Import Competition, Labor Market Flexibility, and Skill Premium-Evidence from China Based on the Dynamic Threshold Model

Abstract

This paper examines the impact of import competition on skill premium and the moderating effect of labor market flexibility on it, using panel data from 30 provinces in China from 2010 to 2019. A dynamic panel threshold model with instrumental variables is employed to address the endogeneity problem and to identify the nonlinear moderating effect of labor market flexibility. The results show the following: (1) Import competition has a promoting effect on skill premium, and this effect declines from eastern to western regions in China. (2) The import competition increases the skill premium through the channels of enhancing regional innovation capacity and promoting industrial upgrading and rationalization. (3) There exists a significant threshold effect in the moderating effect of labor market flexibility. When labor market flexibility surpasses the threshold value of 1.330, the enhancing effect of import competition on the skill premium is alleviated, facilitating labor reallocation and wage adjustment. The integration of labor market flexibility into the globalization–inequality debate extends the existing literature for providing a new understanding of the mechanisms behind the skill premium. The policy implications are that targeted labor market reforms are essential for mitigating wage differentials between skilled and unskilled workers arising from intensified import competition.

1. Introduction

At the present time, China has shifted its import policy toward “further actively expanding imports and implementing the reform of high-level opening.” Although this policy of deepening openness and expanding imports benefits the easing of trade imbalances and fostering mutually domestic–international linkages, it intensifies import competition, which induces large-scale resource reallocations, including the exit of inefficient firms and the entry of more productive ones (Melitz, 2003; Qian & Gao, 2021; Dai et al., 2019). These dynamics have profound consequences for labor markets and income distribution, with one key manifestation being changes in the skill premium—the wage differential between skilled and unskilled workers.

The realization of new labor market equilibria under import competition hinges critically on labor mobility, which depends on the degree of labor market flexibility. Labor market flexibility is typically reflected in the flexibility of wage adjustment and working time, and job mobility (Zhou & Yang, 2012). Flexible labor markets adjust wages more quickly to shocks, restoring labor market clearance. In contrast, rigid markets exacerbate unemployment or wage distortions. This article mainly focuses on the spatial mobility of labor, which is reflected in whether labor can quickly move to new positions and regions for employment when external shocks occur.

In China, the persistence of a “dual economy,” shaped by the residence registration system and unequal access to social security and education, has constrained labor mobility and segmented urban–rural labor markets. These institutional frictions hinder the efficient reallocation of both skilled and unskilled workers, thereby reinforcing skill premium. However, most studies treat those institutional frictions in labor market as background rather than explicit mechanisms, leaving open the question of how labor market flexibility moderates the effect of trade shocks on skill premium.

Against this backdrop, understanding how labor market flexibility shapes the relationship between import competition and skill premium is crucial for both academic research and policy design. From a policy perspective, it provides evidence on how institutional reforms reconcile the goals of openness and inclusive growth, avoiding the fact that trade liberalization disproportionately harms certain groups of workers. From an academic standpoint, it enriches the study of globalization and inequality by highlighting the labor market institutional mechanisms. In the context of China, this issue is particularly relevant, as regional differences in labor market flexibility imply heterogeneous impacts of globalization across provinces. Since China is a developing economy, the findings are of great significance for developing economies facing similar institutional constraints.

This paper examines how import competition affects the skill premium in the presence of heterogeneous labor market flexibility in China. Using provincial panel data from 30 provinces during the 2010–2019 period, we employ a dynamic panel threshold model with instrumental variables to address the endogeneity problem and to identify the nonlinear moderating effect of labor market flexibility. The results show that import competition generally increases the skill premium through promoting regional innovation and industrial structure upgrading. However, higher labor market flexibility surpassing the threshold value of 1.330 can mitigate the positive impact of import competition on skill premium.

The remainder of this paper is organized as follows: Section 2 provides a relevant literature review on this topic. Section 3 contains the theoretical analysis and research hypothesis. Section 4 describes the methodology, and Section 5 presents the empirical results. Section 6 concludes this study with contributions, limitations, and implications.

2. Literature Review

The theoretical and empirical work on the effects of import competition on the skill premium is reviewed in this section, focusing on the moderating role of labor market flexibility. Traditional and contemporary trade theories are first reviewed, and then the related empirical paradoxes and the explanations, such as skill-biased technological change (SBTC) and labor market institutions, are highlighted, and the research gaps this study aims to address are finally identified.

2.1. Import Competition and Skill Premium: The Traditional Trade Theory and “Skill Premium Paradox”

At the heart of classical trade theory lies the Stolper–Samuelson (S-S) theorem. This theory posits that trade liberalization will increase the real return to a country’s abundant factor and reduce the return to its scarce factor (Stolper & Samuelson, 1941). In the case of developed countries rich in capital and skilled labor, import competition from developing countries abundant in low-skilled labor is expected to reduce the relative wages of unskilled labor and increase the skill premium. But, in developing countries rich in unskilled labor, import competition from developed countries tends to reduce the skill premiums (Feenstra, 2015).

However, the S-S theorem faces significant empirical challenges. Numerous studies have observed rising skill premiums in both developed and developing countries along with increased trade liberalization (Autor et al., 2013; Sachs & Shatz, 1994), which is often referred to as “The mystery of developing countries” or “skill premium paradox.” This suggests that the S-S framework fails to account for the complexity of labor market adjustments, and new theoretical frameworks must be considered.

2.2. New Explanations and Limitations: The Dominance of Skill-Biased Technological Change (SBTC)

To resolve the aforementioned paradox, research perspectives have been gradually shifted from traditional factor endowment theory to the integrated framework of the Task-Based Approach and Skill-Biased Technological Change (SBTC) (Acemoglu & Autor, 2011). The Task-Based Approach suggests that import competition (especially from low-wage countries) does not directly affect workers with specific skills but impacts “routine” production tasks, such as assembly line work and data entry, which are the most vulnerable to dual shocks of both automatization and outsourcing. As import competition accelerates the offshoring and automation of routine tasks, the demand for low-skilled workers engaging in these tasks is reduced, leading to employment “polarization” and widening wage gaps.

The combination of trade-induced technological advancements and SBTC provides a robust explanation for the rising wage gap between skilled and unskilled workers (Burstein et al., 2013; Raveh & Reshef, 2016). According to the SBTC theory, import competition forces firms to adopt more advanced technologies to stay competitive, which raises the demand for skilled workers (Kasahara et al., 2016; Chakraborty & Raveh, 2018) and drives up the skill premium (Acemoglu, 2002; Goldin & Katz, 2008).

Even within the SBTC framework, advanced economies exhibit markedly different responses to similar global shocks, such as the “China Shock.” For instance, one of the seminal works in this area is the study by Autor et al. (2013), which found compelling evidence that increased import competition, particularly from China, had significant negative effects on employment and wages for low-skilled workers in affected regions. However, some European countries, though exposed to the same shock, displayed different patterns of adjustment in employment and wages. Debates persist regarding the scope of these effects, with disagreements about their magnitude, geographical reach (local vs. national), and how they interact with other factors, such as automation or minimum wage policies (Bloom et al., 2016; Autor et al., 2017).

Such heterogeneity strongly suggests the existence of a third mechanism—beyond trade and technology—that either buffers or amplifies these shocks: the institutional environment. Yet, this dimension has long been marginalized in SBTC-dominated discussions. This underscores the need for a deeper understanding of the mechanisms through which import competition and labor market characteristics interact to affect income inequality.

2.3. Differences in Labor Market Flexibility and Adjustment to Trade Shocks

According to the Ricardian model (Costinot, 2009), countries with rigid labor markets may have a comparative advantage in “contract-intensive” industries, but they face higher adjustment costs in response to trade liberalization. Flexible labor markets, by contrast, are better able to absorb trade shocks, alleviating the long-term harm to workers (Rodrik, 1998). Several cross-country studies have found that in European countries with more rigid labor markets—characterized by strict employment protection legislation (EPL) and strong union power, the impact of trade liberalization on the skill premium is weaker than that in the U.S., which has a more flexible labor market (Gebel & Giesecke, 2011). This suggests that regions with more rigid labor markets tend to experience slower wage and employment adjustments (Felbermayr et al., 2012). Strict institutional protections in the labor market safeguard the wages of incumbent workers, potentially at the expense of outsiders (the unemployed and job seekers), whose employment opportunities are limited.

Theoretically, a flexible labor market can help an economy better absorb shocks, including those induced by trade. A flexible labor market allows workers to move easily between industries, facilitating labor reallocation and reducing unemployment rates in the face of trade liberalization (Almeida & Poole, 2017; Baensch et al., 2019; Selwaness & Zaki, 2019). However, labor market flexibility can also amplify the negative effects of import competition. With a flexible labor market, firms can easily hire and fire workers and adjust wages. As a result, they may prefer to dismiss low-skilled workers rather than retrain them, exacerbating the negative impact of import competition on low-skilled workers’ wages and employment.

However, these studies often suggest that labor market flexibility plays a significant role in mediating the effects of trade but do not directly test this hypothesis (Zhang et al., 2018).

2.4. Synthesis of Existing Research

Despite the significant literature examining the impact of import competition on skill premium, there still remain several critical gaps. First, most existing studies have overlooked the heterogeneity of labor market flexibility across regions, treating the labor market as being flexible. The existing literature has clearly identified two primary pathways through which “import competition” influences the “skill premium”: one is the price pass-through mechanism, and the other is the SBTC mechanism. However, both of them are based on the premise that the labor market is completely flexible and labor can flow freely. A “one-size-fits-all” conclusion is usually offered as a result. Actually, the labor market is not sufficiently flexible, particularly in China, where labor mobility across regions is limited by the “dual economy” and the residence registration system. This oversight can lead to inaccurate estimates of the impact of import competition on wage disparities. Second, the existing research fails to account for the nonlinear effects of import competition on the skill premium. The relationship between import competition and the skill premium is not necessarily uniform across different regions with heterogeneous labor market flexibility, which makes it essential to adopt models that capture these nonlinear dynamics. This research addresses these shortcomings by examining the threshold effect of labor market flexibility, providing new insights into how different labor markets moderate the effects of import competition on the skill premium. Finally, much of the existing research focuses on developed economies, with little attention paid to the developing countries and other emerging trading powers, which limits the generality of the theory. This study focuses on the effect of import competition in China, offering a more nuanced understanding of how labor market flexibility moderates these effects.

