Sustainable Regional Development Under Demographic Transition: Labor Market Integration and Export Quality Enhancement in the Beijing-Tianjin-Hebei Region

Abstract

It has become a global challenge to realize sustainable regional development in the context of demographic transition. Based on the panel data of the Beijing-Tianjin-Hebei region from 2017 to 2022, this paper analyzes in depth the impact mechanism of labor market integration on export quality and its sustainable development effect by using various econometric methods. It is found that labor market integration enhances regional export quality, and every 1% increase in the integration level can bring 0.184% improvement in export quality. Mechanism analysis shows that labor market integration works mainly through two channels: innovation synergy effect (27%) and labor cost effect (8%). Heterogeneity analysis shows that the elasticity coefficients of general trade and high-income nations are 0.155 and 0.208, respectively, but the elasticity coefficients for processing trade, low-income, lower-middle-income and upper-middle-income nations are not significant. Furthermore, feature fact analysis reveals that the three regions of Beijing, Tianjin, and Hebei have varying degrees of labor market integration: Beijing (0.038) > Tianjin (0.037) > Hebei (0.034); nevertheless, the export product quality gradient is reversed: Beijing (0.617) < Tianjin (0.665) < Hebei (0.669). The evaluation of sustainable development impacts reveals that labor market integration not only mitigates internal labor shortages but also effectively counteracts the external shock of U.S. tariff increases on China. This study provides important theoretical support and policy insights for building a sustainable regional development model in the context of demographic transition.

1. Introduction

Against the backdrop of deepening globalization and regional integration, demographic transition has become a key factor impacting sustainable regional development. As the world’s second largest economy, China is facing profound demographic changes: the population aged 65 and above already accounted for 15.4% of the total population in 2023, marking China’s formal entry into a deeply aging society [1]. This demographic transition will not only have a profound impact on the labor supply structure but also pose a major challenge to the regional economic development model and foreign trade competitiveness. The Chinese government has clearly stated its intention to “implement a national strategy to actively cope with population aging” and to “promote the coordinated development of Beijing-Tianjin-Hebei”, which points to the direction of sustainable regional development in the context of the demographic transition.

As a vital engine of China’s economic growth, the Beijing-Tianjin-Hebei region reached a GDP of CNY 10.44 trillion in 2023, reflecting a year-on-year increase of 5.1% and showcasing its strong development resilience [2]. Simultaneously, as one of China’s crucial gateways to the outside world, the high-quality export growth attained in the Beijing-Tianjin-Hebei region serves as a catalyst, driving force, and pioneering model for exports in the northern hinterland and throughout the nation [3]. Since its designation as a national strategy in 2014, the coordinated development initiative for the Beijing-Tianjin-Hebei region has evolved into a pivotal platform for pioneering sustainable models of regional growth.

A crucial prerequisite for attaining sustainable regional economic development is long-term stable economic efficiency, rather than transient scale expansion [4]. Enhancing the quality of export products necessitates that enterprises invest in innovation and attract highly skilled labor, thereby enabling a transition from “low-cost competition” to “high-value-added breakthroughs” [5]. This, in turn, propels industrial transformation and upgrading through supply chain effects, fostering the emergence of an efficiency-driven economic growth model in the region. However, the Beijing-Tianjin-Hebei region faces dual challenges: the pressure of a declining labor supply and rising costs driven by rapid population aging—Beijing’s aging rate has reached 20.8%, Tianjin’s 19.5%, and Hebei’s 14.7%—alongside heightened export risks stemming from external shocks such as the U.S.–China trade war, the Russia–Ukraine conflict, and the global COVID-19 pandemic. In this context, transforming the export model rooted in “low-cost advantages” and shifting the region’s export strategy from “quantity-driven growth” to “quality-driven enhancement” has emerged as a fundamental pursuit for sustainable economic development.

Amidst the backdrop of demographic transition, enabling the free flow of labor within the market has become essential for unlocking the workforce’s efficiency dividend [6]. In January 2025, the Guidelines for Building a Unified National Market (Trial) advocated for accelerating the development of a cohesive national market through three key strategies: dismantling barriers, constructing bridges, and establishing boundaries. The policy emphasized removing institutional obstacles to talent mobility—such as geographical restrictions, identity requirements, personnel records, and employment relationships—highlighting the importance of labor integration within the unified national framework. Labor market integration entails the gradual dissolution of employment boundaries across regions, manifested in reduced barriers to labor mobility and the promotion of free cross-regional labor flows [7]. This process facilitates the rational allocation of labor resources and plays a pivotal role in preventing China’s manufacturing sector from becoming “locked into low-end production” [8].

Recent studies have extensively demonstrated that labor market integration significantly influences China’s export performance [9,10]. However, there has been limited exploration of how this integration affects the quality of exported products. Among the scarce research that has addressed this link [8], the focus has predominantly been on urban–rural labor mobility. In reality, cross-regional labor mobility resulting from labor market integration encompasses not only urban–rural flows but also interprovincial labor movements [11]. The disparities between these two forms of mobility are notable: barriers to urban–rural labor movement are chiefly rooted in the household registration system [12], whereas interprovincial mobility encounters more complex administrative obstacles, such as local protectionism and fiscal decentralization [13]. Furthermore, under the urban-rural dual economic structure, labor mobility tends to be predominantly unidirectional, flowing chiefly from rural areas to cities [14]. In contrast, interprovincial mobility is more varied, with labor crossing regions driven by local industrial disparities, resource endowments, and market demands [15]. This dynamic pattern of movement promotes a more efficient allocation of labor resources [16]. These differences may give rise to distinct effects and underlying mechanisms through which interprovincial labor mobility influences export product quality, compared to urban–rural mobility. Accordingly, this study concentrates on the impact pathway of interprovincial labor mobility on export quality, driven by labor market integration within the Beijing-Tianjin-Hebei region amid demographic shifts. Its goal is to offer empirical evidence and policy recommendations to foster sustainable regional development.

2. Literature Review

The following aspects are the primary focus of current research on the connection between export product quality and labor market integration.

Economic effects of labor market integration. In terms of economic growth, Bryan and Morten (2019) suggested that free mobility of labor can increase labor productivity by 22%, which in turn unleashes the country’s economic growth potential [17]. In contrast, Au and Henderson (2006) argue that limited labor mobility results in an inadequately sized urban market, which in turn precipitates a decline in labor productivity that directly impedes China’s economic growth [18]. In terms of regional disparities [18], Lewis (1954) posited that enhancing the labor market and facilitating the mobility of labor could accelerate economic growth in underdeveloped regions and reduce income disparities between them [19]. Li (2010) observed that restrictions on labor mobility resulted in interprovincial income disparities in China being significantly greater than those within provinces [20]. Although the government has ultimately eased restrictions on population mobility, the effects of interprovincial boundaries remain [20]. Nonetheless, Wang and Benjamin (2018) contend that while rural labor migration to urban areas has spurred economic growth, it has also led to a loss of talent in rural regions, diminished agricultural production, and intensified urban–rural income inequality [21]. In terms of export trade, Yang et al. (2023) found that the integration of urban and rural labor markets can influence export product quality by enhancing labor skills and increasing labor costs [8]. Lee and Park (2018) focused their research on South Korea, proposing that a flexible labor market can stimulate exports by lowering the adjustment costs associated with them [9]. Similarly, Fajgelbaum et al. (2020) asserted that reducing barriers to labor mobility would encourage high-productivity enterprises to expand their exports, thereby increasing overall export volume [10]. Additionally, several scholars have explored this issue through the lens of labor market segmentation. Cuñat and Melitz (2012) discovered that barriers to labor mobility not only diminish firms’ incentives for innovation but also impede the advancement of export technology complexity [22]. Helpman and Itskhoki (2010) developed a two-country international trade model, concluding that labor market segmentation results in elevated recruitment costs for firms, thereby reducing operating profits, increasing the productivity threshold needed to enter export markets, and consequently hindering the expansion of export volume [23]. Seker (2012) used firm-level statistics from 26 Eastern European and Central Asian nations to further support these theoretical conclusions [24].

