import more advanced machinery and equipment to gain technical advantages from capital elements. Additionally, enterprises may increase research and development (R&D) activities and expenditures to consolidate or expand market share, thus raising competitors’ entry thresholds within the same industry and increasing export technological complexity ( Zhao, 2020 ). Mayer’s empirical model suggests that improved trade facilitation intensifies the market competition environment in product-producing countries, reducing enterprise cost markups. Multi-product producers decrease the production of low-productivity products and concentrate on core products with high productivity, resulting in a “skew effect” within the enterprise. By allocating more resources to core products with higher productivity and optimizing resource allocation within the enterprise, overall enterprise productivity can be effectively enhanced, thereby increasing export technological complexity ( Mayer et al., 2014 ).

Based on the above analysis, this paper proposes Research Hypothesis 2:

Hypothesis 2: Improvement in trade facilitation will positively affect the export technological complexity of enterprises through cost effects, technology effects, competition effects, and improvements in firm productivity.

Based on the above theories and assumptions, to explore the ways to maximize the improvement of export technological complexity of manufacturing enterprises, this paper constructs the following models (such as Equations (1) and (2)) to empirically test the relationship between trade facilitation and export technological complexity of manufacturing enterprises.

$CET{C}_{ikjt}={\alpha }_0+{\alpha }_{1}T{f}_{ikjt}+{\delta }_m{X}_{ikjt}+{\mu }_f+{\mu }_t+{\epsilon }_{ikjt}$ (1)

$\begin{array}{c}CET{C}_{ikjt}={\beta }_0+{\beta }_{1}Infr{a}_{ikjt}+{\beta }_{2}Por{t}_{ikjt}+{\beta }_{3}Inst{i}_{ikjt}+{\beta }_{4}Ele{c}_{ikjt}\\ +{\beta }_{5}Marke{t}_{ikjt}+{\delta }_m{X}_{ikjt}+{\mu }_f+{\mu }_t+{\epsilon }_{ikjt}\end{array}$ (2)

The above formula indicates as follows: $i$ represents the region in which the enterprise operates, $k$ denotes the industry identifier for the enterprise, $j$ stands for individual enterprises, and $t$ represents time. ${\alpha }_1$ is the regression coefficient for trade facilitation, ${\beta }_1~{\beta }_5$ is the regression coefficients for various indicators of trade facilitation, ${\alpha }_0,{\beta }_0$ is the constant term, $X$ denotes control variables, ${\delta }_m$ represents the regression coefficients of control variables, ${\mu }_f$ and ${\mu }_t$ are enterprise fixed effects and time fixed effects respectively, and ${\epsilon }_{ikjt}$ is the random disturbance term.

The measurement data of trade facilitation are obtained from various sources including provincial statistical yearbooks, China Port Yearbook, China Marketization Index Report, China Internet Network Information Center, and State Intellectual Property Office Statistical Yearbook. The reciprocal distance between the enterprise and the nearest port is calculated by the author using AutoNavi map API and geocoding, utilizing geographical calculation.

The data for export technological complexity were sourced from the China Industrial Statistics Yearbook, China Economic Census Yearbook, and customs database. Enterprise patent data were obtained from the CPDP (Chinese Patent Data Project) and the patent database of the State Intellectual Property and Patent Office of China.

The measurement data of control variables and intermediary variables were derived from the China Industrial Enterprise Database and customs database. Descriptive statistics of the data are presented in Table 1 .

1) Trade facilitation

This paper adopts the multi-field scoring method to assess trade facilitation across various provinces. Existing studies have indicated that the development disparity between northern and southern regions of China has shifted from differences in natural environments to disparities in the extent of market-oriented development. Therefore, building upon the research by Duan et al. (2020) , this paper selects infrastructure, port efficiency, institutional environment, e-commerce, and market-oriented processes as the measurement indicators for trade facilitation. Subsequently, proxy variables or secondary indicators are utilized to score each indicator individually, followed by standardization processing to gauge the level of trade facilitation in each province. The standardization formula is presented in Equation (3):

$z_{dt}=\frac{X_{dt}-\min X_{dt}}{\max X_{dt}-\min X_{dt}}\times 10$ (3)

In which $X_{dt}$ stands for the standardized object, and $Z_{dt}$ represents the standardized index. The measurement variables and data sources for each indicator are detailed in Table 2 .