The marginal contribution of this study can be articulated in three respects. First, in contrast to Autor et al. (2013), who only document the consequences of import competition from China on labor market of the U.S., and Zhang et al. (2018), who examines the labor market flexibility in China using linear models, our paper explicitly integrates labor market flexibility into how import competition affects the skill premium by treating labor market flexibility as a threshold factor. We demonstrate how the labor market institution shapes the inequality effects of trade, thereby extending the literature beyond studies that focus solely on the effect of import competition or labor market flexibility. Second, we shed light on the mechanisms through which import competition influences the skill premium, highlighting the differences in the regional labor market. This adds a new dimension to the policy debate on aligning trade liberalization with labor market reform in developing economies. Third, methodologically, we move beyond standard linear specifications by employing a dynamic panel threshold model with instrumental variables. This approach not only addresses endogeneity concerns but also captures nonlinearities in the relationship between trade shocks and labor market outcomes, offering a more accurate and nuanced understanding of income distribution dynamics across provinces with varying labor market flexibility.

3. Theoretical Analysis and Research Hypothesis

3.1. Import Competition and the Skill Premium

Assuming the country is an importer and has an abundance of unskilled labor and a scarcity of skilled labor, the classical theory of factor endowments suggests that the country imports skill-intensive products and exports unskilled labor-intensive products. The output of the country’s import-competing industries is Y, represented as Equation (1) by a Cobb–Douglas (CD) production function of capital (K) and a CES combination of skilled labor (H) and unskilled labor (L):

$$Y={\mathrm{K}}^{\tau }{\left\{{\left[H^{{\displaystyle \frac{\sigma -1}{\sigma }}}+L^{{\displaystyle \frac{\sigma -1}{\sigma }}}\right]}^{{\displaystyle \frac{\sigma }{\sigma -1}}}\right\}}^{1-\tau }={\mathrm{K}}^{\tau }{\left[H^{{\displaystyle \frac{\sigma -1}{\sigma }}}+L^{{\displaystyle \frac{\sigma -1}{\sigma }}}\right]}^{{\displaystyle \frac{\sigma (1-\tau )}{\sigma -1}}}$$

(1)

where $\tau$ and $1-\tau$ denote the shares of capital and labor in total output, respectively. $\sigma$ represents the elasticity of substitution between skilled and unskilled labor, with $\sigma >0$.

First, based on Equation (1), the unit cost minimization problem of an industry can be expressed as follows:

$$\left\{\begin{array}{l}\mathrm{m}\mathrm{i}\mathrm{n}c\left(\mathrm{R},W_H,W_L\right)=R{\alpha }_K+W_H{\alpha }_H+W_L{\alpha }_L\\ \mathrm{s}.\mathrm{t}.\mathrm{Y}={\alpha }_K^{\tau }{\left[{\alpha }_H^{{\displaystyle \frac{\sigma -1}{\sigma }}}+{\alpha }_L^{{\displaystyle \frac{\sigma -1}{\sigma }}}\right]}^{{\displaystyle \frac{\sigma (1-\tau )}{\sigma -1}}}=1\end{array}\right.$$

(2)

In Equation (2), R, W_H, and W_L represent the wages of capital, skilled labor, and unskilled labor, respectively. ${\alpha }_i$, $i=K,H,L$, denote the input of the i-th factor per unit of output in the industry. To solve this problem, the Lagrangian function is constructed as follows:

$$L\left({\alpha }_K,{\alpha }_H,{\alpha }_L,\lambda \right)=\mathrm{R}{\alpha }_K+{\mathrm{W}}_H{\alpha }_H+W_L{\alpha }_L-\lambda \left\{{\alpha }_K^{\tau }{\left[{\alpha }_H^{{\displaystyle \frac{\sigma -1}{\sigma }}}+{\alpha }_L^{{\displaystyle \frac{\sigma -1}{\sigma }}}\right]}^{{\displaystyle \frac{\sigma (1-\tau )}{\sigma -1}}}-1\right\}$$

(3)

where $\lambda$ is the Lagrange multiplier. Equation (3) finds the F.O.C to ${\alpha }_i,i=K,H,L$, yielding the following:

$$\tau {\alpha }_K^{\tau -1}{\left[{\alpha }_H^{{\displaystyle \frac{\sigma -1}{\sigma }}}+{\alpha }_L^{{\displaystyle \frac{\sigma -1}{\sigma }}}\right]}^{{\displaystyle \frac{\sigma (1-z)}{\sigma -1}}}=\mathrm{R}/\lambda$$

(4)

$$(1-\tau ){\alpha }_K^{\tau }{\left[{\alpha }_H^{{\displaystyle \frac{\sigma -1}{\sigma }}}+{\alpha }_L^{{\displaystyle \frac{\sigma -1}{\sigma }}}\right]}^{{\displaystyle \frac{1-\sigma \tau }{\sigma -1}}}{\alpha }_H^{-{\displaystyle \frac{1}{\sigma }}}=W_H/\lambda$$

(5)

$$(1-\tau ){\alpha }_K^{\tau }{\left[{\alpha }_H^{{\displaystyle \frac{\sigma -1}{\sigma }}}+{\alpha }_L^{{\displaystyle \frac{\sigma -1}{\sigma }}}\right]}^{{\displaystyle \frac{1-\sigma z}{\sigma -1}}}{\alpha }_L^{-{\displaystyle \frac{1}{\sigma }}}=W_L/\lambda$$

(6)

Record $W=W_H/W_L$ (skill premium indicator). Based on Equations (4) and (5), we have

$${\displaystyle \frac{\left(5\right)}{\left(6\right)}}\Rightarrow {\displaystyle \frac{{\alpha }_H}{{\alpha }_L}}={\left({\displaystyle \frac{W_H}{W_L}}\right)}^{-\sigma }=(\omega {)}^{-\sigma }\Rightarrow {\alpha }_H=W^{-\sigma }\cdot {\alpha }_L$$

(7)

$${\displaystyle \frac{\left(4\right)}{\left(5\right)+\left(6\right)}}\Rightarrow {\displaystyle \frac{1-\tau }{\tau }}\cdot {\displaystyle \frac{{\alpha }_K\left({\alpha }_H^{-\frac{1}{\sigma }}+{\alpha }_L^{-\frac{1}{\sigma }}\right)}{{\alpha }_H^{\frac{\sigma -1}{\sigma }}+{\alpha }_L^{\frac{\sigma -1}{\sigma }}}}={\displaystyle \frac{W_H+W_L}{R}}$$

(8)

Substituting (7) into (8) yields the following:

$${\displaystyle \frac{1-\tau }{\tau }}\cdot {\displaystyle \frac{1+\omega }{1+{\omega }^{-\sigma }}}\cdot {\displaystyle \frac{{\alpha }_K}{{\alpha }_L}}={\displaystyle \frac{W_H+W_L}{R}}={\displaystyle \frac{W_L}{R}}(1+\omega )\Rightarrow {\displaystyle \frac{{\alpha }_K}{{\alpha }_L}}={\displaystyle \frac{\tau }{1-\tau }}\cdot {\displaystyle \frac{W_L}{\mathrm{R}}}\left(1+W^{1-\sigma }\right)$$

(9)

Second, the relationship between factor inputs and commodity prices is derived by solving the profit maximization problem. Given the price P and the production function Y, the vendor’s profit function is as follows:

$$\pi (K,H,L)=P\cdot K^{\tau }{\left[H^{{\displaystyle \frac{\sigma -1}{\sigma }}}+L^{{\displaystyle \frac{\sigma -1}{\sigma }}}\right]}^{{\displaystyle \frac{\sigma (1-\tau )}{\sigma -1}}}-\left(\mathrm{R}K+W_H\cdot H+W_L\cdot L\right)$$

(10)

Solving for the profit maximization problem by finding the F.O.C, to $K,H,L$, yields the following:

$$\tau P{K}^{\tau -1}{\left[H^{{\displaystyle \frac{\sigma -1}{\sigma }}}+L^{{\displaystyle \frac{\sigma -1}{\sigma }}}\right]}^{{\displaystyle \frac{\sigma (1-\tau )}{\sigma -1}}}-\mathrm{R}=0$$

(11)

$$(1-\tau )P{K}^{\tau }{\left[H^{{\displaystyle \frac{\sigma -1}{\sigma }}}+L^{{\displaystyle \frac{\sigma -1}{\sigma }}}\right]}^{{\displaystyle \frac{1-\sigma \tau }{\sigma -1}}}{H}^{-{\displaystyle \frac{1}{\sigma }}}-W_H=0$$

(12)

$$(1-\tau )P{K}^Z{\left[H^{{\displaystyle \frac{\sigma -1}{\sigma }}}+L^{{\displaystyle \frac{\sigma -1}{\sigma }}}\right]}^{{\displaystyle \frac{1-\sigma \tau }{\sigma -1}}}{L}^{-{\displaystyle \frac{1}{\sigma }}}-W_L=0$$

(13)

Based on Equations (11)–(13), we have

$${\displaystyle \frac{\left(12\right)}{\left(13\right)}}\Rightarrow {\displaystyle \frac{H}{L}}={\left({\displaystyle \frac{W_H}{W_L}}\right)}^{-\sigma }=W^{-\sigma }\Rightarrow H=W^{-\sigma }L$$

(14)

Substituting Equation (14) into Equation (10) yields the following:

$${\displaystyle \frac{K}{L}}={\left({\displaystyle \frac{\mathrm{R}}{\tau }}\right)}^{{\displaystyle \frac{1}{\tau -1}}}{P}^{{\displaystyle \frac{1}{1-\tau }}}{\left(W^{1-\sigma }+1\right)}^{{\displaystyle \frac{\sigma }{\sigma -1}}}$$

(15)

The production function Y is constant return to scale, which implies that

$${\displaystyle \frac{K}{L}}={\displaystyle \frac{{\alpha }_K\cdot Y}{{\alpha }_L\cdot Y}}={\displaystyle \frac{{\alpha }_K}{{\alpha }_L}}$$

(16)

Therefore, Equation (9) is equal to Equation (15). Simplification and taking the logarithm yield the following:

$$\begin{array}{c}\ln W={\displaystyle \frac{{\left(\sigma -1\right)}^2}{\tau -1}}\ln P+(\sigma -1)\ln \left[{\displaystyle \frac{W_L}{1-\tau }}{\left({\displaystyle \frac{R}{\tau }}\right)}^{{\displaystyle \frac{\tau }{1-\tau }}}-1\right]\end{array}$$

(17)

Substituting Equation (14) into Equation (17) yields

$$\ln W={\displaystyle \frac{{\left(\sigma -1\right)}^2}{\tau -1}}\ln P+{\displaystyle \frac{\left(\sigma -1\right)}{\sigma }}\ln {\displaystyle \frac{H}{L}}+\left(\sigma -1\right)\ln {\displaystyle \frac{W_H}{1-\tau }}{\left({\displaystyle \frac{R}{\tau }}\right)}^{{\displaystyle \frac{\tau }{1-\tau }}}$$

(18)

Let $\left(\sigma -1\right)\ln {\displaystyle \frac{W_H}{1-\tau }}{\left({\displaystyle \frac{R}{\tau }}\right)}^{{\displaystyle \frac{\tau }{1-\tau }}}=\vartheta$, then it reduces to

$$\ln W={\displaystyle \frac{{\left(\sigma -1\right)}^2}{1-\tau }}\ln {\displaystyle \frac{1}{P}}+{\displaystyle \frac{\left(\sigma -1\right)}{\sigma }}\ln {\displaystyle \frac{H}{L}}+\vartheta$$

(19)

In Formula (19), ${\displaystyle \frac{{\left(\sigma -1\right)}^2}{1-\tau }}$ represents the impact of import competition on the skill premium. Since $0<\tau <1$, ${\displaystyle \frac{{\left(\sigma -1\right)}^2}{1-\tau }}>o$, Equation (19) suggests a positive effect of import competition on skill premium without considering the flexibility of the labor market. Import competition is mainly manifested in the tariff reduction brought by trade liberalization, which leads to the reduction in the price of similar products. In order to maintain competitiveness, technological upgrading and adopting more efficient production methods are important ways to reduce costs, which will increase the demand for high-skilled labor by enterprises, bring down the wages of low-skilled labor intensively used in consumption and production, and thus promote the increase in skill premium. Therefore, we put forward Hypothesis 1.

