The Impact of Factor Price Distortions on Export Technology Complexity: Evidence from China

Abstract

Increasing export technology complexity could effectively enhance export competitiveness. High-tech exports generally show lower resource consumption and environmental pollution, thus promoting sustainable economic development. However, immature factor markets could lead to factor price distortions. In fact, factor price distortions hinder improvements in export technology complexity. Thus, this study measures the degree of factor price distortions in various regions of China. Empirical methods such as regression model analysis and heterogeneity analysis are used. We reveal the mechanism of how factor price distortions affect export technology complexity. The conclusions are as follows: (1) Factor price distortions suppress the enhancement of export technology complexity. As the degree of factor price distortions increases, export technology complexity decreases. (2) Factor price distortions show significant regional heterogeneity in the suppression of export technology complexity. The impact gradually decreases from west to east. (3) Factor price distortions could hinder improvements in export technology complexity by weakening the positive effects of the FDI and trade openness. However, with the continuous advancements in market-oriented reforms, this inhibitory effect will gradually diminish. Studying the impact of factor price distortions on the sophistication of export technology significantly enhances economic competitiveness. It also improves resource allocation and further promotes the sustainability of economic development and green development. Furthermore, the logic and principles behind the impact of factor price distortions on export technology complexity can provide valuable insights for our consideration of sustainability in the workplace.

1. Introduction

China is the world’s largest merchandise trading nation. According to the statistics of the General Administration of Customs of China, China’s trade volume of goods in 2023 amounted to USD 59,368 million. At the same time, China’s total export volume reached USD 33,800 million. The country’s trade surplus continues to expand. Furthermore, it exerts a significant influence on the global trade landscape. However, compared to developed countries, China remains at the lower end of the global industrial chain’s “smile curve”. China often engages in primary processing stages with low technological content and limited value added. Enhancing export technology complexity is a crucial element in driving trade transformation and upgrading [1]. Simultaneously, as trade scales up, China’s export product structure continues to optimize and upgrade. However, the majority of China’s exported products in foreign merchandise trade are labor-intensive and resource-intensive. China has made significant progress in technological innovation. However, there remains a considerable gap compared to advanced international levels. Particularly in high-tech fields, China is relatively weak and heavily reliant on imports. On one hand, this hinders the export of high-value-added products. On the other hand, this positions the country poorly in the international high-end product market [2]. Thus, China urgently needs to enhance the technological content and value-added level of its export products. An in-depth exploration into the impact of factor price distortions on export technology sophistication could assist in identifying these distortions. The rectification of factor price distortions promotes the flow of resources towards high-tech and high-value-added sectors. Consequently, the technological content and added value of export products would be enhanced. It would strengthen the country’s position within the global value chain. Moreover, analogous to resource allocation in factor markets, the resources within a workplace also require efficient allocation. These resources include human, material, and financial resources. If the prices (or costs) of these resources are distorted, resource waste or inefficient utilization may occur. Thus, the sustainability and efficiency of work operations are affected.

Many scholars have conducted extensive research on the factors that influence export technology complexity. The literature indicates the driving forces behind the evolution of export technology complexity. In developed economies, the factors include economic growth [3], higher technological levels, and the quality of human capital [4]. In developing countries, technology transfer, foreign investment [5], and export growth are crucial means to enhance export technology complexity.

Many researchers have explored the factors influencing export technology complexity from various perspectives in China. China’s export technology complexity has significantly improved, especially in the expors of machinery and vehicles. However, the regional “heterogeneity” in the export technology complexity among provincial units in China is becoming increasingly apparent. Recent studies by Chinese scholars found a means to enhance export technology complexity. Trade facilitation [6], free trade zone development [7], innovative city policies [8], green finance [9], the digital economy [10], and environmental regulations [11] all show a positive impact on enhancing China’s export technology complexity. Conversely, market segmentation [12] and participation in global value chains [13] hinder China’s export technology complexity.

The above research studies thoroughly explored the factors influencing export technology complexity from different perspectives. Furthermore, export technology complexity is heavily influenced by the market environment. The development of factor markets is a crucial factor affecting regional export technology complexity. The developed factor markets not only facilitate the free flow of factors but also ensure the efficient supply of specialized factors. However, imperfect factor markets often lead to resource misallocation and factor price distortions. Factor price distortions are critical concepts within factor market distortions. Market mechanisms have imperfections or are hindered by institutional arrangements. As a result, the actual prices of production factors like land, capital, and labor deviate from the equilibrium prices that would be set by supply and demand in a perfectly competitive market. This deviation leads to the inability of production factor prices to accurately reflect their scarcity value and opportunity costs. This also affects the efficient allocation of resources and overall economic efficiency. This phenomenon commonly occurs in developing countries with nascent market development. To catch up with developed nations, many countries resort to government intervention and subsidies. This aims to reduce factor usage costs and drive rapid economic growth. This inclination leads companies to favor traditional, lower-cost factors over higher-tech and innovation-driven factors [14]. Such distortions not only dampen incentives for independent innovation but also impede advancements in export product technology.

An in-depth exploration into the impact of factor price distortions on export technology sophistication could assist in identifying and rectifying these distortions. The rectification of factor price distortions promotes the flow of resources towards high-tech and high-value-added sectors. Consequently, the technological content and added value of export products would be enhanced, thereby strengthening country’s position within the global value chain.

We examine data from 30 Chinese provinces spanning from 2002 to 2022. Furthermore, we delve into the spatiotemporal evolution trends of factor price distortions and their impact on export technology complexity. We employ theoretical and empirical analyses. In addition, we dissect mechanism pathways through which factor price distortions affect export technology complexity. We aim to offer crucial insights for the relevant governmental bodies and businesses in formulating management decisions.

The purposes of this study are as follows: (1) We attempt to investigate the impact process of factor price distortions on export technology complexity. We first measure the current level of factor price distortions in China. Then, we delve into how factor price distortions affect export technology complexity. (2) We attempt to analyze the mediating roles of FDI and trade openness in the impact process of factor price distortions on export technology complexity. Both FDI and trade openness play crucial mediating roles. We also aim to elucidate the specific contributions of FDI and trade openness in this process. (3) We attempt to explore the moderating role of marketization in the relationship between factor price distortions and export technology complexity. We seek to analyze the direction and magnitude of marketization’s moderating effect. (4) We attempt to uncover the differential impacts of factor price distortions on export technology complexity across different regions in China. Policies and levels of factor price distortions vary between coastal and non-coastal regions in China. In this paper, we aim to reveal the differentiated impacts of factor price distortions on export technology complexity in the coastal and non-coastal regions of China.

The marginal contributions of this paper are as follows: (1) We enhance our understanding of the mechanism behind the impact of export technology complexity. This enriches the transmission mechanisms of how factor price distortions affect export technology complexity. Moreover, it supplements existing research. We calculate the overall factor price distortions and distortions in capital and labor factor prices. At the same time, we reveal their respective impact mechanisms on export technology complexity. Thus, this deepens our understanding of the reasons for the lower export technology complexity in China. Additionally, it provides crucial empirical evidence for the research fields of export technology complexity and the factor price distortions. (2) This paper enriches the research content on the impact of factor price distortions on export technology complexity. The existing literature mostly examines the impact of individual factors on export technology complexity. Moreover, previous studies pay less attention to the transmission mechanisms or regional differences of this impact. However, this paper analyzes how factor price distortions influence export technology complexity through FDI and trade openness. It not only enriches the research content of the existing literature but also presents new research findings. (3) This paper inspires new policy directions. The conclusions of this study indicate that factor price distortions in China show an inhibitory effect on export technology complexity. However, marketization, to some extent, mitigates the inhibitory effect of factor price distortions. Therefore, when formulating policies related to export technology complexity, governments need to consider the extent of distortions across different factors. In addition, governments also need to take into account disparities in marketization processes among regions.