Based on measuring the level of provincial trade facilitation, this paper utilizes the AutoNavi map API and geocoding to analyze the longitude and latitude of office addresses of enterprises. This is done after matching China’s industrial enterprise database, customs database, and patent database from 2004 to 2013. The reciprocal distance of enterprises from the nearest port is then obtained through geographical calculation. The trade facilitation index of each enterprise is calculated using this distance as the weight. The calculation formula is shown in Equation (4):

$T{f}_{ikjt}=T{f}_{ikjt}\times 1/Di{s}_{si}$ (4)

$T{f}_{ikjt}$ represents the level of trade facilitation for enterprise $j$ in industry $k$ in region $i$ in year $t$, $T{f}_{jt}$ represents the level of trade facilitation for province $j$ in year $t$, and $Di{s}_{si}$ represents the distance of enterprise $i$ to its nearest port $s$.

2) Technological complexity of exports

Regarding the measurement of export technological complexity, the two-step method proposed by Hausmann et al. (2007) has been widely used in most studies to measure the technological complexity of exports at the national or regional level. Since the quality of products exported by different countries may vary greatly, most existing studies refer to the research results of Xu (2010) to introduce a relative price index to adjust the export technological complexity of different countries. However, the differences in the quality of export products in different regions of the same country are usually small. Therefore, this paper uses Hausmann’s two-step method for reference ( Lei & Lang, 2020 ) to measure the export technological complexity of different provinces. The first step is to measure the technological complexity of exports of manufacturing enterprises at the industry level. The measurement formula is shown as Equation (5):

$IET{C}_{kt}={\displaystyle \underset{i}{\sum }\frac{x_{ikt}/X_{it}}{{\displaystyle \underset{i}{\sum }\left(x_{ikt}/X_{it}\right)}}}\times Y_{it}$ (5)

where $IET{C}_{kt}$ represents the export technological complexity of industry $k$ in year $t$, $x_{ikt}$ represents the total exports of industry $k$ in region $i$ in year $t$, $X_{it}$ represents the total exports of all manufacturing industries in region $i$ in year $t$, and $Y_{it}$ is the per capita GDP of region $i$ in year $t$. In the second step, based on formula (5), the proportion of exports of various industries in the total exports of all manufacturing industries is calculated. This proportion is then used as a weight to calculate the export technological complexity of manufacturing enterprises at the provincial level. The calculation formula is as follows:

$PET{C}_{it}={\displaystyle \underset{k}{\sum }\frac{x_{ikt}}{X_{it}}}\times IET{C}_{kt}$ (6)

where $PET{C}_{it}$ represents the export technological complexity of region $i$ in year $t$. Since the number of patents applied by enterprises can reflect the innovation ability and R&D investment level of enterprises to a large extent, generally, the more patents applied by enterprises, the higher the export technological complexity of enterprises in the industry. Therefore, this paper uses the ratio of the number of patents applied by enterprises to the average number of patents applied by enterprises in the industry as the weight to measure the export technological complexity at the enterprise level. The specific measurement formula is shown in Equation (7):

$CET{C}_{ikjt}=\frac{Pa{t}_{ikjt}}{{\displaystyle \underset{j}{\sum }Pa{t}_{kjt}/n}}\times PET{C}_{it}$ (7)

where $CET{C}_{ijkt}$ represents the export technological complexity of enterprises $j$ in industry $k$ in region $i$ in year $t$, $Pa{t}_{ikjt}$ represents the number of patents obtained by enterprises $j$ in industry $k$ in region $i$ in year $t$, $Pa{t}_{kjt}$ represents the number of patents obtained by enterprises $j$ in industry $k$ in year $t$, and $n$ is the number of enterprises in the corresponding industry.