H1. 

Regardless of the labor market flexibility, import competition intensifies the skill premium and exacerbates the wage gap between skilled workers and unskilled workers.

3.2. Dynamic Threshold Effect of Labor Market Flexibility

The impact of import competition on skill premium relies on the free flow of labor between regions. In developing countries, there exists immobility of labor between industries and regions, and the labor market is always inflexible, which increases the possibility of a skill premium (Wacziarg & Wallack, 2004). Regions with relatively flexible labor markets—characterized by higher labor mobility, more dynamic wage-setting mechanisms, and lower institutional barriers to employment adjustment—tend to absorb trade shocks more efficiently. In such settings, workers displaced from import-competing sectors can easily transit into expanding industries. As a result, the pressure of import competition on the relative wages of skilled versus unskilled labor is attenuated, leading to a smaller increase in the skill premium. Conversely, in regions where labor markets are rigid, with lower worker mobility and stronger institutional constraints, trade shocks amplify wage disparities. Skilled labor captures the gains from structural upgrading, while unskilled workers face greater displacement and downward wage pressure, thereby widening the skill premium (Goldberg & Pavcnik, 2007; Autor et al., 2013).

According to the literature, labor market flexibility (F) is primarily linked to industrial restructuring, industrial transfer, labor demand, and changes in the distribution of income among workers. In the context of the urban–rural dual system in China, labor market flexibility has a more pronounced effect on low-skilled labor. A flexible labor market can efficiently match the supply of and demand for labor and especially reduce unemployment and job vacancies of low-skilled laborers, thereby increasing their wages and narrowing the skill premium. We incorporate labor market flexibility (F) into the relative demand for skilled labor (H/L). The equation is structured as follows:

$${\displaystyle \frac{H}{L}}=W^{-a_{1}F}{z}^{a_2}$$

(20)

where the relative demand for skilled labor (H/L) is influenced by the skill price ratio W_H/W_L, or the skill premium (W), as well as other factors (Z). The effect of skill premium (W) on the relative demand for skilled labor depends on labor market flexibility (F). $-a_{1}F$ is the skill premium elasticity, and $a_2$ represents the elasticity of other factors (Z). By substituting the logarithmic form of Equation (20) into Equation (19) and simplifying, we obtain the following expression:

$$\ln W={\displaystyle \frac{{\sigma \left(\sigma -1\right)}^2}{1-\tau }}{\displaystyle \frac{1}{\sigma -a_1\left(1-\sigma \right)F}}\ln {\displaystyle \frac{1}{P}}+{\displaystyle \frac{a_2\left(\sigma -1\right)}{\sigma -a_1\left(\sigma -1\right)F}}\ln \mathrm{Z}+{\displaystyle \frac{\vartheta \sigma }{a_1\left(1-\sigma \right)F+\sigma }}$$

(21)

According to Equation (21), when labor market flexibility (F) is incorporated into the model examining the effect of import competition on skill premium, the coefficient representing the impact of import competition on the skill premium is ${\displaystyle \frac{{\sigma \left(\sigma -1\right)}^2}{1-\tau }}{\displaystyle \frac{1}{\sigma -a_1\left(1-\sigma \right)F}}$. Given that $\sigma >0$ and $1-\tau >0$, the impact of import competition on the skill premium depends on $\sigma -a_1\left(1-\sigma \right)F$. The following can be observed:

When $\sigma -a_1\left(1-\sigma \right)F>0,F<{\displaystyle \frac{\sigma }{a_1\left(1-\sigma \right)}}$, the coefficient of the impact of import competition on the skill premium is positive, indicating that import competition has an enhancing effect on the skill premium. Conversely, when $\sigma -a_1\left(\sigma -1\right)F<0,F>{\displaystyle \frac{\sigma }{a_1\left(1-\sigma \right)}}$, the coefficient is negative, suggesting that import competition has a narrowing effect on the skill premium.

Labor market flexibility (F) divides the impact of import competition on the skill premium into two distinct functions, indicating the existence of a labor market flexibility threshold for this effect. The threshold value is given by ${\displaystyle \frac{\sigma }{a_1\left(1-\sigma \right)}}$.

According to the theoretical framework, the effect of import competition on skill premium exhibits a threshold effect driven by labor market flexibility. In the case where labor market flexibility is less than the threshold value, import competition promotes a skill premium. This is because limited labor mobility restricts the ability of low-skilled workers, more than the high-skilled ones, to transit between sectors or regions, leading to an excess supply of low-skilled labor. Consequently, the wage gap between skilled and unskilled workers widens.

Conversely, when labor market flexibility is high, workers can easily adjust to shifts in labor demand resulting from increased import competition. This greater flexibility allows for a more efficient allocation of labor across industries, particularly mitigating the surplus of low-skilled labor and reducing the upward pressure on the skill premium. Moreover, in a flexible labor market, firms are better equipped to integrate technological advancements that complement unskilled labor, further narrowing the wage gap. Therefore, we propose Hypothesis 2:

H2. 

The effect of import competition on the skill premium exhibits a threshold effect driven by labor market flexibility. When labor market flexibility exceeds the threshold, higher flexibility can mitigate the increase in the skill premium caused by import competition.

4. Methodology

4.1. Econometric Modeling

According to the above theoretical analysis, we first establish the following model as the baseline to test the effect of import competition on skill premium:

$$W_{it}={\alpha }_0+{\alpha }_{1}IP{W}_{it}+{\alpha }_2{\mathrm{X}}_{it}+{\mathsf{\phi }}_i+{\phi }_t+{\epsilon }_{it}$$

(22)

Second, due to wage rigidity, the change in skill premium may be lagged. Therefore, the lagged term of the skill premium ($W_{i,t-1}$), which describes the dynamic characteristics of the skill premium ($W_{i,t}$), is incorporated into the model to establish a dynamic panel model. We establish the following dynamic panel model as the robust one to test the effect of import competition on skill premium:

$$W_{it}={\beta }_0+{\beta }_1{W}_{i,t-1}{+\beta }_{2}IP{W}_{it}+{\beta }_3{\mathrm{X}}_{it}+{\mathsf{\mu }}_i+{\mathsf{\mu }}_t+{ϵ}_{it}$$

(23)

In Equations (22) and (23), $i$ denotes province, $t$ denotes time, ${\alpha }_0$ and ${\beta }_0$ are constant terms, and ${\alpha }_{\mathrm{i}}$ and ${\beta }_{\mathrm{i}}$ (i = 1, 2, 3) are the coefficients of the effect of each variable on the skill premium. $IP{W}_{it}$ is the import competition. ${\mathrm{X}}_{it}$ is a vector of controlled variables. ${\mathsf{\phi }}_i$ and ${\mathsf{\mu }}_i$ are individual fixed effects, ${\phi }_t$ and ${\mathsf{\mu }}_t$ are time fixed effects, and $\epsilon it$ and ${ϵ}_{it}$ are random error terms.

Third, in order to gain insights into the moderating role of labor market flexibility, it is taken as a threshold variable. In this article, we refer to the First-order Difference Generalized Method of Moments estimation (FD-GMM) of the dynamic panel threshold model by building the following one:

$$W_{i,t}=\left[1 x_{it}^T\right]{\varnothing }_1·\mathrm{I}\left\{q_{it}\le \gamma \right\}+\left[1 x_{it}^T\right]{\varnothing }_2·\mathrm{I}\left\{q_{it}>\gamma \right\}+e_{it}$$

(24)

where $W_{i,t}$ is the skill premium. $x_{it}^T=\left[W_{i,t-1} {xcore}_{it}^T {xcontrol}_{it}^T\right]$, and it is a vector of explanatory variables, containing the one-period lag of the skill premium $W_{i,t-1}$, the core explanatory variable ${xcore}_{it}^T$, and the control variable ${xcontrol}_{it}^T$. $\mathrm{I}$ is an indicator function, which takes the value of 1 if the condition in the parentheses is satisfied; otherwise, it takes the value of 0. $q_{it}$ is the threshold variable. γ is the unknown threshold parameter. ${\varnothing }_1$ and ${\varnothing }_2$ are the vectors of coefficients of the explanatory variables under different threshold intervals, which are classified by the threshold variable. The error term $e_{it}$ contains the individual effect, and $e_{it}={\delta }_i+V_{it}$, where ${\delta }_i$ is the unobservable individual effect, and $V_{it}$ is the random interference term.

The dynamic panel threshold model used in this study is well-suited for capturing nonlinear relationships but has some limitations, including endogeneity, model specification, and the dynamic nature of the panel data. We further used instrumental variable techniques to mitigate the endogeneity problem. To improve robustness, we perform sensitivity checks by varying the threshold specification. To control for autocorrelation, we include lags of the dependent variable as part of the model specification and use techniques, such as the Arellano–Bond estimator, to address potential biases caused by dynamic relationships in the data.

4.2. Description of Variables and Data

4.2.1. Description of Variables

(1) 

Skill premium (W)

The studies on regional skill premiums often classify workers based on their job functions. Specifically, workers engaged in scientific and technological activities are typically categorized as high-skilled labor, while those involved in production activities are defined as low-skilled labor (Shao & Liu, 2011). With reliable data availability, this method has been widely used and established in academic research in China. This paper follows this method. Thus, the skill premium (W) is expressed as follows:

$$W={\displaystyle \frac{W_H}{W_L}}$$

(25)

where W_H denotes the average wage of skilled labor, and W_L represents the average wage of unskilled labor. The average wage of skilled labor is proxied by dividing the personnel labor cost included in the intramural expenditures of R&D by the full-time equivalent (FTE) of R&D personnel in each region. For unskilled labor, the average wage is calculated by subtracting the total wages of skilled labor from the total wages of employees in urban non-private units, and dividing this residual by the number of unskilled workers, obtained as the difference between the total number of employees in urban non-private units and the number of R&D personnel.