The structure of this paper is as follows: (1) Literature review: This section reviews the relevant research literature on factor price distortions and export technology complexity. (2) Theoretical analysis and research hypotheses: Building on theoretical analysis of the relationships between variables, this section also presents research hypotheses. (3) Research designs: This section outlines the econometric model design, the variable measurements, and the data sources. (4) Results: This section includes main effect analysis, divide effect analysis, heterogeneity tests, robustness checks, endogeneity analysis, and mechanism tests. (5) Conclusion, implications, and research limitations: This section primarily covers conclusions, implications, research limitations, and future research directions.

2. Literature Review

2.1. Research on Factor Price Distortions

Factor price distortions refer to factor prices not equaling factor marginal costs [15]. These distortions could be categorized as endogenous distortions caused by imperfect markets and exogenous distortions resulting from improper government interventions. They could also be classified as absolute distortions and relative distortions, with absolute distortions further divided into positive and negative distortions. Relative to developed countries, factor price distortions are more prevalent in developing and transitioning nations [16]. Many studies indicate that the current factor price in China exhibits the characteristics of negative distortions. Additionally, capital price distortions are often more severe compared to labor prices [17].

We cannot deny the driving role of factor price distortions in economic development during specific periods. International experience suggests that distortions promote total factor productivity in the early stages. However, this promotion is gradually waning. In the long run, the misallocation of resources and efficiency losses caused by distortions outweigh their benefits to the economy [18]. Currently, market reforms primarily focus on commodity markets, with a high degree of marketization at 97.5%, while factor market reforms lag behind. Peters (2013) argued that improper resource allocation could alter firms’ R&D behavior and entry decisions. Moreover, the dynamic efficiency losses caused by the misallocation are four-times greater than static efficiency losses [19]. Thus, in order to optimize resource allocation and enhance economic efficiency, the process of marketization must be continuously advanced.

2.2. The Impact of Factor Price Distortions on Export Technology Complexity

Factor price distortions not only impact the macroeconomic environment but also show a chain effect on export technology complexity. Negative distortions in factor prices lead to a lower price effect. At the same time, the effect increases the scale of exported products. However, for export technology complexity, factor price distortions hinder their enhancement through a series of combined effects. Capital and labor misallocations caused by factor price distortions result in productivity losses. Furthermore, it may reduce firms’ output levels. Long-term distortions affect firms’ total factor productivity. They also further hinder the advancement of export technology complexity. Wang (2023) exposed how labor price distortions hinder China’s manufacturing industry [20]. This obstruction impacts export premium growth via R&D, resource allocation, and scale efficiency. Zong (2013) empirically showed a non-linear relationship between factor price distortions and export technology complexity [21]. In addition, they suppress the enhancement of export technology complexity by impeding FDI and R&D investments.

2.3. The Role of FDI and Trade Openness in Factor Price Distortions Affecting Export Technical Complexity

Whether in developed or developing countries, FDI shows a long-term positive impact on export technology complexity. At the same time, the higher the relative scale of FDI, the more pronounced its effect on enhancing export technology complexity. However, while attracting a large influx of FDI, factor price distortions diminish the quality of FDI. This point is also confirmed by Pi (2020) [22]. Factor price distortions lower the barriers to entry for FDI. Furthermore, they potentially lead to a significant influx in high-energy-consuming, low-tech “sunset industries” from abroad into China. On one hand, factor price distortions increase the total volume of FDI; on the other hand, these distortions simultaneously decrease the quality of FDI. Moreover, factor price distortions, by favoring capital-biased technology, inhibit the development of technology. Consequently, factor price distortions negatively impact export technology complexity by suppressing the positive effects of FDI. In other words, FDI plays an intermediary role in the process of factor price distortions affecting export technology complexity. In other words, factor price distortions hinder export technology complexity by inhibiting FDI.

Trade openness signifies the flow of advanced technology and management expertise. Particularly in the early stages of industrial development, it fosters the enhancement of export technology complexity. In addition, trade is an important way for economies to obtain international technology transfer [23]. Improvements in trade openness are conducive to technical imitation and manufacturing industry innovation in trade. Simultaneously, it enhances firms’ capabilities to produce high-tech products for export. With this rise in trade openness, the improvements in export technology complexity become more apparent [24]. However, factor price distortions partly inhibit the impacts of trade openness. In distorted factor price environments, firms may lean towards utilizing low-cost factors for simple production. However, they may lack the drive to enhance product technological content through innovation to meet international market demands. Finally, distortions would hamper the effectiveness of trade openness in boosting export technology complexity. Furthermore, factor price distortions may lead to industrial development deviations. Moreover, it would hinder the transformation and upgrading opportunities brought about by trade openness. Distortions prevent the effective utilization of international market feedback to enhance export technology complexity. Therefore, market defects are eliminated, and trade opening would promote the complexity of export technology [25]. In summary, we believe that there is a possibility that trade openness plays an intermediary role in the process of factor price distortions affecting export technology complexity. Moreover, factor price distortions have a negative impact on export technology complexity by inhibiting trade openness.

Furthermore, marketization also, to some extent, corrects factor price distortions. Sound institutions could gradually enhance the complexity of exported products [26]. The advancement of marketization could improve factor price distortions and show a positive impact on export technology complexity through various means. Firstly, marketization reforms promote the establishment of effective market pricing mechanisms to ensure prices reflect supply-and-demand relationships. Secondly, marketization could reduce government intervention and distortions. It also makes market resource allocation more efficient. To sum up, marketization reforms facilitate market self-regulation, improve resource misallocation, and alleviate the negative impact of factor price distortions on export [27]. During the process of exploring the negative impact of factor price distortions on export technology complexity, the degree of marketization, as a crucial moderating variable, plays a significant mitigating role. Specifically, as the degree of marketization rises, the decisive role of market mechanisms in allocating resources becomes more and more pronounced. This prominent role of market mechanisms helps to alleviate the extent of factor price distortions, which in turn weaken their inhibitory impact on export technology complexity. By promoting market competition and optimizing resource allocation, the process of marketization contributes to enhancing factor use efficiency. This enhancement, in turn, could partially offset the negative effects of factor price distortions, thereby creating more favorable conditions for upgrades in export technology complexity.

In conclusion, previous scholars have conducted research on factor price distortions and export technology complexity. They have laid an important foundation for our study. However, their research showed the following shortcomings: (1) Many studies focus on the impact of factors such as technological innovation and digital economy on export technology complexity. In a word, they overlook the fundamental influence of macro factor market conditions on export technology complexity. (2) Existing scholars’ research scopes are mostly limited to factor market distortions, with less emphasis on factor price distortions. It leads to research on the extent of distortions across the entire factor market, neglecting research on the distortions in individual factors. (3) Studies on the impact of factor price distortions on export technology complexity lack the consideration of regional heterogeneity. Therefore, it results in a certain degree of bias in the research. Thus, to address the shortcomings of scholars’ research, this study examines the impact process of factor price distortions on export technology complexity. We aim to provide valuable insights for enhancing export technology complexity.

3. Theoretical Analysis and Hypothesis

3.1. The Impact of Factor Price Distortions on Export Technology Complexity

The distortion and deviation of factor prices underscore a crucial problem: the prices assigned to production factors, encompassing labor and capital, are not an accurate reflection of their inherent scarcity. Moreover, these prices fail to accurately capture the associated opportunity costs and potential economic values of these factors, thereby misrepresenting their true worth. Consequently, this inaccurate pricing leads to a suboptimal allocation of resources. It means that resources are not being distributed in the most efficient manner possible, resulting in a decrease in overall economic efficiency. Additionally, the suboptimal allocation of resources and the resulting loss of economic efficiency have profound consequences. These consequences are particularly evident in the trade sector, where they play a significant role. Specifically, they can heavily influence export technology sophistication, which serves as a crucial indicator of a country’s export competitiveness and technological progress.