Factors influencing export products’ quality. Regarding internal factors, Can and Gozgor (2018) confirmed that the level of human capital within enterprises positively influences the quality of export products, as indicated by secondary education completion rates [25]. Using the difference-in-differences (DID) method, Yue (2023) examined China’s university growth program and came to the conclusion that increasing labor capital boosts export product quality by encouraging more investment in physical assets and stimulating innovation [26]. Zhang and Duan (2023) examined Chinese listed companies and suggested that enterprise digitization may exert an inverted “U”-shaped influence on export product quality by removing financial barriers and raising levels of human capital [27]. Bas and Strauss-Kahn (2015) and Manova and Yu (2017) concluded through theoretical analysis and the development of a heterogeneous firm trade model, respectively, that the incorporation of high-quality imported intermediate goods can significantly enhance the quality of export products [28,29]. Regarding external environmental factors, Fan et al. (2015) suggested that trade liberalization can enhance market competition, thereby effectively improving the general quality of a nation’s exports [30]. Huang et al. (2020) contended that the effect of trade liberalization on the quality of Chinese exports differs depending on the type of enterprise [31]. Zhang et al. (2022) conducted further research and concluded that trade liberalization can elevate export quality by improving the caliber of intermediate goods and boosting labor productivity, with this effect being especially significant for enterprises encountering credit constraints [32]. Li et al. (2023) developed a game theory model and concluded that enhancements in the legal environment can improve the ultimate items’ quality by elevating the quality of intermediate goods, particularly in industries characterized by high contract density [33]. Zhang and Yang (2016) posited that intellectual property protection can significantly foster the upgrading of export product quality, although the pathways of its impact differ between developed and developing countries [34]. Dong et al. (2022) identified through empirical research that the level of intellectual property protection positively influences export product quality by bolstering R&D investment, promoting new product development, and alleviating financial constraints [35].

Comprehensive existing research can be found, although there are extensive research results about labor market integration, export quality improvement and sustainable development, and other issues, the following shortcomings still exist: First, the existing research is mostly from a single perspective to assess the effects of demographic change on economic development, there is a lack of research that can systematically combine demographic transition, labor market integration, and export quality enhancement into one unified analytical framework. Second, although some studies involve the impact of regional integration on economic resilience, they do not deeply reveal enough on the micro-mechanisms of how labor market integration affects export quality through the mechanism of optimal allocation of resources. Third, existing studies on sustainable development mostly focus on the environmental dimensions, and research on how to achieve a balance between internal economic efficiency and external economic sustainability against the backdrop of demographic change is relatively weak. Fourth, empirical studies on the realization of sustainable development in specific regions of China (e.g., the Beijing-Tianjin-Hebei region) in the context of demographic transition are relatively limited and lack a thorough examination of the traits of regional heterogeneity.

In light of the aforementioned research deficiencies, this paper’s primary contributions are fourfold:

First, it constructs a theoretical analytical framework to investigate the impact of labor market integration on export quality within the context of demographic transition. By using demographic transition as our focal point, we systematically elucidated the effects and underlying mechanisms through which interprovincial labor mobility, arising from labor market integration, influences export quality. This study’s empirical investigation reveals that for every 1% increase in the degree of labor market integration, regional export quality improves by 0.184% on average. After incorporating the interaction term between aging and labor market integration into the model, the study further confirmed the important role of labor market integration in the context of demographic transition. Further mechanism analysis shows that labor market integration mainly impacts export quality through two major channels: innovation synergy effect (contribution rate of about 27%), and labor cost effect (contribution rate of about 8%), which offers fresh insights into fostering sustainable economic development in the region amidst demographic transition.

Second, it utilizes data from the Beijing-Tianjin-Hebei region spanning 2017 to 2022 to conduct a heterogeneous analysis of the impact of labor market integration on export quality, differentiated by trade mode and trading partner. The findings reveal that the elasticity coefficient for general trade is 0.154 (p < 0.01), while the regression coefficient for processing trade is not statistically significant. This indicates distinct characteristics in labor transfer and industrial upgrading across various trade modes. Furthermore, the elasticity coefficient for high-income countries stands at 0.208 (p < 0.01), whereas other income levels show no significant impact. This empirical evidence supports the formulation of differentiated trade and industrial policies tailored to regional needs, assisting the Beijing-Tianjin-Hebei region in enhancing export quality and achieving sustainable economic growth during the phase of diminishing demographic dividends.

Third, it assesses and delineates the overarching trends and regional disparities in labor market integration and export quality across the three provinces by utilizing data from the Beijing-Tianjin-Hebei region spanning 2017 to 2022. The analysis reveals a hierarchical pattern in the integration index: Beijing (0.038) > Tianjin (0.037) > Hebei (0.034). Conversely, export quality displays an inverse gradient: Beijing (0.617) < Tianjin (0.665) < Hebei (0.669), highlighting a pattern of “asynchronous and uneven” development. These insights offer valuable empirical support for the formulation of tailored, region-specific policies.

Fourth, grounded in the framework of regional economic sustainability, this study systematically explores the dual functions of labor market integration in alleviating internal labor shortages and buffering external environmental shocks. The results demonstrate that labor market integration not only alleviates the pressure of declining working-age populations driven by demographic changes (with an interaction coefficient of 35.1109 between aging and labor market integration) but also mitigates the external impact of U.S. tariff escalations on China (with an interaction coefficient of 3.664). These findings emphasize the vital role of labor market integration in bolstering regional economic resilience and fostering sustainable development, offering robust empirical support for policies that harmonize internal economic efficiency with external sustainability objectives.

3. Theoretical Analysis and Research Hypotheses

In the context of an aging population, enterprises confront the challenge of declining labor productivity [36]. Those with low labor productivity risk being driven out of the market [37]. Consequently, they may abandon the domestic market in a bid for survival, opting to export instead. By competing with low product prices to secure production orders, these enterprises may superficially achieve an expansion in scale. However, this strategy is ultimately detrimental to the quality of their export products. Labor market integration can effectively mitigate this issue. On one hand, it dismantles inter-provincial barriers to labor mobility, reduces asymmetries in labor market information, and enhances the quality and efficiency of the match between enterprises and the workforce. By fully harnessing the potential of the labor force, it significantly boosts labor productivity and provides crucial assistance to businesses looking to raise the quality of their exported goods [38]. On the other hand, labor market integration facilitates the free movement of workers and effectively expands the scale of the labor market. According to “Smith’s Theorem,” this expansion promotes the division of labor and enhances specialization, ultimately leading to increased labor productivity. The quality of imported goods is subsequently raised as a result of this improvement.

Based on the above analysis, this paper proposes Hypothesis 1.

H1: 

Labor market integration positively influences the quality of export products.

Simultaneously, labor market integration is likely to influence the quality of exported products through the following transmission mechanisms:

The Innovation Synergy Effect. In the context of an aging population, the increasing proportion of older workers not only diminishes corporate R&D efficiency but also elevates pension costs, thereby constraining corporate R&D investment and impeding advancements in technological innovation [39]. Labor market integration has diminished institutional barriers to interprovincial mobility among highly skilled workers, thereby facilitating the effective reallocation of human capital [40]. Regarding factor inflow, the mobility of highly skilled labor promotes the spillover of technology, knowledge, and information but also enables workers to acquire and leverage new insights, experiences, and ideas, thereby continuously advancing technological innovation [41,42]. Furthermore, by enhancing production efficiency, it improves corporate performance, alleviating financing constraints for innovation and ultimately fostering greater innovation efficiency [43]. Regarding outflow destination, the migration of highly skilled labor may pose a short-term risk of losing regional innovation factors. However, in the long term, it can drive local technological innovation by compelling governments to optimize the innovation environment and attract talent back [44,45]. Furthermore, in the context of the digital economy, labor can leverage online virtual platforms to generate a “knowledge return” effect through remote collaboration, thereby elevating technological innovation levels. Overall, this process elevates the level of technical innovation in businesses and significantly enhances the quality of products intended for export [46].

The Labor Cost Effect. In the context of an aging population, the availability of age-appropriate labor in the market declines, leading to a scarcity of labor resources and an increase in labor costs for enterprises [47]. However, labor market integration can effectively mitigate the challenges associated with rising labor costs. On the one hand, labor market integration can effectively lower barriers to labor mobility, thereby alleviating the issue of insufficient regional labor supply. On the other hand, it accelerates the dissemination of labor market information, reduces the time required for matching employers with employees, and lowers the search costs for enterprises [48,49]. In turn, it diminishes overall labor costs for businesses and fosters improvements in the quality of export products.

In light of the aforementioned analysis, this paper proposes Hypotheses 2.

H2: 

In the context of aging, labor market integration impacts export product quality through both technological innovation and labor cost effects.