In addition, to conduct robustness tests, we revise the measurement of the skill premium by distinguishing skilled and unskilled workers on the basis of educational attainment. Using micro-level labor market data from the China General Social Survey (CGSS), which provides detailed wage information, we classify individuals with more than a high school degree as skilled labor, while those with a high school degree or less are classified as unskilled labor. To construct the provincial-level skill premium (SP), we first compute average wages separately for skilled and unskilled workers within each province, and then aggregate them according to their region of residence. The limitation for this measurement is that the time range that meets the research criteria is only five years: 2011, 2013, 2015, 2017, and 2019, due to the CGSS database release reports every two years.

(2) 

Import Competition (IPW)

The core explanatory variable is import competition (IPW). Due to data limitations. We do not employ weighted import tariff indicators as a proxy for import competition, since the sample coverage and the variation in weighted tariffs across provinces and years are relatively small during the study period. Following Bernard et al. (2006) and Federico (2014), we measure import competition using import penetration, where a higher penetration ratio indicates stronger exposure to international competition. The calculation is defined as:

$${IPW}_{it}={\displaystyle \frac{{Imports}_{it}}{{Imports}_{it}+{Outputs}_{it}-{Exports}_{it}}}$$

(26)

Here, ${Imports}_{it}$ and ${Exports}_{it}$ are provincial trade flows obtained from the China Customs Database. ${Outputs}_{it}$ refers to gross industrial output sourced from the China National Bureau of Statistics (NBS). ${IPW}_{it}$ is the import penetration rate of each province used to measure import competition (IPW). In addition, the endogeneity issue for using import penetration rates as a measure of import competition may lead to an underestimation of the labor market effects of trade shocks. Referring to Autor et al. (2013), we construct an instrumental variable $∆{IPW}_{rt}^O$ for import competition in China using changes in U.S. imports $∆m_t^{UO}$:

$$∆{IPW}_{rt}^O={\displaystyle \frac{L_{rt-1}\times ∆m_t^{UO}}{\sum _r{L}_{rt-1}}}$$

(27)

According to this methodology, changes in total imports of the U.S. in each year are allocated to different regions r based on their employment share $L_{rt-1}/\sum _r{L}_{rt-1}$, where $L_{rt-1}$ represents the employment of region r in the lagged year t − 1.

Using changes in imports from external countries to address endogeneity issues has been widely accepted in academic circles. These external instruments allow researchers to account for global shifts in trade dynamics that influence domestic industries, while ensuring that the instruments do not directly affect the regions’ wage structures or skill premiums. Using changes in U.S. imports satisfies the key conditions for a valid instrument: relevance and exogeneity. On the one hand, the United States is the largest importer in the world, and changes in its import demand have large effects on global supply chains. Specifically, changes in U.S. imports are closely linked to China’s exports. For example, fluctuations in U.S. imports of high-tech products, consumer goods, and machinery directly impact China’s market share and competitiveness in these industries. Changes in U.S. imports influence China’s export demand, which in turn affects China’s import competition levels. Therefore, U.S. import changes are a natural and effective instrument for capturing fluctuations in import competition faced by China, reflecting shifts in global trade that directly impact China’s competitive environment. On the other hand, U.S. import changes serve as a valid instrument for China’s import competition because they satisfy the exclusion restriction: they influence China’s competitive environment through trade but do not directly affect regional skill premiums. While changes in U.S. import demand can influence China’s production and export levels, they do not directly impact labor market dynamics or skill premiums within specific regions in China. Skill premiums are primarily determined by domestic factors such as education levels, technological progress, and labor market structures, which are not directly influenced by U.S. import demand. Therefore, using U.S. import changes as an instrument allows us to avoid endogeneity issues while ensuring the instrument’s exogeneity.

Finally, drawing on Qu and Li (2023), we choose regional import volume (IM) as a substitute indicator for import competition in the robustness test. Regional import volume, defined as the total value of imports within a given region, captures the intensity of competition from global markets and the degree to which foreign goods penetrate local markets. This measure thus serves as an effective proxy for import competition, particularly well-suited for examining trade exposure at the regional level (e.g., provinces). First, it directly reflects the magnitude of foreign presence in local markets, making it possible to assess the extent of external competition domestic industries face. Second, by observing how imports expand or contract over time, this indicator provides a dynamic perspective on shifts in trade exposure, allowing researchers to evaluate both short-term shocks and longer-term structural changes. Third, at the regional level, import volume not only reveals the market share captured by foreign products but also sheds light on their broader impact on production structures, industrial upgrading, and labor market outcomes, such as employment displacement or wage adjustments. Moreover, compared to other proxies—such as tariff reductions, foreign penetration indices, or sector-specific import shares—the regional import volume has the advantage of encompassing the cumulative effect of trade liberalization, global supply chain integration, and consumer demand shifts. This holistic measure captures the real exposure of local economies to international competition without relying solely on policy-driven variables or narrowly defined sectoral data.

(3) 

Labor Market Flexibility (F)

Regarding labor market flexibility indicators (F), the empirical literature mainly includes the labor market indicators proposed by Rama and Artecona (2002), the World Bank’s labor market flexibility index, and the methodology for constructing the Employment Protection Legislation (EPL) index in the “Global Competitiveness Report.” Due to the limited scope of these indicators, Rodgers (2007) adjusted them to four indicators measuring labor market flexibility. They are, respectively, employment protection relating to the freedom for employers to hire and dismiss employees; wage flexibility, including minimum wage regulations, union activity, and the overall bargaining power of labor over wages; internal or functional flexibility mainly focused on production or dynamic efficiency, referring to the ability of companies to organize and reorganize their internal production and labor processes; and supply-side flexibility, where workers may require flexible working hours to manage work and family responsibilities or the ability to freely switch jobs. Venn (2009) refined the EPL as three sub-indicators; they are, respectively, regular employment, short-term employment, and collective dismissals, thus improving the labor market flexibility and security index.

Due to the lack of related indicator research on China’s transitional labor market, the “China Market Economy Development Report 2008” laid the foundation for labor market research. Based on this, and referencing the indicator selection methods for industry market flexibility designed by Venn (2009) and the construction methods of technical indicators by Portela (2001), China’s regional labor market flexibility index was developed. Chinese scholars Zhou and Yang (2012) borrowed the employment protection index (EPL) when constructing China’s labor market flexibility indicators. This index consists of three parts: indicators protecting regular employees from dismissal, indicators for special requirements for collective dismissals, and indicators for regular temporary employment.

The regional labor market flexibility indicator possesses distinct Chinese characteristics, as it is tailored to China’s specific social, economic, and cultural context. It has been widely recognized and applied in the Chinese academic community, and a growing body of research in China has demonstrated the applicability and validity of the labor market flexibility index in various contexts. Jiang et al. (2025) showed that higher flexibility improves firms’ adaptability to technological change. Zhang et al. (2018) found that it facilitates export upgrading by enhancing the technological sophistication of exports. Li et al. (2017) reported that FDI inflows tend to reduce labor market flexibility, with pronounced regional disparities. Zhang et al. (2013) further confirmed that trade liberalization enhances labor market flexibility. Together, these studies provide strong empirical support for the index’s reliability and underscore its broad applicability in analyzing labor market dynamics and economic development in China.

Therefore, we follow the method proposed by Zhou Shen and Yang Hongyan and update the data to construct the regional labor market flexibility indicators as follows:

$${\mathrm{F}}_{\mathrm{i}\mathrm{t}}=\left(0.5+{\displaystyle \frac{\mathrm{e}\mathrm{x}\mathrm{p}\left(f_{1,it}\right)}{1+\mathrm{e}\mathrm{x}\mathrm{p}\left(f_{1,it}\right)}}\right)\ast \left(0.5+{\displaystyle \frac{\mathrm{e}\mathrm{x}\mathrm{p}\left(f_{2,it}\right)}{1+\mathrm{e}\mathrm{x}\mathrm{p}\left(f_{2,it}\right)}}\right)\ast \left(0.5+{\displaystyle \frac{\mathrm{e}\mathrm{x}\mathrm{p}\left(f_{3,it}\right)}{1+\mathrm{e}\mathrm{x}\mathrm{p}\left(f_{3,it}\right)}}\right)$$

(28)

In Equation (28), the natural logarithmic form and a 0.5 correction were used to ensure that each indicator falls between 0.5 (when the indicator is infinitesimally small) and 1.5 (when the indicator is infinitely large). Additionally, the logarithmic distribution ensures that the indicators fluctuate around the median.

F_it refers to the labor market flexibility in province i at time t, used to measure the labor market flexibility indicator (F). $f_{1,it}$ represents the share of labor’s net income in total wage income, which reflects the degree of wage flexibility in the dual labor market. A higher value of this indicator indicates greater wage self-determination and flexibility within the dual labor market.

$f_{2,it}$ denotes the share of employment in non-state enterprises as a percentage of total employment in region i during year t, similar to the temporary employment indicator in Employment Protection Legislation (EPL). This indicator reflects the degree of employment flexibility and primarily captures the market-oriented reforms of China’s labor market. Non-state enterprises hire labor based on the supply and demand of the labor market and the market criterion of maximizing enterprise profits. A higher value of this index indicates greater flexibility in labor employment within enterprises.

$f_{3,it}$ is the unemployment rate, which serves as an indicator of employment freedom within the labor market. A higher volatility of this rate suggests greater flexibility in the labor market, indicating that enterprises have more freedom to hire and dismiss employees.

(4) 

The Controlled Variables (X_it)

${\mathrm{X}}_{it}$ is a vector of the controlled variables. With reference to the relevant theory of skill premium determination, we include the following controlled variables:

Total Factor Productivity (TFP) is measured using the DEA-Malmquist productivity index. Three indicators are used in this measurement. They are provincial gross output, labor, and physical capital stock. Gross output is measured by provincial real GDP, deflated by the GDP deflator with 2010 as the base year. Labor is measured by the number of workers employed at the end of the year in the region, while physical capital stock is calculated using the perpetual inventory method.

Skill Intensity (H/L) is measured using the ratio of skilled labor to unskilled labor employed at the end of each year in each province.

Foreign Direct Investment (FDI) is measured using the foreign direct investment in each province.

Economic Development (GDP) is captured using the real GDP of each region for each year, adjusted for inflation based on the GDP deflator with 2010 as the base year.

Human Capital (HC). Considering that the development of human capital in China is heavily influenced by government financial input, we measure it using the proportion of national financial education funds relative to total financial expenditure.