The impact of factor price distortions on export technology complexity is multifaceted. It contains direct effects and indirect effects through FDI and trade openness. Moreover, the direct effects can be divided into positive and negative aspects: (1) Positive impacts: Factors often experience negative distortions where the actual prices are lower than the marginal output. Eventually, this directly reduces the factor usage costs. It allows the companies to allocate surplus funds to internal R&D and technological innovation. In addition, it facilitates the enhancement of export technology complexity [28]. (2) Negative impacts: While the negative factor price distortions save costs, they severely affect resource allocation efficiency. As the economy evolves, the efficiency losses worsen, becoming a “stumbling block” that hinders the industrial transformation and upgrading.

Firstly, factor price distortions may lead to imbalances in resource allocation. Companies tend to overuse cheap distorted factors, neglecting more efficient production methods and technologies. It also reduces innovation efficiency [29]. This imbalance hinders investment in technology R&D and upgrades, slowing the increase in export technology complexity. Secondly, factor price distortions disrupt coordination among industries, hindering the formation of robust industry chains and clusters. It impedes technology diffusion and limits the overall enhancements in the technological content of export products [30], for instance, the uneven development in upstream and downstream industries, missing key technological links, and export of high-complexity products. Factor price distortions would lead to the unbalanced development of upstream and downstream industries. It also would lead to the absence of key production technologies in industry and ultimately hinder the production and export of high-complexity products. Factor price distortions could also misalign talent flow with high-tech development. Additionally, it would result in a shortage of skilled personnel in high-tech industries. Furthermore, this may hinder the advancement towards higher technological complexity in exported products. Lastly, prolonged factor price distortions may lead domestic companies to adapt to distorted environments. Moreover, factor price distortions may make it challenging to compete internationally with products of higher technological complexity. Finally, distortions could hamper the enhancement of export technology complexity. In some developing countries, persistent factor price distortions have fostered reliance on low-cost factors. This also affects improvements in export technology complexity.

To sum up, negative factor price distortions may lead to cost savings, thereby benefiting the enhancement of export technology complexity. However, factor price distortions would result in allocation efficiency losses. It also causes a series of adverse effects on improving export technology complexity. The direct impact of factor price distortions on export technology complexity depends on the combined effects of positive and negative aspects. As the economy evolves, the cost-saving effects of factor price distortions are diminishing, while the efficiency losses in resource allocation are growing stronger. Based on the above analysis, this study proposes the following hypothesis:

H1: 

Factor price distortions show a negative impact on export technology complexity.

3.2. The Indirect Impacts of Factor Price Distortions on Export Technology Complexity

In addition to the aforementioned direct effects, factor price distortions also indirectly impact firms’ export technology complexity by inhibiting FDI and trade openness.

(1) Due to technology spillover effects, FDI could promote technological progress in the investment region. In addition, FDI could reduce research and development costs for technology adoption. Moreover, it would enhance the technological equipment and processes of local enterprises. Thereby, this also increases the technological complexity of a region’s export products [31]. However, local governments attract more FDI at low prices by manipulating the allocation and pricing of factor resources. This behavior may not effectively improve export technology complexity. Factor price distortions, with lower costs of capital and labor, attract more labor-intensive and capital-intensive FDI. These distortions potentially lead to FDI concentration in capital- and labor-intensive industries in China. Moreover, FDI inflows into the host country may not possess high technological levels, resulting in limited technology spillovers [32]. (2) The cost advantages generated by early-stage factor price distortions do facilitate China’s export scale and enhance its competitiveness in international markets through lower prices. However, the current international market places more emphasis on product quality and customer satisfaction. In a distorted factor price environment, companies may lean towards using low-cost factors for simple production. The drive to innovate and enhance product technological content to meet international market demands is lacking. This hampers the effectiveness of trade openness in promoting the enhancement of export technology complexity. The adverse impact of factor price distortions on China’s trade openness is increasing [33]. Based on the above analysis, we propose the following hypotheses:

H2a: 

Factor price distortions negatively impact export technology complexity by inhibiting FDI.

H2b: 

Factor price distortions negatively impact export technology complexity by inhibiting trade openness.

3.3. Marketization Plays a Positive Regulatory Role

A higher degree of marketization typically accompanies more efficient resource allocation and improved market mechanisms. In this way, companies are more likely to allocate resources reasonably based on market demand and price signals. It helps alleviate the resource allocation imbalance caused by factor price distortions, thereby enhancing export technology complexity [34]. Moreover, an increase in marketization usually leads to more intense market competition and a more open market environment. This competition and openness help stimulate companies’ innovation drive, encouraging them to invest in technology research and enhance technological levels [35]. Therefore, a higher level of marketization could assist companies in better addressing the innovation drive deficiency that factor price distortions may bring about. Based on the above analysis, we propose the following hypothesis:

H3: 

Marketization positively moderates the inhibitory effect of factor price distortions on export technology complexity.

As illustrated in Figure 1, to enhance the validation of the theoretical framework, we intend to assimilate pertinent research data and adopt rigorous empirical methodologies. Specifically, we employ regression model analysis and heterogeneity analysis to meticulously examine the mechanistic pathway through which factor price distortions influence export technology complexity. Concurrently, we leverage the intermediary effect analysis to elucidate the mediating roles played by FDI and trade openness. Then, we examine the strength of the regulatory effect of marketization during factor price distortions affecting export technology complexity. Furthermore, we conduct endogeneity and robustness tests, serving as empirical safeguards to validate the proposed theoretical model relationships. By executing these comprehensive analytical procedures, we attempt to derive research conclusions that are both scientifically rigorous and precisely calibrated.

4. Research Designs

4.1. Variables

4.1.1. Explanatory Variable

The explanatory variable in this study is factor price distortions (Dist). There are many well-known measurement methods for factor price distortions. They include the production function method, the beyond logarithmic method, and the production frontier analysis method. However, the Cobb–Douglas function is widely used due to its simplicity in calculation [36,37]. Therefore, this paper employs the Cobb–Douglas production function method. In this paper, the C-D production function is used to calculate the output and the total output. On this basis, the marginal output and factor price distortion are estimated. Initially, we construct the following constant elasticity production function:

$$Y_{it}=A{K}_{it}^{\alpha }{L}_{it}^{\beta }$$

(1)

Y represents total output. A denotes technological level. α and β represent the output elasticity of capital and labor, respectively. Taking the logarithm of both sides of the equation yields:

$${LnY}_{it}=A+\alpha {LnK}_{it}+\beta {LnL}_{it}+{\epsilon }_{it}$$

(2)

Building upon the above equation, regressions are performed to determine the output elasticity of capital and labor. We adjust it based on the function’s initial assumptions: α = 0.7, β = 0.3. The marginal output levels of capital and labor in each region are denoted as follows:

$${MPK}_{it}=A_{it}\alpha K_{it}^{\alpha -1}{L}_{it}^{\beta }=\alpha ·{\displaystyle \frac{Y_{it}}{K_{it}}}$$

(3)

$${MPL}_{it}=A_{it}{K}_{it}^{\alpha }\beta L_{it}^{\beta -1}=\beta ·{\displaystyle \frac{Y_{it}}{Lit}}$$

(4)

It enables the calculation of the degree of distortion in capital and labor factor price, as well as the overall factor price distortion.