4. Materials and Methods

4.1. Model Construction

4.1.1. Three-Dimensional Fixed-Effects Model

Building on the theoretical insights and previous research outlined above, this study develops an econometric model for empirical validation. To address the issue of multidimensional omitted variables and correct potential regression biases, and in line with the theoretical foundations and prior studies, the analysis employs a three-dimensional fixed-effects model at the “product-province-year” level [50] to investigate the influence of labor market integration on export product quality:

$${Quality}_{ijt}={\alpha }_0+{\alpha }_1{Integ}_{jt}+{\alpha }_2{Controls}_{ijt}+v_i+{\mu }_j+{\gamma }_t+{\epsilon }_{ijt}$$

(1)

In Equation (1), ${Quality}_{ijt}$ denotes the explanatory variable of export product quality, and ${Integ}_{jt}$ denotes the core explanatory variable of labor market integration level. ${Controls}_{ijt}$ denotes the combination of control variables. Subscripts $i$, $j$, and $t$ refer to the individual product, province, and year, respectively. This paper adopts a three-dimensional panel fixed effects model that controls for individual, province, and year effects, with $v_i$ representing the individual fixed effects, ${\mu }_j$ representing province fixed effects, ${\gamma }_t$ representing year fixed effects, and ${\epsilon }_{ijt}$ denoting the error term.

4.1.2. Moderation Effect Regression Model

Building on the methodology of Chen et al. (2025), this study incorporates an interaction term between aging and labor market integration into the regression model [51]. This approach, widely used in academic research to examine moderation effects, enables a more nuanced analysis of how labor market integration moderates the relationship between aging and export quality in the context of an aging population.

$${Quality}_{ijt}={\alpha }_0+{\alpha }_1{Age}_{jt}+{\alpha }_2{Age}_{jt}\times {Integ}_{jt}+{\alpha }_3{Integ}_{jt}+{\alpha }_4{Controls}_{ijt}+v_i+{\mu }_j+{\gamma }_t+{\epsilon }_{ijt}$$

(2)

In Equation (2), ${Age}_{jt}$ denotes the degree of demographic aging, and this paper employs the old-age dependency ratio as a metric to quantify this phenomenon. ${Age}_{jt}\times {Integ}_{jt}$ denotes the interaction between population aging and labor market integration. The definitions of the other variables are equivalent to those in Equation (1).

4.1.3. Mediation Effect Model

The traditional three-step mediation model holds a prominent position in the field of economics. Numerous scholars have employed this approach to explore influence pathways [7]. Accordingly, this study adopts the methodology proposed by Wen and Ye (2014) to construct a three-step mediation framework [52]. This model aims to elucidate the mechanism by which labor market integration, within the context of an aging population, impacts the quality of export products.

$${MECH}_{jt}={\beta }_0+{\beta }_1{Integ}_{jt}+{\beta }_2{Controls}_{ijt}+v_i+{\mu }_j+{\gamma }_t+{\epsilon }_{ijt}$$

(3)

$${Quality}_{ijt}={\delta }_0+{\delta }_1{Integ}_{jt}{+\delta }_2{MECH}_{jt}+{\delta }_3{Controls}_{ijt}+v_i+{\mu }_j+{\gamma }_t+{\epsilon }_{ijt}$$

(4)

${MECH}_{jt}$ indicates the mediator variable. The study considers Innovation (Inno) and labor costs (salary) as potential mediators. The definitions of the other variables are equivalent to those in Equation (1).

4.1.4. Difference-in-Differences (DID) Model

This paper further analyzes the moderating effect of labor market integration on mitigating the impact of U.S. tariff increases on export product quality. Given the uncertainty surrounding the timing of the Trump administration’s potential trade war against China and the specific list of products subject to tariffs, the Sino-U.S. trade friction satisfies the exogeneity assumption of the difference-in-differences model [53]. This paper employs the multiple rounds of tariff increases imposed by the United States on China from 2018 to 2019 as a natural experiment, utilizing sample data on China’s exports to the United States from 2017 to 2022. Following the core principles of the difference-in-differences (DID) method, products subjected to U.S. tariff hikes on China (Tariff Product List: The list of products subject to multiple rounds of trade friction between the United States and China, as well as specific goods and tax rates, is sourced from the Office of the United States Trade Representative [54]) are designated as the treatment group, while those not affected by such tariffs serve as the control group. The analysis aims to determine whether there were significant changes in the quality of exports from both groups in response to the U.S. tariff increases on China, as well as to investigate the role of labor market integration in mitigating the impact of trade friction on export quality:

$${Quality}_{ijt}={\alpha }_0+{\alpha }_1{inc}_{it}+{\alpha }_2{Controls}_{ijt}+v_i+{\gamma }_t+{\epsilon }_{ijt}$$

(5)

$${Quality}_{ijt}={\alpha }_0+{\alpha }_1{inc}_{it}+a_2{Integ}_{jt}\times {inc}_{it}+{\alpha }_3{Controls}_{ijt}+v_i+{\gamma }_t+{\epsilon }_{ijt}$$

(6)

Here, ${inc}_{it}={post}_{it}\times {tar}_i$. The variable ${post}_{it}$ is a treatment period dummy for product $i$, indexed by both $i$ and $t$ to account for the varying timing of sanctions across different products. If year $t$ corresponds to the year in which product $i$ was subjected to additional tariffs or subsequent years, ${post}_{it}$ equals 1; otherwise, it equals 0. The variable ${tar}_i$ indicates the policy intensity (Policy intensity: Considering that tariff adjustments occurred after the initial imposition of tariffs on products during the period studied in this paper, and that the policy was implemented on a monthly basis, this paper calculates the average tariff based on the month and year of implementation of the tariff increase policy), determined by the month and year when the product was first tariffed and the applicable tariff rate.

4.2. Variables Declaration

4.2.1. Explained Variable: Quality

In this paper, we reference the works of Khandelwal et al. (2013) and utilize demand information backpropagation to assess the quality of export products [55]. First, establish an econometric model based on the product demand function to obtain the quantity of product:

$$q_{ijmt}=p_{ijmt}^{-\sigma }{\lambda }_{ijmt}^{\sigma -1}{\displaystyle \frac{E_{imt}}{P_{imt}}}$$

(7)

where $i$ denotes the product, $j$ stands for the province, $m$ represents the destination country, and $t$ denotes the year. The variables $q_{ijmt}$, ${ \lambda }_{ijmt}$, and $p_{ijmt}$ represent the quantity, quality, and price of product ($i$) exported from province ($j$) to country ($m$) in year ($t$), respectively. $\sigma$ indicates the elasticity of substitution for the product, while $E_{imt}$ and $P_{imt}$ denote the total consumption expenditure and price index of product ($i$). Taking the natural logarithm of both sides yields:

$$ln{q}_{ijmt}={\chi }_{mt}-\sigma ln{p}_{ijmt}+{\epsilon }_{ijmt}$$

(8)

Among these, ${\chi }_{mt}$=${ lnE}_{imt}-{lnP}_{imt}$ functions as a two-dimensional dummy variable for the importing country and year, serving to control for factors such as geographical distance between importing countries, price level variations, and shifts in consumer preferences. The term ${\epsilon }_{ijmt}=(\sigma -1)\mathit{ln}{\lambda }_{ijmt}$ represents the stochastic disturbance, encapsulating information related to the exported product. Building on this, a regression analysis is performed on Equation (3) to derive the quality of exported products at the “province-product-importing country-year” level.

$${Quality}_{ijmt}=ln{\widehat{\lambda }}_{ijmt}={\displaystyle \frac{{\widehat{\epsilon }}_{ijmt}}{\sigma -1}}={\displaystyle \frac{ln{q}_{ijmt}-ln{\widehat{q}}_{ijmt}}{\sigma -1}}$$

(9)

where ${\widehat{ \lambda }}_{ijmt}$,${\widehat{ q}}_{ijmt}$ denote the estimated values of the quality and quantity of product ($i$) exported by province ($j$) to country ($m$) in year ($t$), while ${\widehat{\epsilon }}_{ijmt}$ represents the residual. To facilitate comparable assessments of export product quality across different periods and regions, this study adopts the standardization method proposed by Khandelwal et al. (2013) [55], resulting in a measure of relative export product quality ($r\_{quality}_{ijmt}$). The export product quality at the “province-product-year” level is then calculated using export value as the weighting factor.

$${Quality}_{ijt}={\displaystyle \frac{{value}_{ijmt}}{{\sum }_{ijmt\in \Omega }{value}_{ijmt}}}\times r\_{quality}_{ijmt}$$

(10)

Among these, ${value}_{ijmt}$ denotes the export value of product ($i$) from province ($j$) to country ($m$) in year ($t$), while $\Omega$ signifies the set of products exported by region ($j$) to all countries in the same year.