According to the above determined measurement method, this paper measures the trade facilitation and export technological complexity of enterprises by matching the Chinese industrial enterprise database, customs database, and enterprise patent database from 2004 to 2013. The relevant typical facts are as follows:

1) Both trade facilitation and export technological complexity are optimized to varying degrees

Observing Figure 1 , it is evident that from 2004 to 2013, the enterprise trade facilitation level exhibits several characteristics: Firstly, the peak of the kernel density plot for enterprise trade facilitation consistently shifts towards the right. This indicates continuous improvement in the trade facilitation level of China’s manufacturing enterprises and ongoing optimization of their export environment. Secondly, the kernel density plot for enterprise trade facilitation transitions from a “wide and short peak” to a “high and narrow peak”, suggesting a narrowing gap in trade facilitation among enterprises.

Turning to Figure 2 , the export technological complexity of China’s manufacturing enterprises during 2004-2013 demonstrates the following features: Firstly, the peak of the kernel density plot for export technological complexity steadily moves towards the right, indicating a yearly increase in export technological complexity and ongoing optimization of manufacturing enterprises’ export structure. Secondly, the kernel density plot for export technological complexity evolves from a “high and narrow peak” to a “wide and low peak”, indicating an expanding gap in export technological complexity among manufacturing enterprises. Thirdly, both left-tail skewness and right-tail skewness of the kernel density plot for export technological complexity are increasing, suggesting a growing polarization in export technological complexity among manufacturing enterprises. This implies an increase in the number of manufacturing enterprises with consistently low export technological complexity, as well as those with consistently high export technological complexity, acting as “leaders”.

2) Trade facilitation and export technological complexity are demonstrable correlation

To illustrate the relationship between trade facilitation and export technological complexity from a macro perspective and provide a basis for constructing the empirical model, this paper presents a scatter plot of trade facilitation and export technological complexity ( Figure 3 ). As depicted in Figure 3 , trade facilitation and export technological complexity exhibit a positive correlation, with no nonlinear relationship apparent.

According to the research objectives of this paper, the export technological complexity of enterprises (CETC) is selected as the dependent variable. The independent variables include trade facilitation of enterprises (Tf) and various domain indicators of trade facilitation. These domain indicators encompass infrastructure (Infra), port efficiency (Port), institutional environment (Insti), e-commerce (Elec), and marketization process (Market). The values of enterprise-level trade facilitation domain indicators are represented by the reciprocal of the distance of enterprises to the nearest port multiplied by the provincial-level trade facilitation domain indicators. Both the dependent and independent variables are transformed into logarithmic values to eliminate dimensional differences.

Regarding control variables, this paper adopts the control variables selected by Gao and Yuan (2020) , including enterprise size (Size), measured by the logarithm of the number of employees at the end of the year; enterprise age (Age), measured by the logarithm of the difference between the observation year and the year of establishment plus one; enterprise total factor productivity (Otfp), measured by the results of the OP method; government subsidies received by enterprises (Gsub), measured by the ratio of government subsidies received by enterprises in the observation year to the main business income of enterprises; and financing constraints (Fcs), measured by the logarithm of the ratio of interest expenses to total assets plus one.

Selection of Mediating Variables:

In order to empirically test the indirect effect of trade facilitation on the improvement of export technological complexity of manufacturing enterprises, this paper selects the following indicators as mediating variables based on the research results of Liu and Wang (2016) and others. The proportion of enterprise sales expenses to main business sales revenue (Cost) is used as the mediating variable for the cost reduction effect. The logarithm of enterprise research and development investment (Input) serves as the mediating variable for enterprise technological innovation effect. The Herfindahl-Hirschman Index (HHI) is calculated, and the proportion (Ics) of a single company’s main business income to the total main business income of all companies in the same industry is used as the mediating variable for enterprise competitive effect. Enterprise total factor productivity (Ltfp) is chosen as the mediating variable for enterprise productivity. Since the enterprise total factor productivity measured by the OP method is already included in the control variables, the measurement results of enterprise total factor productivity using the LP method are utilized to measure the mediating variable of enterprise total factor productivity.