$${DistK}_{it}={\displaystyle \frac{{MPK}_{it}}{R_{it}}}$$

(5)

$${DistL}_{it}={\displaystyle \frac{{MPL}_{it}}{W_{it}}}$$

(6)

$${Dist}_{it}={DistK}_{it}^{{\displaystyle \frac{\alpha }{\alpha +\beta }}}{·DistL}_{it}^{{\displaystyle \frac{\beta }{\alpha +\beta }}}$$

(7)

This section’s data illustrate the following:

(1) Y represents each province’s Gross Domestic Product (GDP) as a proxy variable. (2) L is the total number of employed individuals in each province. (3) The calculation of capital stock K follows Zhang et al.’s (2004) method using the perpetual inventory method [38]. The real fixed asset investment completion amount is adjusted by subtracting the nominal fixed asset investment completion amount from each province’s fixed asset price index (1952 = 100). For fixed asset price index data post-2018, this study adopts the approach of Cao (2022) using the Producer Price Index (PPI) as a supplement [39]. Nominal total fixed asset formation data for 2017 in each province are missing. The interpolation and growth rate calculations are utilized to estimate the data for missing years. The depreciation rate is set at 9.6%, following Zhang’s (2004) research, and the perpetual inventory method formula is as follows [38]:

$$K_{it}=K_{it-1}(1-\sigma )+I_{it}$$

(8)

Labor wage W is represented by the average wage level of urban employed individuals. (5) Capital price R follows the method of Han (2024). It is calculated by subtracting the sum of the one-year benchmark loan interest rate and depreciation rate from the inflation rate [40].

The selection of Y, L, W is based on Xu’s (2022) approach, with data sourced from each province’s statistical yearbooks [41]. The calculation of K draws from Zhang (2004) and Cao (2022) [38,39]. It uses data from the National Bureau of Statistics and each province’s statistical yearbooks. R is referenced from Han et al. (2024) [40], with data sourced from the People’s Bank of China and the National Bureau of Statistics.

From Figure 2, we can observe the following: (1) In most cases, capital price distortions (DistK), labor price distortions (DistL), and overall factor price distortions (Dist) exhibit negative distortions [17]. This phenomenon primarily arises from policies prioritizing heavy industry development during the transition period. In addition, in order to maintain the price advantage of exports, the government intervenes to reduce the price of the factor market. However, this negative distortion is diminishing, gradually approaching “1”. Particularly after 2019, labor price distortions (DistL) have even shown positive distortion. On the one hand, this is due to improvements in policy systems. On the other hand, the enhancement of factor marketization has corrected such distortions. (2) Capital price distortions (DistK) are generally higher than labor price distortions (DistL) in most cases. This may be due to greater government intervention in capital markets [42]. Furthermore, the implementation of the “minimum wage standard” in 2004 further reduced labor price distortion (DistL). Moreover, the intervention in capital factors is more convenient. Theoretically, the government could intervene in prices of labor and capital. However, since the ownership of labor resources belongs to the laborers themselves, they can flow based on their own utility judgments, making labor a relatively flexible production factor. If the government forcibly sets wages for laborers below the equilibrium price through administrative means, it is likely to trigger the flow of labor resources. Therefore, the government’s focus on factor price intervention mainly falls on capital factors, which may cause capital prices to be influenced by more non-market factors. Thus, it exacerbates the degree of its negative distortion. (3) The degree of negative capital price distortions (DistK) increased due to the impact of the 2008 economic crisis. The economic downturn caused by the crisis led the government to take measures to stabilize the economy. For example, governments should take the “4 trillion yuan stimulus plan” to increase capital flow, reduce capital costs, and stimulate economic recovery.

From Figure 3, in most cases, capital factor price distortions (DistK), labor factor price distortions (DistL), and overall factor price distortions (Dist) in coastal regions are higher than those in non-coastal regions. This is primarily because, in the early stages of reform and opening up, the government mainly supported economic development in coastal regions. The government implements more interventions in factor markets, leading to higher levels of factor price distortions (Dist) in coastal regions. Additionally, the degree of capital price distortions (DistK) is higher than labor price distortions (DistL), a trend observed both in coastal and non-coastal regions.

This could be attributed to the disparities in economic development levels. Due to history, geography, and policies, coastal regions enjoy a relatively high level of economic development and a relatively mature market mechanism. This facilitates the free flow and effective allocation of resource factors in the market. It also reduces the impact of government intervention and administrative barriers on factor prices. In contrast, non-coastal regions have a lower level of economic development and an incomplete market mechanism. This may lead to more non-market factors interfering with and distorting factor prices.

Moreover, coastal regions demonstrate a heightened level of marketization, characterized by intense market competition and rational behaviors. This dynamic contributes to the establishment of factor prices mirroring genuine supply–demand dynamics. Conversely, the lower degree of marketization in non-coastal regions may result in factor prices deviating from their equilibrium levels. Additionally, coastal regions boast advantageous geographical locations, facilitating integration with international market and the conduct of foreign trade. It promotes the development of a more open and competitive factor market, reducing price distortions. In contrast, the relatively remote geographical locations and inferior transportation conditions of non-coastal regions may hinder the free flow of factors.

To better observe the spatial volatility of factor price distortions (Dist), we also calculate the spatial variation coefficient (as shown in Figure 4 and Figure 5). It could reflect the differences and dynamic changes in distortion of capital and labor prices at different time points. The coefficient of variation at the spatial level of capital price distortions is shown in Figure 4. The coefficients of variation at the spatial level of capital price distortions in all regions are in a process of decreasing first and then falling later. These results point out that following 2002, capital price distortion (DistK) variations between coastal and non-coastal regions show a trend. This trend involves a cycle of first narrowing, then widening, and finally narrowing again in capital price distortions. The initial narrowing of capital price distortions (DistK) is mainly fueled by uneven but strong regional economic growth. Some regions experience faster growth and market integration, facilitating more efficient capital allocation. This efficient allocation subsequently leads to a reduction in distortions. However, the subsequent widening of distortions can be explained by differing and often conflicting local government interventions. Governments may aim to safeguard local industries or advance specific economic goals. These actions introduce obstacles to the free movement of capital and exacerbate price differences.

Figure 5 shows the fluctuation trend of the coefficient of variation at the spatial level for labor price distortions (DistL). Coastal regions experienced a decline from 2002 to 2006, followed by a period of stability, indicating a narrowing of price distortion differences in coastal regions, which then remained stable. In contrast, labor price distortions (DistL) in non-coastal regions showed significant fluctuations from 2002 to 2010, stabilizing after 2012. The main factors influencing the spatial variation coefficient in non-coastal regions are the varying levels of economic development among different regions. It could lead to significant internal differences affecting the degree of labor factor mobility. Thus, it causes fluctuations in distortion differences among regions.

The primary drivers behind the spatial variation coefficient observed in non-coastal regions stem from the diverse and uneven levels of economic development across these different territories. This heterogeneity in economic progress not only creates profound internal disparities but also significantly impacts the degree of mobility of labor factors within these regions. Consequently, these factors play a role in the emergence of fluctuations. These fluctuations persist in distortion differences. These exist among various non-coastal regions. This underscores the intricate relationship between economic development and labor market dynamics.

4.1.2. The Explained Variable

The explained variable in this paper is export technology complexity (Expy). Hausman (2007) constructed export technology complexity based on the logic of the production structure reflected by trade structure [43]. Following this approach, this study calculates export technology complexity of each province with the following formula:

$${Expy}_{it}=\sum {\displaystyle \frac{x_{ikt}}{X_{it}}}·{PRODY}_{kt}$$

(9)

$${PRODY}_{kt}={\displaystyle {\sum }_i}{\displaystyle \frac{{(x}_{ikt}/X_{it})}{{\displaystyle {\sum }_i}{(x}_{ikt}/X_{it})}}·{PGDP}_{it}$$

(10)

where x_ikt represents the export value of industry k in province i in year t. X_ikt represents the total export value of province i in year t. PGDP_kt represents the per capita GDP of province i in year t. PRODY_kt represents the export complexity of industry k in year t. Expy_it represents the export technology complexity of province i in year t. This paper utilizes export amount data for 22 product categories under HS2 customs codes provided by the General Administration of Customs of China. The data used are from 2002 to 2022 to calculate the export technology complexity of each province.