4.2.2. Explanatory Variables: Integ

The labor market integration index in this study is derived using the relative price method, which offers strong data availability and obviates the need to account for potential confounding factors such as trade volume, market size, and factor endowments. This approach is widely adopted in academic research to measure labor market integration [56]. In accordance with the methodology of Zhao and Xiong (2009), the average wage index was chosen as the primary data, with a specific focus on the relationship between neighboring provinces to evaluate the extent of labor market integration [56]. The process begins by calculating the absolute values of relative prices between provinces across different enterprise types over the period, using the following formula:

$$\left|{∆Q}_{jft}^k\right|=\left|\mathrm{l}\mathrm{n}\right(P_{jt}^k/P_{ft}^k)-\mathrm{l}\mathrm{n}(P_{jt-1}^k/P_{ft-1}^k\left)\right|=|\ln \left(P_{jt}^k/P_{jt-1}^k\right)-\mathrm{l}\mathrm{n}\left(P_{ft}^k/P_{ft-1}^k\right)|$$

(11)

Here, $P_{jt}^k/P_{jt-1}^k$ and $P_{ft}^k/P_{ft-1}^k$ denote the average wage indices for type $k$ enterprises in different provinces. To prevent the results from being influenced by the order of regional comparison, this study employs absolute values of the relative prices. The average of $\left|{\mathsf{\Delta }Q}_{jft}^k\right|$ is computed as $\overline{\left|{\mathsf{\Delta }\mathrm{Q}}_{\mathrm{j}\mathrm{f}\mathrm{t}}^{\mathrm{k}}\right|}$, and a mean adjustment is applied to eliminate systematic biases arising from industry-specific fixed effects due to heterogeneity, thereby deriving relative prices $q_{jft}^k$ that reflect solely labor market factors.

$$q_{jft}^k=\left|{\mathsf{\Delta }Q}_{jft}^k\right|-\overline{\left|{\mathsf{\Delta }Q}_{jft}^k\right|}$$

(12)

Subsequently, the variance $Var{q}_{jft}^k$ of $q_{jft}^k$ is computed to serve as the labor market segmentation index between the two provinces. These variances are then aggregated according to provinces to derive the overall labor market segmentation index $Var\left(q_{jft}^k\right)$ between each province and its neighboring regions.

$$Var\left(q_{jft}^k\right)=({\sum }_{j\ne f}Var q_{jft}^k)/N$$

(13)

Here, $N$ denotes the number of neighboring provinces that have been combined. Based on the inverse relationship between market segmentation and market integration, the labor market integration index, ${Integ}_{jt}$, is subsequently constructed as follows:

$${Integ}_{jt}=\sqrt{1/Var\left(q_{jt}\right)}$$

(14)

The greater the value of ${Integ}_{jt}$, the higher the degree of labor market integration. To standardize the measurement units, the labor market integration index was divided by 1000 to derive the levels for 29 provinces (excluding Hainan, Tibet, Hong Kong, Macao, and Taiwan) from 2017 to 2022. Subsequently, the squared values were computed to obtain the squared term of the labor market integration.

4.2.3. Mediating Variables

(1)

Innovation (Inno): The proportion of spending on science and technology to spending on the budget, reflecting a higher degree of regional innovation.

(2)

Labor costs (salary): The proportion of regional wage bill to business revenue.

4.2.4. Control Variables

(1)

Foreign Investment (FDI): The natural logarithm of actual foreign direct investment (FDI) utilization.

(2)

Scale of Exports (EXP): The proportion of regional GDP to total local exports.

(3)

Industrial Structure (IS): The proportion of the gross regional product to the total output of the secondary and tertiary industries.

(4)

Degree of Government Intervention (GOV): The proportion of GDP to local government spending.

(5)

Urbanization Level (city): The proportion of local urban residents to the overall population.

(6)

Pressure on Fiscal Balance (deficit): The proportion of regional budget receipts to expenditures made by the province government.

4.3. Data Sources and Description

This paper chooses data from the Beijing-Tianjin-Hebei region for the years 2017 to 2022. The data necessary for calculating the core explanatory variable, labor market integration level, is sourced from the China Statistical Yearbook. In contrast, the data required for assessing the explanatory variable, export product quality, is derived from the HS 8-digit coded export data within the Foreign Trade Database of the National Research Network. Additionally, the data for the combination of control variables and other provinces is obtained from the China Statistical Yearbook, the Statistical Yearbook of the Three Provinces of Beijing-Tianjin-Hebei, and the Statistical Bulletin.

Furthermore, for the purposes of this study, the export data are processed as follows: (i) Exclude samples that result in information loss, including data lacking product names, partner names, or the names of Chinese partners; (ii) exclude samples where the value of a single trade transaction is less than USD 50 or where the quantity unit is less than 1; (iii) retain the sample with the largest number of counting units under the same product code; (iv) align the HS 8-digit code of trade data with the international HS 6-digit code, and subsequently correlate it with the ISIC Rev.2 3-digit code, the SITC Rev.2 3-digit code, and the 4-digit code, while retaining the manufacturing sample data; (v) in accordance with Lall (2000), exclude samples of primary goods and resource-based goods [57]; (vi) to ensure the credibility of the regression analysis, products with an overall sample size of fewer than 100 are excluded. The resulting product-level data are then merged with provincial data to create a micro-sample for the Beijing-Tianjin-Hebei region, categorized by the dimensions of “year-province-product” for the span of 2017–2022, yielding 51,367 observations in total.

Descriptive statistics for the primary variables are presented in

Table 1. Descriptive analysis of main variables.

Variables	Obs.	Mean	Std.	Min	Median	Max
Quality	51,367	0.6478	0.1616	0.0000	0.6710	1.0000
Integ	51,367	0.0366	0.0159	0.0166	0.0334	0.0814
FDI	51,367	13.6691	0.5221	12.9369	13.7521	14.6596
EXP	51,367	0.1457	0.0669	0.0623	0.1461	0.2766
IS	51,367	0.9616	0.0434	0.8928	0.9869	0.9973
GOV	51,367	0.2101	0.0280	0.1650	0.2146	0.2521
city	51,367	0.7113	0.1172	0.5574	0.6467	0.8933
deficit	51,367	1.6566	0.4067	1.2146	1.4733	2.3580

.

4.4. Factual Analysis of the Characteristics of the Beijing-Tianjin-Hebei Region

The study assessed the average levels of labor market integration and export quality for the Beijing-Tianjin-Hebei region as a whole, as well as for Beijing, Tianjin, and Hebei individually, from 2017 to 2022, following the estimation steps outlined in Section 4.2 on variable measurement. Utilizing the collected data, it performed a systematic analysis of the trend characteristics of both indicators.

4.4.1. Analysis of the Level of Labor Market Integration

As illustrated in Figure 1, the overall labor market integration in the Beijing-Tianjin-Hebei region exhibits a varying declining tendency between 2017 and 2022. Compared to 2017, the degree of integration of the labor market in 2022 has declined by 10.03%. Notably, the period from 2017 to 2019 reflects a pattern of first increasing and then decreasing. The period from 2019 to 2021 exhibits a similar trend, albeit with more pronounced fluctuations; however, in 2022, the level of labor market integration in the Beijing-Tianjin-Hebei region experiences a slight increase. The underlying reason may be attributed to the notable developments in the household registration system change from 2017 to 2019, which effectively lowered the barriers to labor mobility. However, the high cost of living in the Beijing-Tianjin-Hebei region, coupled with the pressures on infrastructure and public services, continues to constrain labor mobility. During the period from 2019 to 2021, the State Council explicitly emphasized the need to guide the reasonable mobility of labor factors to facilitate the transition from “Made in China” to “Created in China”. In response, the Beijing-Tianjin-Hebei region actively embraced the State’s call to promote cross-regional labor mobility. However, with the outbreak of the COVID-19 pandemic, transportation restrictions and closure measures taken in the Beijing-Tianjin-Hebei region directly limited labor mobility. Consequently, the level of labor market integration exhibited a notable upward trend initially, followed by a subsequent decline during this period. With the control of the epidemic and the lifting of lockdowns in the Beijing-Tianjin-Hebei region, numerous conducive conditions were established for labor mobility, resulting in a modest resurgence in the level of integration within the labor market.