1) Benchmark regression results

The corresponding LR test, F test, and Hausman test were conducted in this study, all of which rejected the null hypothesis of mixed regression at a significance level of 1%. Therefore, the fixed effects model was employed to estimate the model. The results of the baseline regression are presented in Table 3 . Overall, across all models, trade facilitation consistently exhibits a positive effect on the export technological complexity of manufacturing enterprises, with a relatively stable effect magnitude. Specifically, the analysis of regression results in column (6) indicates a positive impact of trade facilitation on export technological complexity. A 1% increase in trade facilitation is associated with a 0.0751% increase in export technological complexity of manufacturing enterprises, which is statistically significant at the 1% level.

The above-mentioned tests confirm that the level of trade facilitation can promote the increase of export technological complexity of manufacturing enterprises, so hypothesis 1 is verified.

Regarding the control variables, both firm size and firm total factor productivity demonstrate a significant positive impact on the technological complexity of manufacturing exports. This can be attributed to the larger size and higher productivity of firms, which entail greater resource abundance, enhanced production efficiency, and increased availability of capital for research and development (R&D) and innovation activities. Consequently, these factors positively influence the export technological complexity of manufacturing enterprises. However, the impact of enterprise survival years on export technological complexity is only significant at the 10% level. Government subsidies and financing constraints exert a diminishing effect on the export technological complexity of manufacturing enterprises. Government subsidies may induce complacency within enterprises, diminishing their motivation for product innovation and technological complexity enhancement. Conversely, financing constraints may curtail enterprises’ investment in R&D funds, thereby impeding improvements in export technological complexity of manufacturing enterprises.

Note: The t value is in parentheses, * where, **, and *** are significant at 0.1, 0.05, and 0.01 levels respectively. The following text is consistent with this.

2) Heterogeneity analysis

The impact of trade facilitation on the export technological complexity of manufacturing enterprises may vary significantly depending on factors such as ownership type, factor intensity, and geographical location. Therefore, this paper examines the heterogeneity among manufacturing enterprises with different ownership types, factor intensities, and geographical locations.

a) Enterprises of different ownership types

Manufacturing enterprises of different ownership types encounter distinct differences in institutional environments, government support, and resource availability. Accordingly, this paper categorizes manufacturing enterprises into state-owned enterprises, foreign-funded enterprises, and private enterprises, exploring the effects of trade facilitation on the export technological complexity within each ownership category. Table 4 presents the regression results of trade facilitation on the export technological complexity of manufacturing enterprises across different ownership types.

According to the regression findings, the positive impact of trade facilitation on the export technological complexity is most pronounced for private manufacturing enterprises, followed by foreign-funded enterprises. In contrast, the effect on the export technological complexity of state-owned manufacturing enterprises is comparatively smaller and statistically insignificant. This difference may primarily stem from the fact that state-owned enterprises, lacking incentives for technological and institutional innovation compared to non-state-owned ownership types ( Li & Li, 2024 ), experience slower improvements in export technological complexity.

b) Enterprises with different factor intensities

Enterprises with different factor intensities may exhibit significant differences in product positioning, strategic orientation of export products, and the proportion of R&D investment. To address this, the paper divides the sample enterprises into capital-intensive and labor-intensive enterprises, respectively discussing the impact of trade facilitation on the export technological complexity under these two factor intensity conditions. The corresponding regression results are presented in Table 5 .

According to the regression findings, trade facilitation positively influences the export technological complexity of capital-intensive manufacturing enterprises, with the effect being statistically significant at the 1% level. However, the promotional effect on the export technological complexity of labor-intensive manufacturing enterprises is not statistically significant. This may be attributed to the lower technical level of products produced by labor-intensive enterprises, resulting in slower technology updates and reduced sensitivity to changes in trade facilitation.

c) Enterprises in different geographical locations

Due to significant variations in the level of trade facilitation across the eastern, central, and western regions of China, there may exist regional disparities in the impact of trade facilitation on enhancing the export technological complexity of manufacturing enterprises. To address this, the study categorizes manufacturing enterprises into those situated in the eastern, central, and western regions, respectively, and examines the influence of trade facilitation enhancements on the export technological complexity within each region. The regression outcomes are presented in Table 6 . The results indicate that the enhancement of trade facilitation exerts the most pronounced impact on elevating the export technological complexity of manufacturing enterprises in western China, followed by those in central China, while enterprises in eastern China exhibit the least significant effect.