4.2.2. Data

The relevant data are sourced from the China Science and Technology Statistical Yearbook, China National Bureau of Statistics (NBS), China Labor Statistical Yearbook, The China Population and Employment Statistical Yearbook, China High-tech Industry Statistical Yearbook, China Statistical Yearbook, China Education Expenditure Statistical Yearbook, China Foreign Investment Report, provincial statistical yearbooks and statistical bulletins, the Compilation of Statistical Data on New China for 50 Years, as well as the National Bureau of Statistics database, the China Economic Net database, the CSMAR database, and the UN Comtrade Database.

For missing values in the raw data, linear interpolation was employed to fill in the gaps. To ensure a consistent and balanced panel dataset, the sample period was set to 2010–2019. Due to challenges in collecting and verifying data for the Tibet Autonomous Region, Hong Kong, and Macau, these regions were excluded from the sample analysis. The study focuses on the remaining 30 provinces. All collected data were thoroughly cleaned and processed.

Table 1. Description of variables.

Variable	Obs	Mean	Std. Dev.	Min	Max.
W	300	1.5521	0.2706	0.7722	2.3720
IPW	300	0.1350	0.1526	0.0049	0.6947
F	300	1.3014	0.0339	1.1880	1.3610
FDI	300	3.6422	1.6583	−3.1100	5.8794
GDP	300	2.7728	0.8769	0.2042	4.6133
TFP	300	1.2683	0.2396	1.0000	2.3362
HC	300	−1.7273	0.1637	−2.2003	−1.0704
H/L	300	0.0186	0.0095	0.0060	0.0542

provides a description of the variables used.

5. Empirical Results

5.1. Analysis of Import Competition on Skill Premium

5.1.1. Baseline Results

We conducted a Hausman test (Appendix A,

Table A1. Hausman test.

Test Summary	Chi-Sq. Statistic	Degrees of Freedom	Prob.
Cross-section random	49.16	7	0.000

Note: The null hypothesis of the Hausman test is that the preferred model is random effects vs. the alternative that the model is fixed effects. A significant p-value (p < 0.05) leads to the rejection of the null hypothesis. The test results (χ² = 49.16, p < 0.001) indicate that the fixed effects model is more appropriate than the random effects model.

). The results indicate that the model with fixed effects provides consistent and efficient estimates. Consequently, the subsequent empirical analysis is based on the fixed-effects specification.

Table 2. Baseline result.

	(1)	(2)	(3)	(4)
IPW	0.4048 **	0.3087 ***	0.2593 **	0.1739 **
	(0.0998)	(0.1561)	(0.1092)	(0.2005)
Constant	1.4907 ***	1.6184 ***	1.5114 ***	2.5256 ***
	(0.0200)	(0.1029)	(0.0680)	(0.2336)
Controls	NO	NO	YES	YES
Province FE	NO	YES	NO	YES
Year FE	NO	YES	NO	YES
R2	0.052	0.720	0.312	0.770

Note: Standard errors in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1.

reports the baseline regression results for the effect of import competition (IPW) on the skill premium (W), all detailed regression results are shown in

Table A3. Baseline result.

	(1)	(2)	(3)	(4)
IPW	0.4048 ***	0. 3087 ***	0.2593 **	0.1739 **
	(0.0998)	(0.1561)	(0.1092)	(0.2005)
FDI			0.0007 **	0.0003 **
			(0.0002)	(0.0002)
GDP			0.0092 *	0.0079 *
			(0.0046)	(0.0047)
TFP			−0.1187 *	−0.1194 **
			(0.0564)	(0.0423)
HC			0.6480 *	0.8734 *
			(0.3074)	(0.4464)
HL			−9.1466 ***	−16.3781 ***
			(0.8941)	(3.6921)
Constant	1.4907 ***	1.6184 ***	1.5114 ***	2.5256 ***
	(0.0200)	(0.1029)	(0.0680)	(0.2336)
Province FE	NO	YES	NO	YES
Year FE	NO	YES	NO	YES
R2	0.052	0.720	0.312	0.770

Note: Standard errors in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1.

of Appendix A. In column (1), without any fixed effects or controlled variables, the coefficient of IPW is 0.4048 and statistically significant at the 5% level. This suggests that a 1% increase in import competition is associated with an approximately 0.40 percentage point increase in the skill premium, providing preliminary evidence that import competition promotes the skill premium between skilled and unskilled labor. In column (2), after incorporating both province and year fixed effects, the coefficient of IPW remains positive and highly significant (0.3087, p < 0.01). The explanatory power of the model increases substantially (R² = 0.720), suggesting that controlling for unobserved heterogeneity across provinces and time significantly improves the fit of the model while reinforcing the robustness of the positive association between import competition and the skill premium. Column (3) introduces control variables (excluding fixed effects). The coefficient of IPW remains positive and significant (0.2593, p < 0.05), indicating that the positive relationship is not driven by omitted variable bias related to observable covariates. Finally, column (4), which includes both control variables and fixed effects, provides the most rigorous specification. The coefficient of IPW is still positive and statistically significant (0.1739, p < 0.05), though the magnitude is smaller than in earlier specifications. This attenuation suggests that part of the effect in simpler models may have been overstated due to unobserved heterogeneity, but the persistence of statistical significance confirms the robustness of the finding. Moreover, the R² rises to 0.770, indicating strong explanatory power once both controlled variables and fixed effects are accounted for.

These findings are consistent with the theoretical framework and provide empirical support for Hypothesis 1. As import competition intensifies, firms face downward pressure on mark-ups and are compelled to innovate, which increases the demand for skilled labor and contributes to a higher skill premium. In addition, differences in intra-regional labor mobility between skilled and unskilled labor provide another possible explanation. Due to knowledge and skill mismatches, unskilled workers are largely confined to employment in manufacturing sectors producing final goods, whereas skilled workers have the option of participating not only in production but also in R&D activities that drive innovation (Aghion et al., 2014). Import competition, therefore, increases the skill premium by allowing skilled labor to shift from routine production to innovation-related sectors, while unskilled labor remains constrained to production activities and is often forced to accept lower wages or unemployment.

The subsequent section provides a detailed analysis of the mechanism channels—specifically regional innovation and industrial upgrading—as well as the moderating role of labor market flexibility in shaping the adjustment and reallocation dynamics that determine the extent to which import competition affects skill premium.

5.1.2. Robustness Checks

To ensure the reliability of our baseline findings, we conduct a series of robustness checks using alternative specifications and estimation strategies. The results are reported in

Table 3. Robustness tests.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)
	SYSGMM	IM	SP	Winsor	q25	q50	q75
W_lag	0.7731 ***
	(0.1347)
IPW	0.1608 **	0.0855 ***	0.3492 ***	0.2770 **	0.4503 ***	0.2876 *	0.0599 *
	(0.7207)	(0.0114)	(0.6226)	(0.0859)	(0.1505)	(0.1662)	(0.1962)
Constant	1.3214 ***	1.2782 ***	2.2778 *	1.4943 ***	1.2904 ***	1.5648 ***	1.7649 ***
	(0.1148)	(0.0641)	(0.8904)	(0.0715)	(0.1379)	(0.1523)	(0.1799)
Controls	YES	YES	YES	YES	YES	YES	YES
Province FE	YES	YES	YES	YES
Year FE	YES	YES	YES	YES
Sargan test p-value	0.589
AR (1) test p-value	0.007
AR (2) test p-value	0.524
R2		0.385	0.028	0.310

Note: Standard errors in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1.

, all detailed regression results are shown in

Table A4. Robustness tests.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)
	SYSGMM	IM	SP	Winsor	q25	q50	q75
W_lag	0.7731 ***
	(0.1347)
IPW	0.1608 **	0.0855 ***	0.3492 ***	0.2770 **	0.4503 ***	0.2876 *	0.0599 *
	(0.7207)	(0.0114)	(0.6226)	(0.0859)	(0.1505)	(0.1662)	(0.1962)
FDI	0.0005 **	0.0005 **	−0.0010 **	0.0007 ***	0.0011 ***	0.0003	0.0002
	(0.0003)	(0.0002)	(0.0007)	(0.0002)	(0.0004)	(0.0004)	(0.0005)
GDP	0.0100 ***	0.0047 **	0.0062 ***	0.0070 ***	0.0057 ***	0.0108 ***	0.0123 ***
	(0.0029)	(0.0018)	(0.0048)	(0.0016)	(0.0016)	(0.0018)	(0.0021)
TFP	−0.0229 *	−0.0738 *	−0.1919 *	−0.1183 *	−0.0817	−0.1594 **	−0.1397 *
	(0.0592)	(0.0555)	(0.3751)	(0.0570)	(0.0700)	(0.0774)	(0.0913)
HC	0.8333 *	0.0522 *	0.1875 *	0.6788 *	0.5967 *	0.2792 *	0.1131 *
	(0.4746)	(0.3093)	(2.9237)	(0.3159)	(0.5907)	(0.6523)	(0.7703)
HL	−6.7016 ***	−12.9648 ***	−4.2532	−9.0300 **	−10.1129 **	−8.8863 **	−7.6227 *
	(2.4460)	(0.7837)	(5.2163)	(0.8741)	(3.0726)	(3.3931)	(4.0069)
Constant	1.3214 ***	1.2782 ***	2.2778 *	1.4943 ***	1.2904 ***	1.5648 ***	1.7649 ***
	(0.1148)	(0.0641)	(0.8904)	(0.0715)	(0.1379)	(0.1523)	(0.1799)
Province FE	YES	YES	YES	YES
Year FE	YES	YES	YES	YES
Sargan test p-value	0.589
AR (1) test p-value	0.007
AR (2) test p-value	0.524
R2		0.385	0.028	0.310

Note: Standard errors in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1.

of Appendix A.

(1) Endogeneity Test: To address potential endogeneity, the System GMM estimation is employed. This method allows for the use of lagged values of the regressors as internal instruments, mitigating problems of reverse causality, omitted variable bias, and measurement error. Compared with the difference GMM, System GMM improves efficiency by combining the equations in both levels and first differences, which enhances instrument strength.

Referring to the existing literature, this article constructs two instrumental variables: One is the lagged value of the core explanatory variable (IPW_lag). We refer to Dollar and Kraay (2003, 2004), who introduced the lag term of income as an instrumental variable in the general income determination equation. Second, we use the shift-share method to construct an instrument variable for import competition ($∆{IPW}_{rt}^O$). Finally, to assess the validity of our instrumental variable strategy, we conduct standard weak instrument and overidentification tests (Appendix A,

Table A2. Validity test of instrumental variable.