Sub-regionally, the average level of labor market integration in the Beijing-Tianjin-Hebei region shows regional differences, with Beijing (0.038) > Tianjin (0.037) > Hebei (0.034) in descending order. Among them, Beijing has experienced a slight, fluctuating upward trend following a significant decline. This may be attributed to Beijing’s recent efforts to vigorously advance its industrial restructuring, reducing reliance on traditional manufacturing and labor-intensive industries. As a result, the demand for certain segments of its workforce has diminished, contributing to a decline in labor market integration. However, with the deepening of synergistic development in the Beijing-Tianjin-Hebei region, improvements in transportation infrastructure between Beijing and its neighboring areas, along with enhanced information sharing and policy coordination can foster the reasonable mobility of the labor force. During this period, Hebei exhibits a little downward tendency, which is probably caused by the region’s aging population. By 2022, the proportion of the aging population had reached a notable 21.01%. The aging labor force tends to be relatively weaker in terms of physical strength, skills, and adaptability to learning, coupled with a low willingness to engage in interregional mobility, which diminishes overall labor market mobility. However, the presence of reforms in the household registration system and other incentives aimed at promoting labor mobility has led to a fluctuating downward trend in the overall level of labor market integration in Hebei. In Tianjin, the average degree of labor market integration is 0.037. From 2019 to 2022, the level of labor market integration in Tianjin exhibited a sharp rise, followed by a steep decline, and subsequently a slow rebound trend, with fluctuations significantly more pronounced than those in Beijing and Hebei. This volatility may stem from the Tianjin government’s introduction of a series of policies during this period aimed at encouraging labor mobility, including financial subsidies and talent acquisition programs. These initiatives attracted a substantial influx of high-quality laborers to Tianjin, effectively driving a significant increase in labor market integration. With the eruption of the COVID-19 pandemic in 2020, Tianjin, as a vital transportation hub and densely populated city, placed strict limitations on people’s freedom of movement and business operations, resulting in a decline in labor market integration. Furthermore, Tianjin’s significant reliance on traditional manufacturing and its port economy, along with a relatively homogeneous industrial structure, render its labor market particularly sensitive to economic fluctuations and policy adjustments. Consequently, the level of labor market integration experiences pronounced fluctuations when subjected to external shocks, such as policy changes and the COVID-19 pandemic.

4.4.2. Analysis of the Level of Export Product Quality

As illustrated in Figure 2, a general overview reveals that the quality of export products in the Beijing-Tianjin-Hebei region showed an increase trend from 2017 to 2022. Compared to 2017, the general quality degree of export goods in this region increased to 0.666 in 2022. While there was an overall rise during the earlier period, the magnitude of this change was relatively modest. The primary cause for this trend is the government’s implementation of a series of tax cuts and fee reductions, alongside policies aimed at optimizing the business environment. These measures have effectively lowered trade costs, allowing businesses to devote more funds to improving the quality of their export goods. However, the increasing aging population in China—reflected in the rising elderly dependency ratio—has somewhat hindered the effectiveness of these policies in bolstering the quality of export products. In the latter part of the period, there was a slight decline, followed by a gradual recovery. This fluctuation can likely be attributed to the effect of the COVID-19 pandemic, which severely disrupted the worldwide logistics system and resulted in shortages of certain key raw materials. Consequently, this shortage adversely affected the quality of production for export products. As the epidemic situation stabilizes and China’s economy gradually recovers, the government is urging enterprises to enhance their R&D innovation and technological transformation. This initiative aims to upgrade manufacturing capabilities and fundamentally elevate the quality of export products and technological standards.

Sub-regionally, there are also regional differences in the quality of exported products in the Beijing-Tianjin-Hebei region, specifically manifested as Beijing (0.617) < Tianjin (0.665) < Hebei (0.669). Among these, the trends in export quality for Beijing and Tianjin are generally aligned, but Tianjin’s export quality level is slightly higher than Beijing’s. This may be attributed to Tianjin’s role as an important industrial base and port city in northern China. This disparity may be attributed to Tianjin’s status as a significant industrial base and port city in the north, which likely places greater emphasis on the reform and upgrading of its manufacturing sector and the enhancement of export competitiveness through targeted policies. Although the city boasts a robust R&D capability in high-tech fields, the outcomes of this research and development are more likely to manifest in the trade of services and technology exports rather than in the enhancement of the quality of manufactured goods. With an initial increase followed by a fall, Hebei has the highest average export quality (0.669). In the context of coordinated regional growth, Hebei’s strategic development of the Xiong’an New Area and its role in absorbing industrial transfer from Beijing and Tianjin are the causes of this trend. These programs have greatly improved Hebei’s industrial levels and product quality by introducing cutting-edge technology and management know-how. However, Hebei’s economy remains heavily reliant on conventional sectors, with many businesses producing mostly low-value goods, which restricts the potential for future improvements in export quality. Furthermore, Hebei’s export quality declined as a result of the COVID-19 pandemic in 2020, creating a trend that began with an increase and ended with a fall.

5. Empirical Results and Analysis

5.1. Baseline Regression Results and Analysis

Table 2. Benchmark regression results and moderation effect regression.

	(1)	(2)	(3)
Variables	Benchmark Regression	Benchmark Regression	Moderation Effect Regression
Integ	0.2362 ***	0.1836 ***	−6.0446 ***
	(6.35)	(4.26)	(−16.14)
Age			−3.4483 ***
			(−27.67)
Age × Integ			35.1109 ***
			(17.75)
FDI		0.0089 **	−0.0743 ***
		(2.29)	(−15.12)
EXP		0.1809 ***	1.8370 ***
		(3.37)	(22.36)
IS		−1.7820 ***	−5.1851 ***
		(−4.48)	(−10.12)
GOV		−0.0470	−0.5187 ***
		(−1.04)	(−10.84)
city		0.0539 ***	0.1175 ***
		(3.63)	(7.61)
deficit		−0.0062	−0.2917 ***
		(−0.32)	(−13.24)
Constant	0.6391 ***	2.1886 ***	7.5307 ***
	(440.61)	(5.23)	(14.00)
ID FE	Yes	Yes	Yes
Province FE	Yes	Yes	Yes
Year FE	Yes	Yes	Yes
Observations	51,367	51,367	51,367
R-squared	0.603	0.604	0.611

t statistics in parentheses, ** p < 0.05, *** p < 0.01.

presents the findings of the benchmark regressions. Column (1) includes only labor market integration in the regression model, which shows that the regression coefficient of the labor market integration index is noticeably positive at the 1% level. As illustrated in column (2), after accounting for other variables, the elasticity coefficient of labor market integration is 0.184 (p < 0.01), indicating that a 1% increase in the level of labor market integration corresponds to a 0.184% improvement in export product quality. The underlying reason may be attributed to the fact that, once the labor force achieves free mobility, the level of information asymmetry within the labor market diminishes. This enhanced flow of labor facilitates a more rational allocation of labor resources and generates knowledge spillover effects, thereby driving the enhancement of the export products’ quality that businesses produce. Furthermore, the integration of the labor market can, to a certain extent, enlarge the labor market’s scope, thereby effectively enhancing the separation of labor and increasing the degree of specialization. This, in turn, leads to heightened labor productivity, which contributes to the improved quality of export products. In column (3), the coefficient of Age and Age × Integ is −3.448 (p < 0.01) and 35.111 (p < 0.01), which illustrates that effective labor market integration can substantially mitigate the detrimental effects of demographic aging on the quality of export products. The reason may lie in the fact that the percentage of elderly workers is increasing in an aging society, yet their learning abilities are far less advanced than those of younger people, which impedes company technology progress. However, successful labor market integration can encourage the spread of information, expertise, and technology, allowing employees to learn and adjust to new experiences, ideas, and insights. This dynamic helps to encourage continuous innovation inside businesses so as to counteract the negative effects of aging on export quality.

5.2. Robustness Test

5.2.1. Substitution of Core Explanatory Variables

The core of labor market integration resides in the efficient allocation of labor resources, primarily manifested through the free movement of labor across regions [7]. Consequently, the degree of labor mobility serves as a direct indicator of the level of market integration—greater mobility signifies higher integration. Utilizing this as a proxy demonstrates that the regression outcomes are robust, remaining unaffected by the specific measurement approach of the labor market integration index. Consequently, this paper utilizes labor mobility intensity as a proxy indicator for labor market integration, incorporating it into a three-dimensional fixed effects model for regression analysis. This paper employs the proportion of people working in the three main industries to the overall population as a measure of labor mobility intensity. The specific calculation formula is as follows:

$${pft}_{jt}={\displaystyle \frac{{Te}_{jt}}{{Tp}_{jt}}}$$

(15)

where $pft$ denotes the intensity of labor mobility, $Te$ denotes the number of people employed in the three major industries, $Tp$ represents the total population, $j$ signifies the province, and $t$ denotes the year. The regression results are presented in Column (1) of

Table 3. Robustness test.