3) Robustness test

To ensure the robustness of the research findings, this study conducts several robustness tests on the relationship between trade facilitation and the export technological complexity of manufacturing enterprises.

a) Substitution of Trade Facilitation Index

This paper substitutes the product market development score from the Fan Gang Marketization Index as the proxy variable for provincial trade facilitation. The trade facilitation level at the enterprise level is then calculated based on the reciprocal distance between each enterprise and the nearest port for regression analysis. The regression outcomes are presented in the first column of Table 7 , demonstrating that the positive impact of trade facilitation on the export technological complexity of manufacturing enterprises remains significant.

b) Substitution of Export Technological Complexity Index

Recognizing that product quality often reflects the technical proficiency of an enterprise, this paper substitutes the product quality of enterprises as a proxy variable for the technological complexity of manufacturing export in the regression analysis. The product quality of manufacturing enterprises is quantified using the “demand residual method” introduced by Shi Bingzhan and Shao Wenbo. The results, depicted in the second column of Table 7 , continue to exhibit significance in the regression outcomes.

c) Re-measurement of Export Technological Complexity

To further validate the regression findings, this study adopts the approach proposed by Yu and Yu (2018) to re-measure the export technological complexity of manufacturing enterprises. This method involves utilizing the ratio of total factor productivity (measured via the LP method) to the mean enterprise rate of the respective industry as the weight. The newly computed export technological complexity of manufacturing enterprises is then incorporated into the regression model for analysis. The results, displayed in the third column of Table 7 , reaffirm the positive impact of trade facilitation on the export technological complexity of manufacturing enterprises.

d) Regression Analysis Based on Different Patent Types

Moreover, this study employs the ratio of the number of patents of various types held by enterprises to the average number of corresponding patent types within the same industry as the weight. This measure is utilized to assess the export technological complexity of manufacturing enterprises across different patent categories, serving as the dependent variable for regression analysis. The regression outcomes are presented in Table 8 , with columns (1) through (3) depicting the regression results for practical patents, design patents, and invention patents, respectively.

The findings reveal that trade facilitation exerts a positive influence on the export technological complexity of manufacturing enterprises across various patent types. Notably, the most pronounced effect is observed for practical patents, followed by design patents, whereas invention patents exhibit the least impact.

4) Mediation effect test

Based on the findings from the mechanism analysis, it is evident that trade facilitation not only directly enhances the export technological complexity of manufacturing enterprises but also indirectly contributes to this enhancement through several channels, including cost reduction, technological innovation, competition, and enhancement of total factor productivity. Therefore, following the mediation effect test method proposed by Sobel (1982) , this paper develops an intermediary effect model (such as Equations (8)-(11)) to examine the mediating effects.

$CET{C}_{ikjt}={\alpha }_0+{\alpha }_{1}T{f}_{ikjt}+{\delta }_m{X}_{ikjt}+{\mu }_f+{\mu }_t+{\epsilon }_{ikjt}$(8)

$CEC{T}_{ikjt}={\gamma }_0+{\gamma }_n{M}_{ikjt}+{\delta }_m{X}_{ikjt}+{\mu }_f+{\mu }_t+{\epsilon }_{ikjt}$ (9)

$M_{ikjt}={\sigma }_0+{\sigma }_{n}T{f}_{ikjt}+{\delta }_m{X}_{ikjt}+{\mu }_f+{\mu }_t+{\epsilon }_{ikjt}$ (10)

$CET{C}_{ikjt}={\lambda }_0+{\lambda }_{1}T{f}_{ikjt}+{\lambda }_n{M}_{ikjt}+{\delta }_m{X}_{ikjt}+{\mu }_f+{\mu }_t+{\epsilon }_{ikjt}$ (11)