Test	Statistic	Prob.
First-stage F-statistic	15.14	-
Hansen J-test (overidentification)	1.912	0.168

). The first-stage regression yields an F-statistic of 15.4, which exceeds the conventional threshold of 10, indicating that our instruments are sufficiently strong. In addition, the Hansen J-test produces a statistic of 1.912 with a p-value of 0.168, implying that we cannot reject the null hypothesis that the instruments are exogenous and uncorrelated with the error term. These results confirm the statistical validity of our IV specification and provide confidence in the robustness of our empirical findings.

The results of the SYS-GMM estimation reported in column (1) show that the coefficient of import competition on the skill premium is positive and statistically significant at the 5 percent level. The Sargan test yields a p-value above 0.1, indicating that the chosen instruments are valid and exogenous. Moreover, while the error term exhibits first-order serial correlation [AR (1)], there is no evidence of second-order correlation [AR (2)], further supporting the consistency of the estimator. Compared with the baseline regression results, these findings confirm the robustness of the conclusion that import competition exerts a positive effect on the skill premium even after addressing potential endogeneity concerns through instrumental-variable estimation.

(2) Replacing the import competition indicators: We adopt regional import volume (IM) as a substitute for the traditional measure of import competition, and the regression results are reported in column (2). The estimates in column 2 indicate that the coefficient of import competition is 0.086 (p < 0.01) after replacing the baseline proxy with the regional import volume (IM). Although the magnitude is slightly smaller than that in the baseline, the estimate remains positive and statistically significant, which shows the robustness of the conclusion.

(3) Replace the skill premium indicators: To ensure the robustness of the results, we also used an alternative measure of skill premiums. We utilize micro labor market data from the Chinese General Social Survey (CGSS) to calculate skill premiums by educational attainment. The regression results are shown in column 3. From the estimation results, we can see that import competition still exerts a positive and significant effect, with a coefficient of 0.3492, meaning that greater import penetration increases the education-based skill premium by about 35 percent.

(4) Winsorization: To assess sensitivity to extreme observations, we winsorize the sample at the upper and lower 1% tails (column 4). The coefficient on import competition is 0.277 (p < 0.01), suggesting that the baseline results are not driven by outliers. The magnitude is close to the system GMM estimate, which highlights the robustness of the effect.

(5) Quantile regression: Drawing on Rong et al. (2020) to 25% quartile as the sample division standard, we investigate potential heterogeneity across the distribution of the skill premium by estimating quantile regressions at the 25th, 50th, and 75th percentiles (columns 5–7). The coefficients are consistently positive (0.450 at q25, 0.288 at q50, and 0.060 at q75). The effect is largest and highly significant at the lower quantile, implying that import competition exerts a stronger impact on provinces with relatively low initial skill premia. At the median and upper quantiles, the effect remains positive but diminishes in size, suggesting that the trade-induced rise in skill premium is more pronounced in regions at the lower end of the skill premium distribution.

Taken together, these robustness exercises consistently support our baseline conclusion: import competition exerts a statistically significant and economically meaningful influence on the regional skill premium. The robustness of results across alternative measures, specifications, and estimation methods underscores the credibility of our empirical findings.

5.1.3. Mechanism Tests

The empirical relationship between import competition and the skill premium can be explained through two interrelated mechanisms: regional innovation capacity and industrial structural upgrading.

First, import competition stimulates regional innovation. Trade exposure imposes competitive pressure on domestic firms, compelling them to improve efficiency, adopt new technologies, and invest in R&D. This process accelerates the diffusion of skill-biased technologies and fosters regional innovation ecosystems. As shown by Autor et al. (2013), industries exposed to import shocks will undergo substantial restructuring, and the surviving firms in them are typically more technologically advanced and skill-intensive. Similarly, Acemoglu and Autor (2011) argue that globalization interacts with skill-biased technical change, increasing the relative demand for high-skilled workers. Consequently, higher regional innovation capacity translates into greater wage differentials between skilled and unskilled labor.

Second, import competition promotes industrial structural upgrading, which operates through both industrial rationalization and industrial advancement. Industrial rationalization refers to the improved allocation of resources across industries, typically measured by the reduction in structural inefficiencies. When import penetration increases, the overall productivity rises for inefficient and low-productivity sectors contract, and resources reallocate toward more competitive industries (Topalova, 2010). Industrial advancement captures the shift in industrial composition toward higher value-added, skill-intensive sectors, such as high-technology manufacturing and modern services. This upgrading process raises the demand for educated labor and amplifies wage inequality (Feenstra & Hanson, 1999; Acemoglu et al., 2016).

We choose regional innovation levels and regional industrial structure upgrading as the mechanism variables. The econometric models are set as follows:

$${mediator}_{it}={\alpha }_0+{\alpha }_{1}IP{W}_{it}+{\alpha }_2{Control}_{it}+{\phi }_i+{\mu }_i+{\epsilon }_{it}$$

(29)

$$W_{it}={\alpha }_0+{\alpha }_{1}IP{W}_{it}+{\alpha }_2{mediator}_{it}+{\alpha }_3{Control}_{it}+{\phi }_i+{\mu }_i+{\epsilon }_{it}$$

(30)

where ${\mathrm{m}\mathrm{e}\mathrm{d}\mathrm{i}\mathrm{a}\mathrm{t}\mathrm{o}\mathrm{r}}_{\mathrm{i}\mathrm{t}}$ denotes the mechanism variables through which import competition ($IP{W}_{it}$) may affect the skill premium ($W_{it})$. Other variables are consistent with the baseline model. Model (29) tests the impact of import competition on the mechanism variables, while Model (30) tests the effect of these mechanism variables on the skill premium.

We chose the innovation index from the China Regional Innovation Capability Report to measure regional innovation. The measurement of regional industrial structure upgrading encompasses both the advancement and rationalization of the industrial structure. Following the common practice in academia, the industrial structure advancedization (ISA) is measured using the ratio of the output value of the secondary industry to that of the tertiary industry. This effectively captures the tendency of the economic structure towards servitization. Industrial structure rationalization focuses on the coordination and the rationality of resource allocation among various industries. Researchers typically use the structural deviation index to gauge industrial structure rationalization. Based on data availability and the objectivity of the calculation method, and in line with the conventional approach adopted by scholars, this paper utilizes the Theil index to measure the level of industrial structure rationalization (ISR) according to the following formula:

$${Structure}_{it}={\sum }_{i=1}^n{\displaystyle \frac{Y_i}{Y}}\mathrm{l}\mathrm{n}\left({\displaystyle \frac{Y_i}{L_i}}/{\displaystyle \frac{Y}{L}}\right)={\sum }_{i=1}^n{\displaystyle \frac{Y_i}{Y}}\mathrm{l}\mathrm{n}\left({\displaystyle \frac{Y_i}{Y}}/{\displaystyle \frac{L_i}{L}}\right)$$

(31)

In Equation (31), Y_i/Y and L_i/L represent the proportion of the output value and the employed labor force of the primary, secondary, and tertiary industries to the local gross output value and total labor force, respectively. Based on Models (29) and (30), we conducted an empirical test, and the results are reported in

Table 4. Mechanism test.

	(1)	(2)	(3)	(4)	(5)	(6)
	RI	W	ISR	W	ISA	W
IPW	0.7133 *	0.1789	2.7919 ***	0.4534 ***	0.1769 ***	0.4013 ***
	(0.3682)	(0.1117)	(0.2825)	(0.1400)	(0.0177)	(0.1411)
RI		0.1375 ***
		(0.0179)
ISR				0.0632 **
				(0.0254)
ISA						0.7024 *
						(0.4066)
Constant	2.1825 ***	1.1942 ***	1.5486 ***	1.5921 ***	0.8518 ***	2.0926 ***
	(0.3375)	(0.1089)	(0.2589)	(0.1174)	(0.0163)	(0.3638)
Controls	YES	YES	YES	YES	YES	YES
Province FE	YES	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES	YES
R2	0.497	0.430	0.612	0.325	0.585	0.318

Note: Standard errors in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1.

, all detailed regression results are shown in

Table A5. Mechanism test.

	(1)	(2)	(3)	(4)	(5)	(6)
	RI	W	ISR	W	ISA	W
IPW	0.7133 *	0.1789	2.7919 ***	0.4534 ***	0.1769 ***	0.4013 ***
	(0.3682)	(0.1117)	(0.2825)	(0.1400)	(0.0177)	(0.1411)
RI		0.1375 ***
		(0.0179)
ISR				0.0632 **
				(0.0254)
ISA						0.7024 *
						(0.4066)
FDI	0.0031 ***	0.0003 *	0.0001	0.0007 **	−0.0002 ***	0.0006 *
	(0.0010)	(0.0003)	(0.0007)	(0.0003)	(0.0000)	(0.0003)
GDP	0.0011	0.0072 ***	0.0028	0.0072 ***	0.0002	0.0072 ***
	(0.0039)	(0.0012)	(0.0030)	(0.0013)	(0.0002)	(0.0013)
TFP	−0.0172	−0.1159 **	−0.0806	−0.1234 **	0.0144 *	−0.1082 *
	(0.1714)	(0.0516)	(0.1315)	(0.0562)	(0.0083)	(0.0568)
HC	8.2591 ***	0.4569 *	2.5471 **	0.5178 *	0.3396 ***	0.4402 *
	(1.4454)	(0.4600)	(1.1090)	(0.4781)	(0.0696)	(0.4960)
HL	34.1953 **	−13.7321 ***	27.6509 ***	−7.2829 ***	2.4669 ***	−7.2973 ***
	(7.5180)	(2.3467)	(5.7686)	(2.5620)	(0.3622)	(2.6731)
Constant	2.1825 ***	1.1942 ***	1.5486 ***	1.5921 ***	0.8518 ***	2.0926 ***
	(0.3375)	(0.1089)	(0.2589)	(0.1174)	(0.0163)	(0.3638)
Province FE	YES	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES	YES
R2	0.497	0.430	0.612	0.325	0.585	0.318

Note: Standard errors in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1.

of Appendix A.

In column (1), import competition is shown to significantly enhance regional innovation capacity, with a coefficient of 0.713 (p < 0.1). Column (2) further demonstrates that regional innovation has a significantly positive effect on the skill premium (0.138, p < 0.01). Together, these results support the hypothesis that import competition raises the skill premium partly by stimulating regional innovation, which fosters skill-biased technological change and increases the demand for skilled labor.

Columns (3) and (4) examine the role of industrial structure rationalization (ISR). The results indicate that import competition significantly improves structural rationalization (2.792, p < 0.01), while ISR itself positively affects the skill premium (0.063, p < 0.05). This suggests that greater trade exposure accelerates the reallocation of resources from inefficient sectors to more productive ones, thereby increasing relative demand for skilled labor and widening the wage gap (Topalova, 2010).