	(1)	(2)	(3)
Variables	Substitution of Explanatory Variables	Removal of Extreme Outliers	Addition of Missing Variables
Integ		0.1828 ***	0.1835 ***
		(4.32)	(3.41)
pft	0.6084 ***
	(4.28)
FDI	0.0128 ***	0.0088 **	0.0219 ***
	(3.27)	(2.31)	(3.55)
EXP	−0.0641	0.1777 ***	−0.0628
	(−0.81)	(3.38)	(−0.61)
IS	−1.5067 ***	−1.7752 ***	−1.2045 ***
	(−3.96)	(−4.55)	(−2.68)
GOV	−0.0469	−0.0488	0.0154
	(−1.04)	(−1.10)	(0.31)
City	0.0643 ***	0.0539 ***	0.0527 ***
	(4.26)	(3.70)	(3.52)
deficit	0.0145	−0.0072	0.0744 **
	(0.77)	(−0.38)	(2.08)
lnpop			−1.1155 ***
			(−2.82)
fin			−0.0402 *
			(−1.91)
Constant	1.5704 ***	2.1863 ***	10.2953 ***
	(4.00)	(5.33)	(3.54)
ID FE	Yes	Yes	Yes
Province FE	Yes	Yes	Yes
Year FE	Yes	Yes	Yes
Observations	51,367	51,367	51,367
R-squared	0.604	0.607	0.604

t-statistics in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

. After substituting the core explanatory variables, the regression coefficient for labor market integration stays notably negative at the 1% level, while the quadratic regression coefficient continues to be notably positive, verifying that the benchmark regression results are resilient.

5.2.2. Bilateral Indentation

This study employs export quality data at the product level, which is vulnerable to random fluctuations and unique circumstances. Such factors can produce extreme outliers within the research sample, misrepresenting the true quality level of the products and undermining the scientific validity and accuracy of the regression results. Building on this, the paper employs a two-tailed trimming procedure at the 1% level on export product quality to mitigate the influence of extreme values and enhance the robustness of the regression analysis. As illustrated in Table 3, following the trimming process, the regression coefficient for labor market integration remains largely consistent, reaffirming that the positive impact of labor market integration on export product quality is both stable and reliable.

5.2.3. Addition of Missing Variables

Although this paper utilizes a three-dimensional fixed-effects model of “product-province-year” to partially address the issue of omitted variable bias, it cannot entirely eliminate such concerns. Regions with larger populations tend to demonstrate greater market demand, and this growth in demand often motivates enterprises to enhance their export quality. Additionally, higher levels of financial development provide firms with access to superior financial resources, which may facilitate improvements in export quality [58]. Consequently, to ensure that critical control variables are not overlooked, this study incorporates population size and the level of financial development into the regression model: population size (lnpop): assessed by the logarithm of the region’s total population at the end of the year; financial development level (fin): measured by the ratio of financial institutions’ loan balances to GDP. The regression results are presented in Table 3, where the regression coefficient for labor market integration remains significantly positive at the 1% level, even after sequentially controlling for population size and financial development level. This further affirms the reliability of the regression results.

5.3. Endogenous Analysis

To address potential bidirectional causality and associated regression bias, this study employs an instrumental variables approach for endogeneity analysis. Excessive labor mobility costs that lead to labor market segmentation are a primary driver of the urban–rural income gap [59]. Enhancing labor mobility and achieving labor market integration are vital strategies for narrowing this income disparity [60]. Consequently, labor market integration is correlated with the urban–rural income gap, while the gap itself is relatively exogenous to fluctuations in export product quality, showing no direct correlation with it. This alignment meets the principles of correlation and exogeneity essential for the selection of instrumental variables. Therefore, this paper uses the earnings disparity between urban and rural dwellers (Theil) as an instrumental variable for two-stage least squares (2SLS) regression. The following is the precise calculation formula:

$${Theil}_t={\sum }_{i=1}^2({\displaystyle \frac{I_{it}}{I_t}})ln{\displaystyle \frac{I_{it}/P_{it}}{I_t/P_t}}$$

(16)

where ${Theil}_t$ denotes the urban–rural income gap, $I_{it}$ denotes urban or rural disposable income per capita, $I_t$ denotes provincial disposable income per capita, $P_{it}$ denotes rural or urban resident population, and $P_t$ denotes provincial resident population.

The regression results are presented in

Table 4. Endogeneity analysis.

	(1)	(2)
Variables	Integ	Quality
Integ		1.1603 *
		(1.80)
Theil	0.5330 ***
	(16.75)
FDI	0.0096 ***	0.0011
	(22.18)	(0.17)
EXP	−0.5951 ***	0.1626 ***
	(−8.84)	(3.02)
IS	2.9416 ***	−4.8486 **
	(51.03)	(−2.39)
GOV	0.2996 ***	−0.2987 *
	(55.40)	(−1.75)
city	−0.0158 ***	0.0613 ***
	(−10.43)	(3.98)
deficit	0.1358 ***	−0.1422
	(60.95)	(−1.57)
ID FE	Yes	Yes
Province FE	Yes	Yes
Year FE	Yes	Yes
Kleibergen–Paap rk LM		324.493
statistic		[0.00]
Kleibergen–Paap Wald rk F statistic		280.472
		{16.38}
Observations	51,367	51,367
R-squared	-	−0.009

t-statistics in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

, where the Kleibergen–Paap rk LM statistic exhibits a p-value of 0, and the Kleibergen–Paap Wald rk F statistic exceeds the critical value at the 10% bias level. This indicates that the instrumental variables are neither under-identified nor weak. The regression coefficient for labor market integration in column (3) is positive and achieves significance at the 1% level. This suggests that, after addressing endogeneity concerns, the regression results remain consistent with those of the benchmark regression, further affirming the reliability of the benchmark findings.

5.4. Heterogeneity Analysis

5.4.1. Heterogeneity of Trade Models

Given that labor market integration may influence the quality of exported products differently across various trade modes, this paper classifies trade modes into general trade and processing trade for group-specific regression analysis [61]. The regression results are presented in

Table 5. Regression results for heterogeneity in trade patterns.

	(1)	(2)
Variables	General Trade	Processing Trade
Integ	0.1547 ***	0.6481
	(3.33)	(1.42)
FDI	0.0119 ***	−0.0816
	(2.79)	(−1.55)
EXP	0.2031 ***	−0.1407
	(3.46)	(−0.32)
IS	−1.4489 ***	−3.4340
	(−3.39)	(−0.92)
GOV	−0.0313	0.4435
	(−0.64)	(1.17)
city	0.0648 ***	−0.0357
	(3.77)	(−0.25)
deficit	−0.0114	−0.2103
	(−0.53)	(−1.06)
Constant	1.8221 ***	5.4109
	(4.06)	(1.34)
ID FE	Yes	Yes
Province FE	Yes	Yes
Year FE	Yes	Yes
Observations	43,681	850
R-squared	0.613	0.923

t-statistics in parentheses. *** p < 0.01.

, which indicate a positive coefficient of 0.1547 (p < 0.01) for labor market integration, demonstrating a significant effect, whereas its influence on processed trade products remains statistically insignificant. Thus, labor market integration positively influences the quality of general trade products only. This phenomenon may stem from the high elasticity of product quality changes in general trade. As the labor force begins to move freely, the labor market expands, leading to an increase in the division of labor and a greater degree of specialization, thereby enhancing labor productivity, and subsequently improving the quality of exported products. In contrast, processing trade products are more reliant on imported intermediate goods, and their processing processes are more stable, limiting the scope for product quality adjustments. Consequently, the impact of labor market integration on processing trade is relatively constrained.

5.4.2. Heterogeneity of Trade Partner

Given that the effect of labor market integration on export product quality varies in response to changes in trading partners, this study adopts the World Bank’s classification criteria to categorize trade partners into low-income, lower-middle-income, upper-middle-income, and high-income groups for the purpose of group regression analysis. The regression results are presented in

Table 6. Heterogeneity analysis of export destination countries.