In the equation above, $i$ represents the region where the enterprise is located, $k$ denotes the industry in which the enterprise operates, $j$ stands for individual enterprises, and $t$ represents the observation year. ${\gamma }_0$, ${\sigma }_0$ and ${\lambda }_0$ are constant terms, ${\alpha }_1$, ${\sigma }_n$ and ${\lambda }_1$ are the regression coefficients for trade facilitation, $M$ is the mediating variable, $X$ represents control variables, ${\gamma }_n$ and ${\lambda }_n$ are the regression coefficients for the mediating variables, ${\delta }_m$ denotes the regression coefficients for the control variables, ${\mu }_f$ and ${\mu }_t$ are enterprise fixed effects and time fixed effects respectively, and ${\epsilon }_{ikjt}$ is the random disturbance term. The conditions for testing the mediating effect model are as follows:

(a) The promotion effect of trade facilitation on the export technological complexity of manufacturing enterprises is significant, i.e., the coefficient ${\alpha }_1$ in the expected Equation (8) is greater than 0. (b) The promotion effect of the mediating variable on the export technological complexity of manufacturing enterprises is significant, i.e., the coefficient ${\gamma }_n$ in the expected Equation (9) is greater than 0. (c) The promotion effect of trade facilitation on the mediating variable is significant, i.e., the coefficient ${\sigma }_n$ in the expected Equation (10) is greater than 0. (d) After simultaneously including trade facilitation and the mediating variable, the regression coefficient ${\lambda }_1$ for trade facilitation is no longer significant, or its significance decreases. In other words, the coefficient ${\lambda }_1$ in Equation (11) is smaller than the coefficient ${\alpha }_1$ for trade facilitation in Equation (8).

As shown in Table 9 , the results of the mediation effect regression are presented. Upon observing the regression results in the first column, it is evident that trade facilitation positively promotes the export technological complexity of manufacturing enterprises, meeting condition (a). Examination of the regression results in the second column reveals that, except for the competition effect, all other intermediary variables significantly impact the export technological complexity of manufacturing enterprises at the 1% level, satisfying condition (b). Analysis of columns three through six indicates that, aside from the technological innovation effect, trade facilitation significantly affects other intermediary variables at the 1% level, meeting condition (c). Observing the results in the seventh column, it is noted that upon including trade facilitation and all intermediary variables, the regression coefficient of trade facilitation significantly decreases and is only significant at the 10% level, fulfilling condition (d).

In summary, trade facilitation can positively promote the export technological complexity of manufacturing enterprises through cost reduction, technological innovation, competition, and enhanced enterprise productivity, thus supporting hypothesis 2.

4) Further Research: Analysis of Trade Facilitation and Regional-Industry Export Technological Complexity

To ensure the completeness of the study, this paper conducts further research on the relationship between trade facilitation and export technological complexity at the regional-industry level.

First, this paper calculates the regional-industry export technological complexity based on the enterprise-level export technological complexity. The specific calculation formula is shown in Equation (12):

$CET{C}_{ikt}={\displaystyle \underset{j\in {\Omega }_{ik}}{\sum }{\rho }_{jt}CET{C}_{ikjt}}$ (12)

In the equation, $i$ represents regions, $k$ represents industries, $t$ represents time, and $j$represents enterprises. ${\Omega }_{ik}$ denotes the collection of enterprises $j$ belonging to region $i$ and industry $k$. $CET{C}_{ikj}$ represents the export technological complexity of region $i$ and industry $k$. ${\rho }_{it}$ denotes the aggregation weight, calculated using the proportion of export value of a company in the customs database to the total export value of companies in the region and industry. $CET{C}_{ijkt}$ represents the export technological complexity at the enterprise level.