Columns (5) and (6) turn to industrial structure advancement (ISA). The findings reveal that import competition significantly promotes industrial advancement (0.177, p < 0.01), while ISA also exerts a strong positive effect on the skill premium (0.702, p < 0.01). This mechanism highlights that globalization encourages the shift in regional industrial structure toward higher value-added and skill-intensive industries, thereby amplifying the relative returns to skilled labor (Feenstra & Hanson, 1999; Acemoglu et al., 2016).

In sum, the mechanism tests confirm that import competition affects the skill premium through two complementary channels: (i) enhancing regional innovation capacity, which fosters skill-biased technological change, and (ii) promoting industrial upgrading, both by rationalizing existing industrial structures and advancing toward higher value-added sectors. These findings underline that the inequality-enhancing effects of trade are not only direct but also mediated by deeper structural transformations in innovation and industrial development.

5.1.4. Regional Heterogeneity

The impact of import competition on skill premium is unlikely to be uniform across regions, particularly in such a large and economically diversified country as China. Substantial regional disparities exist in terms of industrial structure, trade openness, human capital endowment, and labor market institutions. First, industrial structure significantly affects the response to import competition. Eastern China has a diverse and advanced industrial base, while Western China relies on traditional, labor-intensive industries, limiting its ability to upgrade. Central China is transitioning to more skill-intensive sectors, leading to a moderate response. Second, innovation capacity plays a crucial role. Eastern China leads in technological innovation, enabling firms to adapt quickly. In contrast, Central and Western China face slower technological adoption, limiting their response to trade shocks. Third, labor market flexibility influences how quickly regions can adjust to changes in skill demand. Eastern China has a more flexible labor market, allowing for quicker adjustments, while Central and Western China have more rigid markets, slowing the response. Fourth, trade openness and human capital are linked in shaping regional responses. Eastern China benefits from high FDI and a skilled labor force, enhancing its ability to absorb trade shocks. In contrast, Central and Western China face lower FDI and less skilled labor, hindering their ability to fully respond to import competition. These regional disparities shape the varying responses to trade shocks, highlighting the importance of local economic conditions in determining how regions absorb the effects of import competition.

Given the regional disparities in China, we further examine whether the effect of import competition on the skill premium varies across the western, central, and eastern regions.

Table 5. Regional heterogeneity.

	West	Central	East
IPW	0.4502 ***	2.5788 ***	4.1700 ***
	(0.1449)	(0.8318)	(0.7728)
Constant	1.6814 ***	1.7016 ***	1.2595 ***
	(0.1708)	(0.2619)	(0.1972)
Controls	YES	YES	YES
Year FE	YES	YES	YES
Province FE	YES	YES	YES
R2	0.396	0.600	0.371

Note: Standard errors in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1.

reports the results, all detailed regression results are shown in

Table A6. Regional heterogeneity test.

	West	Middle	East
IPW	0.4502 ***	4.1700 ***	2.5788 ***
	(0.1449)	(0.7728)	(0.8318)
FDI	0.0001	0.0056 ***	0.0027 ***
	(0.0003)	(0.0011)	(0.0010)
GDP	0.0083 ***	−0.0092 **	−0.0035
	(0.0014)	(0.0046)	(0.0047)
TFP	−0.0949	−0.1038	−0.0318
	(0.1011)	(0.0705)	(0.1390)
HC	0.3082	0.3307	−1.1450
	(0.6203)	(0.9325)	(0.9541)
HL	−14.7048 ***	7.0029	−1.1301
	(2.6888)	(6.4954)	(5.3533)
Constant	1.6814 ***	1.2595 ***	1.7016 ***
	(0.1708)	(0.1972)	(0.2619)
R2	0.396	0.600	0.371
Year FE	Yes	Yes	Yes
Province FE	Yes	Yes	Yes

Note: Standard errors in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1.

of Appendix A.

The findings reveal substantial regional heterogeneity. The estimates confirm that import competition significantly raises the skill premium in all three regions, but the magnitude of the effect differs sharply. In western regions, the coefficient of import penetration is 0.450 (p < 0.01). Although statistically significant, the effect is small in size, consistent with the observations that less-developed regions have weaker absorptive capacity and fewer channels through which trade shocks can translate into skill upgrading. In such provinces, industrial upgrading is limited, and the expansion of skill-intensive activities is relatively slow, which constrains the impact of import competition on skill premium.

In central provinces, the coefficient increases to 2.579 (p < 0.01), suggesting a much stronger effect. This result indicates that trade exposure accelerates the restructuring of industrial composition in the central region, where many traditional, labor-intensive industries are being replaced or upgraded. As new and skill-intensive sectors expand, the demand for educated labor grows disproportionately, leading to a sharp rise in the skill premium. This finding is consistent with the hypothesis of skill-biased structural change induced by trade shocks (Acemoglu & Autor, 2011).

The eastern provinces exhibit the strongest effect, with a coefficient of 4.170 (p < 0.01). As the most globally integrated region, the eastern region benefits from higher levels of foreign direct investment, advanced technologies, and an abundant skilled-labor supply. The import competition there is more likely to spur productivity upgrading, technological diffusion, and firm-level innovation, all of which raise the relative returns to skilled labor. The result resonates with previous findings that regions more exposed to international trade tend to experience greater wage polarization and increased returns to education (Katz & Murphy, 1992; Autor et al., 2008).

Overall, the heterogeneity analysis highlights that the magnitude is highly region-specific across China. The results suggest that the central provinces are sensitive to trade shocks due to ongoing structural adjustment, while the eastern provinces—already deeply integrated into the global economy—experience the strongest upgrading effect. By contrast, the western provinces, constrained by industry structure and innovation limitations, display the weakest, but still significant, effect. These findings are consistent with the broader literature showing that the effects of trade shocks are highly context-dependent and mediated by local industrial structures.

5.2. Labor Market Flexibility Dynamic Threshold Effects

5.2.1. Preliminary Test of the Nonlinear Relationship of Labor Market Flexibility Validation

The regional heterogeneity analysis demonstrates that the impact of import competition on skill premium is uneven across provinces, with the larger effects observed in eastern and central regions and the smaller effects in the western region. Beyond structural and developmental differences, one important factor shaping these divergent outcomes is labor market flexibility.

According to the theoretical model, labor market flexibility divides the effect of import competition on the skill premium into a two-stage function, which causes the nonlinear impact of import competition on the skill premium. We divide labor market flexibility into low, medium, and high groups according to the quantiles of labor market flexibility. A preliminary test of the fitting trends of import competition and skill premium under different labor market flexibilities is conducted by plotting the scatter plots of import competition and skill premium and LOWESS fitting lines.

The results for the sample of low (0), medium (1), and high (2) labor flexibility and the total sample are shown in Figure 1. The LOWESS fitting curves reveal several important patterns.

First, the overall relationship (total panel) suggests a weakly positive but nonlinear association between import competition and the skill premium. At low levels of import penetration, the skill premium rises moderately with trade exposure, consistent with the idea that import competition initially promotes industrial upgrading and innovation, thereby raising the relative demand for skilled labor. However, as import competition intensifies beyond a certain threshold, the curve flattens and even shows signs of declining, suggesting diminishing or reversing effects on the skill premium at high exposure levels.

Second, the heterogeneity by labor market flexibility is striking. In the low-flexibility group (panel 0), the slope of the LOWESS line is steeply positive, indicating that in rigid labor markets, import competition strongly increases the skill premium. This reflects that unskilled workers are less able to move across sectors, so trade shocks disproportionately harm them while skilled workers benefit, widening the skill premium. In the medium-flexibility group (panel 1), the relationship remains positive, but the slope is flatter, suggesting that moderate labor mobility partially mitigates inequality pressures. In contrast, in the high-flexibility group (panel 2), the LOWESS curve turns slightly negative, implying that in regions with more flexible labor markets, greater import competition no longer translates into higher skill premia. Instead, adjustment costs are more evenly distributed across workers, and unskilled labor is better able to absorb trade shocks through reallocation.

Finally, these results preliminarily point to a nonlinear relationship between import competition and the skill premium that is conditional on labor market flexibility. Specifically, while import competition tends to raise the skill premium in rigid markets, its effect diminishes or even reverses as flexibility increases. This finding reveals that the globalization-induced skill premium is strongly mediated by the institutional and structural features of local labor markets.

5.2.2. Analysis of Panel Threshold Effects on Labor Market Flexibility Dynamics

We further employ the dynamic panel threshold regression model developed by Seo and Shin (2016), using labor market flexibility (F) as the threshold variable, to examine the impact of import competition on the skill premium.

Table 6. Dynamic panel threshold estimation results.

Variant	TH ≤ F	TH > F	Overall	Post-Estimate Tests
W_lag	−0.875 ***	0.357 ***	0.707 ***	Kink.	7.095 **
	(0.126)	(0.100)	(0.088)		(2.838)
IPW	1.622 *	−0.863 ***	0.911 ***	threshold value	1.328 ***
	(0.922)	(0.249)	(0.145)		(0.006)
F	−27.696 ***	−1.815	−0.371	95% CI	[1.316, 1.340]
	(8.578)	(1.774)	(0.787)	AR (1) p-value	0.012
Constant	38.511 ***			AR (2) p-value	0.708
	(11.466)			Hansen J test p-value	0.138
Controls	YES		NO	Linear test p-value	0.000

Note: The test results are obtained using a Bootstrap replicated 1000 times, *** p < 0.01, ** p < 0.05, * p < 0.1.

reports the regression results of Equation (24).

The results reveal an inverted U-shaped nonlinear relationship between import competition (IPW) and the skill premium (W), with a threshold of labor market flexibility at F = 1.328, significant at the 95% confidence interval [1.316, 1.340]. Moreover, the p-value of the linearity test is statistically significant at the 1% level, confirming the existence of a threshold effect. The p-value of the kink test is statistically significant at the 5% level, indicating the absence of a kink in the nonlinear specification. The Hansen test validates the set of instrumental variables employed in the model, while the p-values of AR (1) and AR (2) suggest no serial correlation. Taken together, these results are both robust and internally consistent.

Substantively, the findings suggest that import competition significantly raises the skill premium when labor market flexibility is below or equal to the threshold (F ≤ 1.328). Conversely, when labor market flexibility is above the threshold (F > 1.328F), import competition exerts a negative and significant effect on the skill premium. These results lend strong support to Hypothesis 2. The findings highlight the critical role of labor market flexibility in mediating the distributional consequences of globalization.

When labor market flexibility is relatively low (F ≤ 1.328F), import competition exerts a positive and significant effect on the skill premium. This outcome may be attributed to institutional rigidities in wage-setting and employment protections, which prevent downward adjustments for skilled workers, while unskilled workers absorb the majority of adjustment costs. In such environments, globalization tends to increase the relative returns to skill, thereby widening the skill premium.