4.1.3. Mediating Variables

The mediating variables in this paper are as follows: (1) Foreign direct investment (FDI) [44]: the proportion of regional FDI to GDP is used to reflect this. (2) Trade openness (OL) [45]: the ratio of import and export values to regional GDP is employed. The moderating variable is the marketization level (MI), measured by the “Marketization Index” in this paper [46].

4.1.4. Control Variables

To control for the impact of other variables on export technology complexity, we draw from Liang (2024) [47] and select the following: (1) Government intervention level (Gov): general public budget expenditure of local governments/Local GDP. (2) Industrialization level (DI): industrial value added/Gross Domestic Product. (3) Per capita Gross Domestic Product (PGDP). (4) The level of informatization (IL): total postal and telecommunication services/GDP. (5) Regional technical capability (RTA), data sourced from the Regional Innovation Capability Report, including sub-indices, such as knowledge creation, knowledge acquisition, corporate innovation, innovation environment, and innovation performance. The main variables are defined as shown in

Table 1. Main variable description.

Variables	Type of Variables	Variable Declaration	References
Expy	Explained variable	$\sum {\displaystyle \frac{x_{ikt}}{X_{it}}}$ × PRODY	Hausman et al. (2007) [43]
Dist	Explanatory variable	${DistK}^{{\displaystyle \frac{\alpha }{\alpha +\beta }}}$ $\times {DistL}^{{\displaystyle \frac{\alpha }{\alpha +\beta }}}$	García-Belenguer et al. (2013) and Yang et al. (2018) [36,37]
DistK	Explanatory variable	capital marginal output/capital price
DistL	Explanatory variable	labor marginal output/labor price
FDI	Mediating variable	regional FDI/regiona GDP	Li and Xiao (2024) [44]
OL	Mediating variable	regional total import and export volume/regional GDP	Luqman (2024) [45]
MI	Moderating variable	marketization index	Fan et al. (2003) [46]

.

4.2. Model Construction

First of all, we construct the relationship function between factor price distortions and export technology complexity. We logarithmically transform non-binary variables to standardize dimensions and reduce the sample data fluctuations. We apply trimming at the 1% and 99% levels to the main variables to ensure robustness against outliers.

$${Expy}_{it}={\alpha }_0+{\beta }_1{dist}_{it}+{\beta }_2\sum \mathit{Control}+{ province}_i+{year}_t+{\epsilon }_{it}$$

(11)

$$\sum \mathit{Control}: {gov}_{it}+{di}_{it}+{gnp}_{it}+{il}_{it}+{rta}_{it}$$

(12)

Diverse levels of distortion exist in labor and capital factor prices, impacting export technology complexity differently. To investigate this issue, we dissect the explanatory variables into the following econometric models for analysis.

$${Expy}_{it}={\alpha }_0+{\gamma }_1{disK}_{it}+{\gamma }_2\sum \mathit{Control}+{ province}_i+{year}_t+{\epsilon }_{it}$$

(13)

$${Expy}_{it}={\alpha }_0+{\delta }_1{disL}_{it}+{\delta }_2\sum \mathit{Control}+{ province}_i+{year}_t+{\epsilon }_{it}$$

(14)

The explained variable in the above model is export technology complexity in region i at time t, while the key explanatory variable is the overall factor price distortions in region i at time t. ${DisK}_{it}$ and ${DisL}_{it}$ represent, respectively, the distortions in capital and labor price in region i at time t. The model includes control variables, such as government intervention level (Gov), industrialization level (DI), per capita GDP (PGDP), the level of informatization (IL), regional technical capability (RTA). In addition, the model includes the region fixed effect in province _i, time fixed effect in year _t and the residual term ${\epsilon }_{it}$.

Table 2. Descriptive statistics of the primary variables.

Variables	N	Mean	Standard Deviation	Minimum Value	Maximum Value
Expy	630	8.5663	0.7374	6.9220	9.6308
Dist	630	1.8764	0.6920	0.6817	4.9294
DistK	630	2.2095	0.9272	0.7061	6.8542
DistL	630	1.2744	0.5040	0.0969	3.5795
FDI	630	0.0238	0.0223	0.0001	0.1635
OL	630	0.3075	0.3577	0.0076	1.7113
MI	630	0.7437	0.2043	0.2243	1.2864
Gov	630	0.2259	0.1053	0.0811	0.7583
DI	630	0.3537	0.0860	0.1001	0.5738
$\mathrm{PGDP}$	630	10.3535	0.8040	8.0886	12.1564
$\mathrm{IL}$	630	0.0625	0.0428	0.0153	0.2901
RTA	630	3.3147	0.3264	2.6376	4.1819

Note: The authors generated this method by using Stata 14.

reveals that the maximum value of export technology complexity is 9.6308; the minimum is 6.9220. Export technology complexity has a standard deviation of 0.7374, indicating variations among provinces. Meanwhile, the maximum value of overall factor price distortions is 4.9294; the minimum is 0.6817. Additionally, factor price distortions have a standard deviation of 0.6920, showing a concentration trend around 1.8764 among provinces. The significant difference between the maximum value and the mean suggests substantial disparities in overall price distortion. Particularly, similar situations exist for capital and labor factor price distortions. Capital factor price distortions have a higher mean, indicating greater dispersion from the mean and the presence of extreme values in certain provinces.

4.3. Description of the Data Source

Data were collected from official statistical yearbooks, and samples with too many missing data were excluded. As a result, this study sets the sample time period from 2002 to 2022, covering 30 provinces and regions. Drawing on research in related fields, we conducted accounting and processing of the raw data. After the completion of data accounting, to prevent outliers from affecting the research results, we performed winsorization at the 1% and 99% levels. Data on factor price distortions (Dist) are primarily sourced from the National Bureau of Statistics, People’s Bank of China, and provincial statistical yearbooks. Export technology complexity (Expy), FDI, and trade openness (OL) data are obtained from the General Administration of Customs and provincial statistical yearbooks. The moderating variable, the marketization index (MI), is derived from comprehensive marketization index reports. Control variable government intervention level (GOV) data are derived from provincial statistical yearbooks. The industrialization level (DI) data are from the Ministry of Industry and Information Technology. Per capita GDP (PGDP) data are sourced from provincial statistical yearbooks. Informationization level (IL) data come from the “China Information Industry Yearbook”. Regional technical capability (RTA) data are obtained from the “China Regional Innovation and Entrepreneurship Index”.

5. Results

5.1. Main Effects Analysis

We conduct regression analysis using panel data and employ a two-way fixed effects model for main effects analysis, divide effect test, and subsequent mechanism analysis. The panel data models are prone to heteroscedasticity and serial correlation issues, so mixed OLS and XTSCC methods are utilized in this section as references to ensure the robustness of the research findings.

To visually depict the relationship between factor price distortions and export technology complexity, this paper plots a linear regression graph of both variables. Figure 6 illustrates a negative linear relationship between factor price distortions and export technology complexity. The empirical regression results quantify the impact of factor price distortions on export technology complexity.

Table 3. The results of the main effects analysis.