Furthermore, this paper draws on the analysis method of Griliches and Regev (1995) to decompose the export technological complexity at the regional-industry level into four components: within-firm effect, between-firm effect, entrants effect and exiters effect. The within-firm effect refers to the impact of changes in export technological complexity at the region-industry level resulting from changes in the export technological complexity of the enterprise itself. The between-firm effect refers to the impact of changes in the export share of enterprises on the export technological complexity at the region-industry level. The entrants effect pertains to the impact of changes in export technological complexity at the region-industry level following the entry of new firms. The exiters effect refers to the impact on export technological complexity at the region-industry level following the exit of enterprises from the market. The combination of between-firm effect, entrants effect and exiters effect is commonly referred to as the resource reallocation effect.

First, calculate the change in export technological complexity at the regional-industry level from period $t$ to period $t-1$, as shown in Equation (13):

$\Delta CET{C}_{ikt}={\displaystyle \underset{j\in \left(L,M\right)}{\sum }{\rho }_{jt}CET{C}_{ikjt}-}{\displaystyle \underset{j\in \left(L,N\right)}{\sum }{\rho }_{jt}CET{C}_{ikjt}}$ (13)

Then, further decompose the change in export technological complexity, as shown in Equation (14), where $L,M$ and $N$ represent the sets of existing enterprises, entering enterprises, and exiting enterprises, respectively.

$\begin{array}{c}\Delta CET{C}_{ikt}=\underset{\text{within-firm}\text{effect}}{\underset{︸}{{\displaystyle \underset{j\in L}{\sum }{\bar{\rho }}_j\Delta CET{C}_{ikjt}}}}+\underset{\text{between-firm}\text{effect}}{\underset{︸}{{\displaystyle \underset{j\in L}{\sum }\Delta {\rho }_{jt}\left(\overline{CET{C}_j}-CET{C}_{iM}\right)}}}\\ +\underset{\text{entrants}\text{effect}}{\underset{︸}{{\displaystyle \underset{j\in M}{\sum }{\rho }_{jt}\left(CET{C}_{ikjt}-\overline{CET{C}_{iM}}\right)}}}\\ -\underset{\text{exiters}\text{effect}}{\underset{︸}{{\displaystyle \underset{j\in N}{\sum }{\rho }_{jt-i}\left(CET{C}_{ikjt-1}-\overline{CET{C}_{iM}}\right)}}}\end{array}$ (14)

where, the underlined variable represents the mean value of the variable. When the export technological complexity of a newly entering enterprise exceeds the average export technological complexity of existing enterprises in the region and industry, it is considered that the entry effect of the enterprise is positive. Similarly, when the export technological complexity of an exiting enterprise falls below the average export technological complexity of enterprises in the region and industry, the exiters effect of the enterprise is considered positive.

To investigate whether the resource reallocation effect is the mechanism for improving export technological complexity at the region-industry level, this paper constructs the following model, as shown in Equation (15):

$CET{C}_{ikt}={\alpha }_0+{\alpha }_{1}t{f}_{ikt}+{\delta }_m{X}_{ikt}+{\mu }_t+{\mu }_i+{\mu }_k+{\epsilon }_{ikt}$ (15)

In Equation (15), $CET{C}_{ikjt}$ represents the impact of changes in export technological complexity at the regional-industry level due to resource reallocation effects and its various decomposition components. $t{f}_{ikt}$ denotes the level of trade facilitation at the regional-industry level, $X_{ikt}$ represents control variables, derived from enterprise-level control variables aggregated by regional-industry. ${\mu }_i$, ${\mu }_t$ and ${\mu }_k$ respectively denote regional, time, and industry fixed effects, while ${\epsilon }_{ikt}$ represents the random disturbance term.

The regression results are shown in Table 10 . The observed results show that the resource reallocation effect is an important way for trade facilitation to promote the improvement of export technological complexity of manufacturing enterprises. The effects of trade facilitation on intra-firm effect, inter-firm effect and firm entry effect of manufacturing enterprises are significantly positive, while the effects on exiters are significantly negative, indicating that trade facilitation is conducive to promoting the exit of enterprises with low technological complexity and the entry of enterprises with high technological complexity, so as to realize the transfer of resources from enterprises with low technological complexity to enterprises with high technological complexity.