	(1)	(2)	(3)	(4)
Variables	low-Income Countries	lower-Middle-Income Countries	Upper-Middle-Income Countries	High-Income Countries
Integ	0.2342	−0.1172	0.1944	0.2080 ***
	(0.45)	(−0.91)	(1.41)	(2.87)
FDI	0.0137	0.0251 **	−0.0010	0.0006
	(0.26)	(2.15)	(−0.08)	(0.09)
EXP	0.8121	0.3599 **	0.1254	0.0407
	(1.56)	(2.32)	(0.73)	(0.45)
IS	2.2396	−1.1460	−0.6882	−1.9313 ***
	(0.50)	(−0.95)	(−0.54)	(−2.95)
GOV	0.3231	−0.0145	−0.0211	0.0454
	(0.75)	(−0.11)	(−0.15)	(0.61)
city	−0.1239	0.0442	0.0644	0.0829 ***
	(−0.89)	(1.06)	(1.37)	(3.25)
deficit	0.1251	0.0054	−0.0379	−0.0076
	(0.58)	(0.09)	(−0.62)	(−0.23)
Constant	−2.0411	1.3195	1.3218	2.4276 ***
	(−0.42)	(1.04)	(0.98)	(3.53)
ID FE	Yes	Yes	Yes	Yes
Province FE	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
Observations	2476	11,795	10,202	21,676
R-squared	0.931	0.786	0.821	0.717

t-statistics in parentheses, *** p < 0.01, ** p < 0.05.

, which reveals a significant coefficient of 0.208 (p < 0.01) for high-income countries, whereas the coefficients for low-income, lower-middle-income, and upper-middle-income nations are not statistically significant. This suggests that labor market integration positively influences the quality of exports exclusively when directed toward high-income markets. This discrepancy may stem from the varying market characteristics associated with countries at different income levels, as well as the distinct nature of product demand. In low-income countries, consumption levels are generally low, and consumers tend to be price-sensitive, showing less concern for product quality and a preference for affordable options. The quality standards in lower-middle-income and upper-middle-income markets tend to be more stable, with consistent demand for products. Consequently, the quality of goods exported to these markets remains relatively steady, and China’s labor market integration exerts a limited influence on the quality of exports to these three categories of destination countries; high-income countries have a preference for quality, as consumers in these markets are more inclined to purchase superior goods. Therefore, the integration of our labor market can significantly influence the quality of products exported to these types of destination countries.

5.5. Mechanism Test

5.5.1. Innovation Synergy Effect

As shown in

Table 7. Analysis of mediating effects of technological innovation.

	(1)	(2)	(3)
Variables	Quality	Inno	Quality
Integ	0.1836 ***	0.5630 ***	0.1339 ***
	(4.26)	(110.70)	(2.73)
Inno			0.0882 **
			(2.12)
FDI	0.0089 **	−0.0491 ***	0.0132 ***
	(2.29)	(−107.27)	(3.02)
EXP	0.1809 ***	−0.1563 ***	0.1947 ***
	(3.37)	(−24.70)	(3.60)
IS	−1.7820 ***	−2.6719 ***	−1.5463 ***
	(−4.48)	(−56.91)	(−3.74)
GOV	−0.0470	0.0584 ***	−0.0521
	(−1.04)	(10.96)	(−1.15)
city	0.0539 ***	0.1424 ***	0.0413 ***
	(3.63)	(81.22)	(2.58)
deficit	−0.0062	−0.0806 ***	0.0009
	(−0.32)	(−34.79)	(0.05)
Constant	2.1886 ***	3.2965 ***	1.8977 ***
	(5.23)	(66.74)	(4.31)
ID FE	Yes	Yes	Yes
Province FE	Yes	Yes	Yes
Year FE	Yes	Yes	Yes
Observations	51,367	51,367	51,367
R-squared	0.604	0.648	0.604

Robust t-statistics in parentheses, *** p < 0.01, ** p < 0.05.

, column (1) presents the benchmark regression, while column (2) indicates that the level of labor market integration has a positive effect on technological innovation. This suggests that, following the integration of the labor market, the free flow of labor resources facilitates the transfer of technology, knowledge, and information, thereby enhancing the technological spillover effects among enterprises, industries, and regions. Such integration fosters the workforce’s capacity to acquire and absorb new knowledge, experiences, and ideas, effectively mitigating the challenges of low technological innovation associated with aging and continuously advancing the technological innovation capabilities of enterprises. The coefficient reflecting the impact of technological innovation on export product quality in column (3) is positive. Additionally, the effect of labor market integration on export product quality is also positive, indicating that technological innovation plays a beneficial role in enterprises’ efforts to enhance the quality of their export products. This supports the hypothesis that labor market integration influences the quality of export products by altering the level of technological innovation. Additionally, the results of the regression show that the coefficient for Inno in column (3) is 0.088 (p < 0.01), whereas the coefficient for Integ in columns (1) and (2) are 0.184 (p < 0.01) and 0.563 (p < 0.01), respectively. These findings suggest that the impact of labor market integration on export quality is partially mediated by innovation levels. The innovation synergy effect contributes about 27% of the total, according to the mediation effect formula (${\beta }_1{\delta }_2/{\alpha }_1$). This outcome further underscores the pivotal role of innovation in enhancing the quality of exported products.

5.5.2. Labor Cost Effect

Table 8. Analysis of mediating effects of labor costs.

	(1)	(2)	(3)
Variables	Quality	Salary	Quality
Integ	0.1836 ***	−0.0034 ***	0.1689 ***
	(4.26)	(−24.66)	(3.89)
salary			−4.3508 ***
			(−2.81)
FDI	0.0089 **	0.0053 ***	0.0320 ***
	(2.29)	(430.44)	(3.53)
EXP	0.1809 ***	−0.0084 ***	0.1443 ***
	(3.37)	(−49.41)	(2.61)
IS	−1.7820 ***	0.0073 ***	−1.7503 ***
	(−4.48)	(5.78)	(−4.40)
GOV	−0.0470	0.0160 ***	0.0225
	(−1.04)	(111.31)	(0.44)
city	0.0539 ***	0.0019 ***	0.0621 ***
	(3.63)	(40.13)	(4.10)
deficit	−0.0062	0.0127 ***	0.0490 *
	(−0.32)	(203.60)	(1.77)
Constant	2.1886 ***	−0.0664 ***	1.8996 ***
	(5.23)	(−49.94)	(4.41)
ID FE	Yes	Yes	Yes
Province FE	Yes	Yes	Yes
Year FE	Yes	Yes	Yes
Observations	51,367	51,367	51,367
R-squared	0.604	0.999	0.604

t-statistics in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1.

presents the results concerning the mediating effect of labor costs. Column (1) displays the findings from the benchmark regression, demonstrating that labor market integration greatly improves the quality of export goods. Column (2) reveals that labor market integration substantially restrains the rise in labor costs. This suggests that following labor market integration, market asymmetry is significantly diminished, effectively reducing the search costs between enterprises and labor. Consequently, this offsets the increase in labor costs associated with improvements in the labor market system, thereby lowering the overall labor costs for enterprises. In Column (3), the regression coefficient for labor costs is negative, while the coefficient for labor market integration is markedly positive. This suggests that the main way labor market integration improves export quality is by lowering labor expenses. In particular, the salary coefficient in Column (3) is −4.351 (p < 0.01), whereas the Integ coefficients in Columns (1) and (2) are 0.184 (p < 0.01) and −0.003 (p < 0.01), respectively. The labor cost has a mediating impact of about 8%, according to the mediation effect formula (${\beta }_1{\delta }_2/{\alpha }_1$). These results provide more evidence that labor expenses are an important mediating factor in the process by which labor affects export quality.

6. Exploratory Analysis

6.1. Policy Background

In April 2025, the United States implemented a reciprocal tariff policy targeting China, ushering in a new wave of trade tensions that posed a formidable challenge to Chinese export enterprises. Given that the quality of export products is inherently difficult to alter in the short term, this study analyzes the multiple rounds of tariff escalations imposed by the United States on China between 2018 and 2019. Since 2018, the United States has unilaterally enacted several rounds of additional tariffs on Chinese exports: on 6 July 2018, it initially imposed a 25% import duty on 818 Chinese goods valued at USD 34 billion. On 23 August, additional tariffs at the same rate were levied on 279 categories of goods valued at USD 16 billion. On 24 September of that year, the United States dramatically broadened its scope of tariffs, levying a 10% duty on 6031 categories of Chinese goods exported to the U.S., valued at USD 200 billion. On 10 May 2019, the United States increased the tariff rate on these goods, valued at USD 200 billion, from 10% to 25%. Ultimately, The United States imposed a 15% tariff on USD 125 billion worth of Chinese imports on 1 September 2019, encompassing nearly all Chinese exports to the United States. According to trade figures published by the U.S. Department of Commerce, China’s exports to the United States declined sharply from 505.47 billion in 2017 to 435.45 billion in 2020, marking a decrease of 13.85%. Will the U.S. tariff increase policy have an impact on the quality of Chinese exports? In light of the tariff escalation, how will labor market integration influence the quality of exports? Investigating these issues enables a precise evaluation of whether labor market integration can effectively mitigate the impact of external shocks, such as U.S. tariffs on China, thereby offering valuable theoretical insights for fostering sustainable regional external economic growth.