Variables	Model 1	Model 2	Model 3	Model 4
	Full sample
Dist	−0.140 *** (0.0119)	−0.0271 *** (0.00908)	−0.142 ** (0.0551)	−0.0656 ** (0.0270)
Gov	1.455 *** (0.0985)	0.753 *** (0.0652)	0.242 (0.458)	0.745 *** (0.153)
DI	−0.151 (0.0940)	0.349 *** (0.0737)	0.237 (0.398)	1.221 *** (0.202)
PGDP	0.871 *** (0.0124)	0.171 *** (0.0238)	0.855 *** (0.0614)	0.317 *** (0.0645)
IL	−0.0495 *** (0.00960)	0.0264 ** (0.0120)	−0.0111 (0.0260)	−0.0726 * (0.0397)
RTA	−0.268 *** (0.0430)	−0.0433 * (0.0259)	−0.629 *** (0.199)	0.0638 (0.0993)
Constant	0.457 ** (0.180)	5.601 *** (0.188)	1.934** (0.709)	−151.4 *** (22.08)
Controll variables	Yes	Yes	Yes	Yes
Province	Yes	Yes	No	Yes
Year	No	Yes	No	Yes
Estimation method	FE	FE	OLS	XTSCC
$R^2$	0.984	0.996	0.886	0.971
N	630	630	630	630

Note: *** represents p < 0.01, ** represents p < 0.05, * represents p < 0.1.

shows the regression results of the impact of factor price distortions on export technology complexity across different models in columns 1–4. The results consistently show a significant negative coefficient for factor price distortions on export technology complexity in all estimation strategies [48,49]. It indicates that regional factor price distortions hinder improvements in export technology complexity when distortions occur. Higher levels of factor price distortions make it more challenging to enhance export technology complexity due to resource misallocation caused by these distortions. Therefore, hypothesis H1 is supported by the findings.

Factor price distortions lead to the inability of the market to effectively allocate resources. When factor prices deviate from their true values, enterprises may make production decisions based on distorted price signals. It also neglects the importance of technological upgrading and efficiency improvements. Therefore, to enhance the technological sophistication of exports, it is necessary to deepen the market-oriented reform of factors. It also allows the market to play a decisive role in resource allocation. By improving the formation mechanism of factor prices and eliminating price distortions, we can guide enterprises to make production decisions and allocate resources based on authentic market signals.

5.2. Divide Effect Analysis

To examine the effects of capital price distortions and labor price distortions on export technology complexity, this paper conducts separate regressions for both variables. The results are presented in

Table 4. The results of the divide effect analysis.

Variables	Model 1	Model 2	Model 3	Model 4
	Full sample	Sub-sample
Dist	−0.0271 *** (0.00908)
DistK		−0.0207 *** (0.00580)
DistL_1			0.00918 (0.00732)
$\mathrm{DistL}\_2$				0.0140 * (0.00741)
Gov	0.753 *** (0.0652)	0.726 *** (0.0642)	0.703 *** (0.0670)	0.664 *** (0.0663)
DI	0.349 *** (0.0737)	0.328 *** (0.0723)	0.288 *** (0.0742)	0.0670 (0.0819)
PGDP	0.171 *** (0.0238)	0.163 *** (0.0237)	0.164 *** (0.0244)	0.203 *** (0.0271)
IL	0.0264 ** (0.0120)	0.0274 ** (0.0120)	0.0270 ** (0.0121)	0.681 *** (0.169)
RTA	−0.0433 * (0.0259)	−0.0430 * (0.0257)	−0.0367 (0.0260)	−0.0792 *** (0.0275)
$\mathrm{Constant}$	5.601 *** (0.188)	5.681 *** (0.191)	5.570 *** (0.191)	5.400 *** (0.211)
$R^2$	0.996	0.996	0.996	0.996
N	630	630	630	540

Note: *** represents p < 0.01, ** represents p < 0.05, * represents p < 0.1.

. The findings reveal a significant negative relationship between capital price distortions and export technology complexity, with a coefficient of −0.0207. In contrast, labor price distortions show a coefficient of 0.00918, which is not statistically significant. Considering the changes in the direction of labor price distortions, this study excludes the impact of these anomalies and conducts a separate regression analysis for the years 2002–2019. The results indicate a positive and significant coefficient of 0.0140 for labor price distortions on export technology complexity. Furthermore, it also suggests that negative labor price distortions show a modest positive impact on export technology complexity. However, negative capital price distortions have the most significant obstructive effect on export technology complexity.

When distortions occur in the price of capital factors, the cost of capital utilization may fall below its true value, leading enterprises to excessively rely on capital inputs while neglecting the balanced allocation of other production factors. In the long run, this imbalanced resource allocation reduces overall production efficiency, thereby affecting the technological sophistication and quality of products. While distortions in capital prices may stimulate enterprises to expand their scale, the realization of economies of scale does not always enhance technological sophistication. If scale expansion is not accompanied by effective management and technological innovation, it may instead lead to decreased efficiency and increased costs. R&D and innovation are crucial to enhancing the technological complexity of products, and the lack of these investments would directly constrain improvements in product technological content.

5.3. Heterogeneity Tests

To explore regional differences in the impact of factor price distortions on export technology complexity, this paper divides the analysis into coastal and non-coastal regions. The results are shown in

Table 5. The results of the heterogeneity tests.

Variables	Model 1	Model 2	Model 3	Model 4
	Expy	Expy	Expy	Expy
Dist	−0.0474 *** (0.00961)
DistK		−0.0324 *** (0.00627)
DistL_1			−0.00472 (0.00866)
$\mathrm{DistL}\_2$				−0.00293 (0.00996)
$\mathrm{D}\times$$\mathrm{Dist}$	0.0409 *** (0.00750)
$\mathrm{D}\times$DistK		0.0250 *** (0.00556)
$\mathrm{D}\times$DistL_1			0.0224 *** (0.00756)
$\mathrm{D}\times$DistL_2				0.0249 ** (0.00986)
Gov	0.67 2 *** (0.0653)	0.667 *** (0.0645)	0.68 3 *** (0.0669)	0.644 *** (0.0664)
DI	0.370 *** (0.0720)	0.343 *** (0.0712)	0.295 *** (0.0737)	0.0688 (0.0815)
PGDP	0.107 *** (0.0260)	0.118 *** (0.0254)	0.146 *** (0.0250)	0.198 *** (0.0270)
IL	0.0242 ** (0.0117)	0.0258 ** (0.0118)	0.0266 ** (0.0120)	0.701 *** (0.169)
RTA	−0.0280 (0.0254)	−0.0308 (0.0255)	−0.0358 (0.0259)	−0.0791 *** (0.0274)
$\mathrm{Constant}$	6.147 *** (0.209)	6.059 *** (0.206)	5.736 *** (0.198)	5.455 *** (0.211)
$R^2$	0.996	0.996	0.996	0.996
N	630	630	630	540

Note: *** represents p < 0.01, ** represents p < 0.05.

. In coastal regions, D = 1, and in non-coastal regions, D = 0. The regression results show that D$\times$Dist is significantly greater than 0 [50]. It suggests that both coastal and non-coastal regions experience inhibitory effects of factor price distortions on export technology complexity. Moreover, the negative impact of factor price distortions on export technology complexity is weaker in coastal regions. In environments with a similar distortion of factor markets, the location advantage of coastal regions would reduce trade costs and trade risks. Furthermore, this decline is conducive to export technology complexity improvements. The international competition brought about by the development of international trade would further encourage enterprises to continuously improve the technical content of their products. Additionally, the higher degree of marketization in coastal regions compared to non-coastal regions partially alleviates the hindrance of factor price distortions on export technology complexity.