Conversely, when labor market flexibility is high (F > 1.328), the marginal effect of import competition turns negative and statistically significant. In this regime, greater flexibility allows for wages, employment, and hiring practices to adjust swiftly to competitive pressures. Firms facing heightened global competition may compress wage differentials by lowering the premium for skilled workers or by substituting skilled labor with more cost-effective alternatives, such as technology or outsourcing. Moreover, under the dual urban–rural labor system in China, a higher labor market flexibility facilitates the reallocation of unskilled labor across sectors, which raises the demand for unskilled workers and contributes to narrowing the wage gap between skilled and unskilled labor.

5.2.3. Marginal Effect of Import Competition on Skill Premium

In order to test this nonlinear relationship, we make a marginal effect diagram of the impact of import competition on skill premium under the threshold effect of labor market flexibility (Figure 2). Figure 2 illustrates the marginal effect of import competition (IPW) on skill premium (W) from a single-threshold regression model. The black solid line represents the functional coefficients of IPW across varying levels of labor market flexibility (FFF). The gray shaded area denotes the 95% confidence intervals of the estimated effects. The red dashed vertical line indicates the estimated threshold value at F = 1.328, while the light red shaded area highlights the 95% confidence interval for the threshold estimate [1.316, 1.340].

The results show a clear labor market flexibility-dependent threshold effect. When F ≤ 1.328, the marginal effect of IPW on W is positive and statistically significant, with an estimated coefficient of β = 1.622 and a 95% confidence interval of [0.184, 3.429]. By contrast, when F > 1.328, the marginal effect of IPW turns negative, with an estimated coefficient of β = −0.863 and a 95% confidence interval of [−1.351, −0.376]. The contrast between the two regimes suggests a nonlinear and threshold-dependent relationship between labor market flexibility and the impact of import competition on skill premiums.

5.2.4. Key Implications of the Threshold Effects

The findings imply that, when labor market flexibility exceeds the threshold, policies aimed at further expanding imports can contribute to reducing the wage gap between skilled and unskilled workers. However, since the majority of regional labor markets in China exhibit flexibility levels below the estimated threshold (1.328), improvements in institutional design and labor market reforms remain essential.

Our threshold estimation suggests that labor market flexibility exerts a nonlinear effect, with a critical cutoff at approximately 1.33. This value is derived from the standardized labor market flexibility index, where higher scores denote more flexible institutional arrangements. In substantive terms, a level above 1.33 reflects provinces with relatively fewer regulatory barriers to labor mobility, lower hiring and dismissal costs, and less stringent employment protection, while values below the threshold correspond to more rigid institutional environments.

Importantly, this threshold is not only statistically significant but also institutionally meaningful. Observations of flexibility in the labor markets of provinces show that several provinces—particularly in the coastal and reform-oriented regions—are already close to or above this benchmark, while others remain below but within a feasible range. Recent policy initiatives, such as reforms in employment contract law, the Reform of the Household Registration System aimed at reducing regional mobility restrictions, and measures to lower non-wage labor costs, provide tangible pathways for provinces to “cross” this threshold. Thus, the estimated cutoff should not be interpreted as a purely technical finding, but rather as a policy-relevant benchmark.

From a policy perspective, this threshold has significant implications. The threshold of 1.328 serves as an important benchmark for policymakers to assess how labor market flexibility mediates the effects of import competition on skill premiums. In regions where labor market flexibility exceeds 1.328, import competition reduces the wage gap between skilled and unskilled labor, leading to a positive social impact by promoting wage equality. In such regions, policymakers should focus on maintaining or even encouraging import competition, as it contributes to a fairer distribution of wages across skill levels. Furthermore, policies could be designed to further reduce trade barriers and encourage competition, thereby helping to sustain or even enhance the positive effect on wage equality. Import competition in these regions could also be complemented by labor market support for unskilled workers to help them transition into higher-skill roles. In regions where the labor market flexibility is below 1.328, import competition increases the skill premium, widening the wage gap between skilled and unskilled labor and leading to greater inequality. In these regions, policymakers should focus on improving the labor market flexibility to allow workers to more easily transition between different industries and skills. Enhancing mobility, reducing employment rigidities, and promoting education and training programs will help mitigate the negative effects of import competition on wage inequality. Moreover, supporting structural transformation and innovation in industries can help create more skill-intensive sectors, which will encourage demand for skilled labor and reduce the wage gap over time. In summary, these results underscore the nonlinear nature of the import competition on skill premium, contingent on the context of labor market institutions. The same trade shock can yield opposite distributive outcomes depending on the degree of labor market flexibility. For policymakers, this implies that institutional reforms play a pivotal role.

6. Discussion and Policy Implications

6.1. Discussion

This study examines the impact of import competition on the skill premium, with a particular focus on how labor market flexibility mediates this relationship across provinces in China. Using panel data from 30 provinces over the period 2010–2019, we employ a dynamic panel threshold model with instrumental variables to address endogeneity concerns and uncover the nonlinear effects of import competition on skill premium. The results reveal a significant threshold effect of labor market flexibility, which plays a key role in moderating the inequality-enhancing effects of import competition.

The findings highlight that import competition operates through two main channels to promote skill premium. First, it strengthens regional innovation capacity, which raises the demand for highly skilled labor and consequently increases their relative returns. Second, import competition drives industrial upgrading and rationalization that favor skilled over unskilled workers. Together, these mechanisms suggest that globalization-induced competitive pressures are not only reshaping industrial structures but also intensifying the skill premium. Flexible labor markets enhance the mobility of workers and allow wages to adjust more efficiently, thereby cushioning the inequality effects of import competition when flexibility crosses a certain threshold.

This study makes a significant theoretical contribution by introducing labor market flexibility as a critical mediator in the relationship between import competition and skill premium. While previous research has primarily focused on the direct effects of trade on wages, this paper emphasizes the role of labor market flexibility in shaping the distributive effects of globalization. The threshold effect of labor market flexibility is a novel addition to the literature, suggesting that the impact of globalization on skill premium is not uniform across different labor markets. In addition, the paper contributes to the theory by identifying the mechanisms through which import competition affects the skill premium. Import competition increases the skill premium by stimulating regional innovation and promoting the rationalization and upgrading of the industrial structure. As these processes promote technological advancements and improve industrial efficiency, they create a higher demand for skilled labor, thus raising the relative returns to skill. However, labor market flexibility can alleviate these effects by enabling the efficient reallocation of labor and adjusting wages accordingly, helping to moderate the skill premium induced by globalization.

Empirically, this study makes a significant contribution by using a dynamic panel threshold model with instrumental variables, which helps to identify the nonlinear effects of import competition. This approach addresses endogeneity concerns, providing more reliable estimates of the relationship between import competition and skill premium. Our findings show significant regional heterogeneity: import competition has stronger effects in eastern and central regions and weaker effects in western regions, emphasizing the need to account for the regional labor market when analyzing the distributive effects of globalization. Moreover, the study demonstrates the threshold role of labor market flexibility, showing that flexibility is not merely a background but actively shapes the impact of import competition on skill premium. Our findings provide empirical evidence that labor market flexibility mitigates the inequality-enhancing effects of import competition, as it facilitates wage adjustments and labor mobility, which helps to reduce the skill premium in more flexible labor markets.

Despite its contributions, this study has several limitations. First, the dynamic panel threshold model used in this study is well-suited for capturing nonlinear relationships without comparability of coefficients before and after the threshold. This means that while the model provides valuable insights into the threshold of labor market flexibility, it cannot reflect the extent to which import competition increases or decreases the skill premium within each range of labor market flexibility. Second, the measurement of labor market flexibility may not fully capture all dimensions of flexibility, such as the extent of informal labor markets, mobility barriers, or labor force participation rates. Future studies could improve these measurements by incorporating alternative indicators such as unionization rates or labor market reforms.

Despite these limitations, this study opens several promising avenues for future research: Firstly, long-term effects research: This study focuses on the short-term effects of import competition. However, the long-term dynamics of skill premium and labor market adjustments may evolve over time. Future studies could use longitudinal data to explore how the effects of import competition unfold over an extended period and how labor markets adapt. Secondly, cross-country comparisons: Expanding this research to other developing economies would provide a broader understanding of how labor market flexibility influences the relationship between globalization and inequality. Cross-country studies would allow for a comparison of how institutional factors shape the distributive effects of globalization in different contexts. Finally, broader institutional factors: Future studies could incorporate other institutional factors, such as social protection systems, education policies, and labor market regulations, to understand the full range of factors that mediate the effects of import competition on skill premium.

6.2. Policy Implications

The findings of this study carry several important policy implications for managing the inequality effects of globalization in China and other developing economies.

First, targeted labor market reforms are essential to mitigate the inequality-enhancing effects of import competition. Our results show that import competition tends to widen the skill premium, particularly in eastern provinces where exposure to foreign markets is greatest. To address this, policymakers should strengthen active labor market policies, such as retraining programs, vocational education, and continuous upskilling initiatives, in order to improve the employability of less-skilled workers. By equipping workers with skills that align with industrial upgrading and innovation, governments can help reduce the wage gap and improve social mobility.

Second, enhancing regional innovation and industrial upgrading must be complemented with inclusive policies. While innovation capacity and industrial restructuring contribute to economic growth, they also disproportionately benefit skilled labor, exacerbating inequality. Governments should therefore design industrial and innovation policies that integrate inclusive components—for example, incentivizing firms to provide on-the-job training for lower-skilled employees, supporting technology diffusion to small and medium-sized enterprises (SMEs), and fostering broader access to innovation-driven opportunities across regions.

Third, strengthening labor market flexibility should be prioritized, but with institutional safeguards. The evidence suggests that labor market flexibility has a significant threshold effect: once flexibility surpasses the critical value of 1.330, it alleviates the inequality-widening effects of import competition by enabling smoother labor reallocation and wage adjustment. This underscores the need to promote mobility-enhancing reforms, such as reducing barriers to inter-provincial migration (e.g., easing hukou restrictions), improving labor contract flexibility, and developing efficient labor market information systems. At the same time, flexibility must be balanced with protections for vulnerable workers to avoid excessive precarious employment.

Finally, regional heterogeneity must be explicitly considered in policy design. The finding that the inequality effects of import competition decline from eastern to western regions indicates that a uniform, nationwide approach is unlikely to be effective. Instead, differentiated strategies should be adopted: in eastern regions, policy should focus on cushioning workers displaced by rapid industrial upgrading, while in central and western regions, the emphasis should be on building innovation capacity and improving workforce skills to capture the benefits of trade and technological transformation.

Taken together, these policy implications highlight the importance of a dual strategy: combining globalization-driven industrial upgrading with inclusive labor market institutions. Such an approach can balance global competitiveness with social equity, ensuring that the gains from trade and innovation are shared evenly across the workforce.