The regression analysis reveals pronounced regional heterogeneity in the impact of factor price distortions on export technology complexity. To gain a deeper insight into these varying effects, we specifically examined the differential impacts of labor price distortions on export technology complexity between coastal and non-coastal regions. In parallel, we also conducted separate regression analyses to verify the unique effects of capital price distortions on export technology complexity within both coastal and non-coastal regions. The results show that D$\times$DistK, D$\times$DistL_1, and D$\times$DistL_2 are all significantly greater than 0. This indicates that the negative effect of capital and labor factor price distortions in coastal regions on export technology complexity is also weaker than that in non-coastal regions.

Due to long-term historical development and the pioneering role of reform and opening-up, coastal regions possess a solid economic foundation and a more mature market environment. This enables enterprises in coastal regions to possess more resources and capabilities to adjust and optimize production structures when facing distortions in capital and labor prices. It also mitigates negative impacts on the technological sophistication of exports. Secondly, coastal regions generally enjoy a higher degree of openness to the outside world than non-coastal regions. This facilitates coastal enterprises’ access to internationally advanced technologies and management experiences. At the same time, it also enhances their technological level and production efficiency. Furthermore, coastal regions typically boast richer talent and educational resources. The clustering of universities, research institutions, and vocational training institutions provides coastal enterprises with an abundant supply of high-quality labor. These talents not only possess advanced professional skills but also demonstrate strong innovative consciousness and learning abilities. This contributes to the enhancement of technological sophistication in exported products.

Recognizing coastal regions’ advantages in addressing distortions in capital and labor prices is essential. This recognition could aid governments in formulating regional development policies. The policies should be more scientific and rational. They aim to promote coordinated regional economic development. Governments may consider increasing support for non-coastal regions. This would strengthen their economic foundations and market environments. Additionally, it promotes technological upgrading among enterprises in those regions. Product innovation is also encouraged to further boost regional development.

5.4. Endogeneity Analysis

Considering that export technology complexity may also affect factor price distortions and the potential omitted variable issues, we employ the 2SLS method for endogeneity testing, following the approach of Shi et al. (2012) [50]. Initially, the lagged one-period variables replace the current variables in regression. Subsequently, the lagged one-period variables are used as instruments for the current variables in regression. The results in

Table 6. The results of the endogeneity analysis.

Variables	Lag One Phase	The First Steps	The Second Step
	Expy	Dist	Expy
Dist			−0.1911 *** (0.0337)
L.Dist	−0.0268 *** (0.00926)
$\mathrm{IV}$		0.7905 *** (0.0224)
Gov	0.732 *** (0.0677)	—0.2116 *** (0.0871)	0.1357 (0.1523)
DI	0.378 *** (0.0759)	−0.0033 (0.0830)	0.1567 (0.1370)
PGDP	0.170 *** (0.0249)	−0.0513 *** (0.0211)	0.8113 *** (0.0289)
IL	0.239 ** (0.120)	−1.4512 *** (0.2452)	−0.1497 (0.1955)
RTA	−0.0342 (0.0274)	0.0818 *** (0.0398)	−0.5719 *** (0631)
Constant	5.677 *** (0.199)	0.6982 *** (0.1964)	2.3496 *** (0.0289)
$R^2$	0.995	0.916	0.871
N	600	600	600

Note: *** represents p < 0.01, ** represents p < 0.05.

demonstrate robustness, unaffected by endogeneity.

5.5. Robustness Checks

(1) We change the time interval. We consider the impact of the 2008 financial crisis. In order to avoid the disturbance between factor price distortions and export technology complexity caused by economic fluctuations in a specific period, we change the time interval to 2012–2022 to test the credibility of the results. (2) We substitute the dependent variable. Following Dai’s approach, we introduce a standard export technology complexity index for robustness testing [51]. The formula for calculating the standard export technology complexity index is as follows:

$${SExpy}_{it}=\left[\right({Expy}_{it }{-Expy}_{it}\min )/({Expy}_{it}\max -{Expy}_{it}\max \left)\right]·100$$

(15)

The values range from 0 to 100 without measurement units, allowing for intertemporal comparisons (estimated using natural logarithms). The regression results in

Table 7. The results of the robustness checks.

Variables	Model 1	Model 2
	Expy	SExpy
Dist	−0.0467 ** (0.0182)	−0.0689 ** (0.0323)
Gov	0.0692 (0.0981)	2.517 *** (0.235)
DI	0.682 *** (0.105)	0.869 *** (0.263)
PGDP	−0.0736 * (0.0432)	0.511 *** (0.0844)
IL	0.00810 (0.00824)	0.883 ** (0.426)
RTA	−0.0147 (0.0265)	−0.222 ** (0.0919)
Constant	9.438 *** (0.450)	−3.882 *** (0.669)
$R^2$	0.981	0.977
N	330	630

Note: *** represents p < 0.01, ** represents p < 0.05, * represents p < 0.1.

demonstrate that all regression coefficients are significantly negative, indicating robustness.

5.6. Mechanism Tests

5.6.1. Mediator Effect Analysis

In this section, we further examine the process of factor price distortions affecting export technology complexity through FDI and trade openness. The results in

Table 8. The results of the mediator effect analysis.

Variables	Model 1	Model 2	Model 3	Model 4
	The second step of FDI	The third step of FDI	The second step of OL	The third step of OL
Dist	−0.0041 * (0.00227)	−0.0247 *** (0.00902)	−0.0576 *** (0.0179)	−0.0142 * (0.00822)
$\mathrm{FDI}$		0.579 *** (0.165)
$\mathrm{OL}$				0.224 *** (0.0190)
Gov	0.0443 *** (0.0163)	0.727 *** (0.0649)	0.687 *** (0.129)	0.599 *** (0.0599)
DI	0.0164 (0.0184)	0.339 *** (0.0730)	−0.183 (0.145)	0.390 *** (0.0662)
PGDP	0.0255 *** (0.00593)	0.156 *** (0.0239)	0.614 *** (0.0469)	0.0332 (0.0243)
IL	0.00518 * (0.00300)	0.0234 * (0.0119)	0.0639 *** (0.0237)	0.0120 (0.0108)
RTA	0.0117 * (0.00646)	−0.0501 * (0.0257)	−0.221 *** (0.0510)	0.00628 (0.0236)
$\mathrm{Constant}$	−0.238 *** (0.0470)	5.739 *** (0.191)	−4.472 *** (0.372)	6.605 *** (0.189)
$R^2$	0.312	0.996	0.441	0.997
N	630	630	630	630

Note: *** represents p < 0.01, * represents p < 0.1.

indicate that factor price distortions show a negative impact on export technology complexity through FDI and trade openness suppression.

We found that when FDI and trade openness play an intermediary role, the absolute influence coefficient of factor price distortions on export technology complexity becomes smaller [52,53]. The regression shows that when adding FDI and trade openness, respectively, the influence of the coefficient of factor price distortions on export technology complexity is smaller. This suggests that FDI and trade openness play a partial mediating role, an effect not replaced, as shown in the Figure 7.

The total effect of factor price distortions on export technology complexity is −0.0271. When FDI acts as a partial mediator, the direct effect is −0.0247, the indirect effect is −0.0024, and the mediating effect accounts for 8.86%. When trade openness acts as a partial mediator, the direct effect is −0.0142 with reduced significance, the indirect effect is −0.0129, and the mediating effect accounts for 47.6%. Thus, while both FDI and trade openness play partial mediating roles, FDI’s mediating contribution is lower.

The mediating role of FDI indicates that attracting high-quality foreign direct investment can partially alleviate negative impacts stemming from factor price distortions. Therefore, policymakers should strive to optimize the foreign investment environment and attract foreign investment projects. These could also bring forward advanced technologies and management experiences. Through the technology spillover effect of foreign capital, the technological level and innovation capabilities of domestic enterprises could be enhanced, which could indirectly elevate the technological sophistication of export products. The mediating effect of trade openness underscores the significance of international trade in fostering technological upgrading. By reducing tariff barriers, simplifying customs clearance, and strengthening trade agreement negotiations, market access can be expanded. This expanded market access intensifies competition in domestic and foreign markets, compelling enterprises to increase R&D investments. Enterprises then upgrade product quality and technological content, adapting to international demands, thereby enhancing the technological sophistication of export products.

5.6.2. Regulatory Effect Analysis

To examine whether the degree of marketization moderates the relationship between factor price distortions and export technology complexity, we regressed the moderating effect of the marketization index.

The results in

Table 9. The results of the regulatory effect analysis.

Variable Name	Model 1	Model 2
	The benchmark regression	The moderating effect
Dist	−0.0295 *** (0.00897)	−0.0144 * (0.00862)
$\mathrm{MI}$	−0.183 *** (0.0433)	−0.106 ** (0.0418)
C_MI$\times$C_Dist		0.206 *** (0.0239)
Gov	0.708 *** (0.0651)	0.539 *** (0.0643)
DI	0.306 *** (0.0733)	0.287 *** (0.0690)
PGDP	0.192 *** (0.0240)	0.115 *** (0.0243)
IL	0.0319 *** (0.0119)	0.0321 *** (0.0112)
RTA	−0.0390 (0.0255)	−0.0349 (0.0240)
$\mathrm{Constant}$	5.512 *** (0.187)	6.208 *** (0.193)
$R^2$	0.996	0.996
N	630	630

Note: *** represents p < 0.01, ** represents p < 0.05, * represents p < 0.1.

show that the interaction term coefficient is 0.206, with a significant coefficient greater than zero. This indicates that the marketization index indeed plays a positive moderating role. Furthermore, it means that the marketization index weakens the negative impact of factor price distortions on export technology complexity [54].

The enhancement of marketization contributes to optimizing the efficiency of resource allocation. In a highly marketized environment, enterprises can flexibly adjust their production and product structures. This adjustment is based on market demands and factor price changes, achieving optimal resource allocation. This will facilitate improvements in enterprises’ production efficiency and innovation capabilities, further boosting the technological sophistication of export products.

Governments should gradually reduce direct intervention in factor markets and allow market forces to play a more significant role in shaping factor prices. By refining market mechanisms, governments could achieve rationalization and transparency in factor pricing. This would subsequently reduce factor price distortions and enhance the technological sophistication and international competitiveness of export products.

6. Conclusions, Implications and Research Limitations

6.1. Conclusions

Due to the delayed reform in factor marketization in China, negative factor price distortions persist. This study focuses on various provinces in China to investigate the impact of factor price distortions on export technology complexity. We systematically collect and collate the data. The research quantitatively verifies the relationships among factor price distortions, FDI, trade openness, and export technology complexity. The specific conclusions are as follows:

(1) China still faces negative factor price distortions, with capital factor distortions generally higher than labor factor distortions most of the time. However, recent calculations have shown that factor price distortions in China are gradually easing. This indicates the effectiveness of the factor marketization reforms in recent years.

(2) Factor price distortions show a significant negative impact on the enhancement of export technology complexity. In other words, factor price distortions hinder improvements in export technology complexity. Even though the initial low-cost advantage of distortions provides some incentive for business development, as factor price distortions persist, the drawbacks increasingly overshadow the positive effects of serious price distortions.

(3) Factor price distortions hinder the enhancement of export technology complexity by suppressing FDI and trade openness. Factor price distortions have a direct effect on export technology complexity. In addition, factor price distortions would also indirectly hinder export technology complexity through FDI and trade openness. This suggests the presence of other mediating variables between them, providing new research avenues for the future.

(4) The level of marketization weakens the negative impact of factor price distortions on export technology complexity. This means that the higher the degree of marketization, the weaker the inhibitory effect of factor price distortions on export technology complexity. In recent years, with the advancement of marketization in China, the phenomenon of factor price distortions has been alleviated. The increased transparency of market-based pricing mechanisms and enhanced market competition, to a certain extent, promotes the technological innovation and efficiency improvements of export enterprises, and the export technology complexity continuously improves [55].

6.2. Implications

To visually present the research findings and their practical application value, we constructed Figure 8, which clearly illustrates how the research conclusions can be translated into practical recommendations.

(1) Some companies should ensure the rational allocation and efficient utilization of various production factors such as labor, materials, and finances to avoid resource idleness or waste. They should conduct accurate cost accounting and value assessment for their products and services. Furthermore, they should set reasonable prices and prevent factor price distortions caused by unreasonable pricing.

(2) Some companies should promptly monitor price fluctuations and policy adjustments in domestic and international markets. They should prepare in advance for countermeasures and mitigate the impact of factor price distortions. By advancing digital transformation, these companies could leverage digital technologies to enhance production efficiency, management capabilities, and market responsiveness, thus increasing export technology complexity. For instance, some high-tech enterprises have successfully developed high-end products with core competitiveness by increasing R&D investments continuously. Moreover, they gain strong reputations and added value in international markets, effectively avoiding the adverse effects of factor price distortions. Simultaneously, they utilize the digital systems to monitor and optimize their supply chains in real time, ensuring stable and reasonably priced raw materials.

(3) By reducing distortions in resource prices, enterprises can promote sustainable innovation and upgrading in the workplace. This could be achieved by optimizing compensation systems to reflect employees’ true value or enhancing equipment efficiency to reduce energy consumption. The detrimental influence of factor price distortions on the sophistication of export technology is pronounced and cannot be overlooked. This underscores the importance of technological innovation in driving industrial upgrading and sustainability during enterprise development. In the workplace, encouraging employee innovation and providing necessary R&D support and resources can enhance workflow efficiency and sustainability.

(4) Governments need to advance reforms in factor markets based on the actual situation of factor price distortions and the marketization process in different regions. They should promote the initial distribution reform of factors through continuous improvements in fiscal and tax systems and wage structures. Additionally, governments should particularly focus on enhancing export technology complexity. Furthermore, they should continuously adjust and improve export tax rebate policies and subsidy schemes.

(5) Relevant governments need to enhance fiscal and tax policies to encourage companies to increase R&D investments. They should coordinate adjustments in import and export tariffs with industrial upgrading policies. Governments could promote the development of high-tech products in domestic trade by adjusting import and export tariffs. The government should encourage high-tech enterprises to adopt advanced technologies in the international vertical specialization system. Additionally, it should guide them in this endeavor through industrial upgrading policies.

6.3. Research Limitations and Future Research Directions

Due to constraints on the research duration and authors’ resources, there are still some limitations in this study. These limitations will guide our future research focus: (1) This paper only accounts for capital and labor price distortions when calculating factor distortions. We neglect the impact of distortions in other factors on export technology complexity. We will explore the effects of distortions in other factors, like land, energy, and data prices, on export technology complexity in the future. (2) The model design in this paper only considers FDI and trade openness as mediating factors between factor price distortions and export technology complexity. However, these indicators do not comprehensively cover all aspects of the issue. Hence, additional relevant mediating indicators, such as production efficiency, innovation performance, and firm profitability, could be included in the future. Enriching the content of relevant research indicators will lead to more scientifically accurate research conclusions. (3) This paper’s scope needs further expansion. The research only examines the impact of factor price distortions on export technology complexity at the provincial level. In addition to the macro level, exploring the effects of factor price distortions on export technology complexity at the micro level could be beneficial. Areas to consider include high-tech manufacturing industries or specific sectors like medical equipment manufacturing and electromechanical equipment manufacturing. In addition to investigating the impact of factor price distortions on industry-specific export technology complexity, further research could delve into the product-specific export technology complexity.