6.2. The Impact of Trade Frictions on the Quality of Export Products

Currently, numerous scholars are exploring the relationship between trade friction and the quality of Chinese exports, though their perspectives and conclusions diverge. Khandelwal et al. (2013) discovered that tariff reductions enhanced the quality of products near the international standard frontier, while concurrently diminishing the quality of products positioned further from this benchmark [55]. Meanwhile, Bas and Strauss-Kahn (2015) and Fan et al. (2015) explored the link between the liberalization of intermediate goods trade and the quality of export products, concluding that such liberalization can significantly enhance export quality [28,30]. Feng et al. (2017) argued that, from the standpoint of trade policy uncertainty, China’s WTO membership mitigated trade policy ambiguity, intensified market competition, and optimized resource allocation [62]. This shift facilitated the export of high-quality, low-cost products while phasing out high-priced, low-quality goods, ultimately elevating the overall standard of China’s export offerings [62]. Vandenbussche and Wauthy (2001) discovered through their research on anti-dumping measures in Europe that exporting firms tend to improve the quality of their products to preserve their established market positions [63]. However, Chandra and Long (2013) observed that, following anti-dumping sanctions, Chinese exporters not only decrease their export volumes but also experience a decline in production efficiency, undermining efforts to enhance export quality [64]. This detrimental impact is especially pronounced among companies heavily reliant on exports [64].

6.3. Results of Exploratory Analysis

The regression results are presented in

Table 9. Exploratory analysis.

	(1)	(2)	(3)
Variables	Quality	Quality	Quality
inc	−0.1050 ***	−0.1129 ***	−0.2026 ***
	(−3.29)	(−3.46)	(−3.71)
Integ × inc			3.6635 **
			(2.04)
FDI		0.0835 *	0.0823 *
		(1.83)	(1.82)
EXP		1.1502 *	1.3428 **
		(1.75)	(2.04)
IS		−7.5664 *	−7.7656 *
		(−1.73)	(−1.79)
GOV		−0.8293 *	−0.9439 *
		(−1.70)	(−1.94)
city		0.0212	−0.0087
		(0.13)	(−0.05)
deficit		−0.3262	−0.3389
		(−1.54)	(−1.61)
Constant	0.6983 ***	7.3692	7.6132 *
	(131.08)	(1.64)	(1.71)
ID FE	Yes	Yes	Yes
Province FE	Yes	Yes	Yes
Observations	1585	1585	1585
R-squared	0.934	0.937	0.939

t-statistics in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1.

. Columns (1) and (2) reveal that, even after incorporating control variables, the coefficient of the primary explanatory variable (inc) remains notably negative, indicating that U.S.–China trade tensions exert a significant detrimental effect on the quality of Chinese exports to the United States. Column (3) demonstrates that, following the inclusion of the interaction term, the coefficient of Integ×inc is notably positive, suggesting that greater labor market integration can effectively alleviate the adverse effects of U.S. tariffs on the quality of Chinese exports. The underlying reason for this may be twofold. Firstly, the imposition of additional tariffs raises the trade costs for Chinese products exported to the United States. To mitigate the financial losses incurred from these tariff increases and to preserve their market share in the U.S., exporting companies tend to opt for low-cost, low-quality goods for the U.S. market. Secondly, the rise in U.S. tariffs on Chinese goods has diminished the price competitiveness of these exports in the U.S. market. In an effort to sustain profits, companies have consequently reduced their investments in research and development aimed at quality enhancement, resulting in a decline in technological innovation and, ultimately, a deterioration in the quality of exported products. Labor market integration can diminish information asymmetry and effectively lower search costs between enterprises and the workforce, thereby partially mitigating the adverse effects of rising trade costs on the quality of export products. Furthermore, labor mobility facilitates spillover effects in technology, knowledge, and information, continuously fostering technological innovation within enterprises and enhancing labor productivity. In turn, it effectively alleviates the impact of tariff increases on the quality of exported goods.

7. Discussion

This study explores the influence of labor market integration within the Beijing-Tianjin-Hebei region on export quality amid demographic transitions, aiming to understand how regional economic sustainability can be maintained during such shifts. This section will present the research findings, comparative analysis with existing literature, research significance, research limitations, and future prospects.

7.1. Research Findings

The central issue addressed in this paper is the impact and underlying mechanisms of labor market integration in the Beijing-Tianjin-Hebei region on export quality amid demographic transitions, with the aim of evaluating the role of labor market integration in fostering sustainable regional economic development. Utilizing panel data from the Beijing-Tianjin-Hebei region spanning 2017 to 2022, this study employs a three-dimensional fixed-effects model (product-province-year) for empirical analysis. The findings indicate that labor market integration significantly enhances export quality, with a 1% increase in integration levels resulting in a 0.184% improvement in export quality. This beneficial effect is primarily mediated through technological innovation (27%) and labor cost dynamics (8%). By integrating interaction terms for aging and labor market integration into the model, the study reaffirmed the pivotal role of labor market integration amid demographic transitions. Additionally, employing a Difference-in-Differences (DID) approach, the research revealed that labor market integration effectively cushions the adverse effects of U.S. tariff policies on export quality. This underscores that labor market integration fosters sustainable regional economic development by alleviating internal pressures and enhancing resilience against external shocks.

7.2. Comparative Analysis with Existing Literature

Numerous studies have affirmed the positive influence of labor market integration on export trade. Yang et al. (2023) [8] demonstrated that urban–rural labor market integration enhances export product quality by fostering worker skill development and elevating labor costs. Lee and Park (2018) [9] argued that flexible labor markets reduce export adjustment costs, thereby invigorating export activity. Fajgelbaum et al. (2020) [10] contended that easing barriers to labor mobility encourages high-productivity firms to expand their export reach, resulting in an overall increase in export volume. This study investigates the relationship between interprovincial labor mobility and export product quality, arriving at a similar conclusion: labor market integration exerts a positive influence on exports. These findings collectively reinforce the notion that labor market integration is a vital catalyst for sustainable growth within the trade economy. While Yang et al. (2023) [8] investigated the relationship between labor markets and export quality, their study predominantly focuses on labor mobility between urban and rural areas. In contrast, this paper emphasizes interprovincial labor mobility and concurrently integrates demographic transition, labor market integration, and export quality into the research model to examine their intrinsic logical relationships. It elucidates the vital importance of labor market integration in alleviating the decline of the “demographic dividend” and fostering sustainable regional economic development amid demographic shifts. Moreover, this paper explores the influence of labor market integration on export quality against the backdrop of escalating U.S. tariff policies, offering empirical evidence for the optimization of intra-regional labor resource allocation to effectively navigate external uncertainties.

7.3. Research Significance

On a theoretical level, this study integrates demographic transition, labor market integration, and export quality into its analytical framework. Through a comparative analysis of labor mobility patterns both between urban and rural areas and across provinces, it underscores the vital role of inter-provincial labor mobility within the Beijing-Tianjin-Hebei region in bolstering export quality amidst demographic shifts. Furthermore, this study investigates the influence of interprovincial labor mobility on export quality amid U.S. tariff escalations, transcending the confines of prior sustainability research that primarily concentrated on environmental aspects. It thereby enriches the theoretical foundation for fostering sustainable regional economic growth. Practically, this paper leverages empirical insights to formulate targeted policy recommendations across micro, meso, macro, and sustainable development strata. These proposals aim to guide governments, industries, and enterprises in jointly fostering regional economic sustainability, offering valuable references for informed decision-making toward sustainable regional growth.

7.4. Research Limitations

While this study offers valuable insights into the relationship between labor market integration and export quality within the context of demographic transition, certain limitations persist. First, as China’s Statistical Yearbook data is only updated through 2023, and the latest edition did not publish the average price indices for state-owned, collective, and other enterprises, this study predominantly relies on data from 2017 to 2022. As a result, it does not fully capture the most recent developments during the post-pandemic economic recovery period. Second, the assessment of labor market integration primarily relies on objective indicators, which inadequately consider the impact of institutional factors. Third, this study concentrates on the Beijing-Tianjin-Hebei region, necessitating further validation of the generalizability of its findings. Fourth, as a nascent economic paradigm, the digital economy exerts a significant influence across various sectors. This paper does not integrate the digital economy into its analysis, highlighting the need for further research to investigate its impact on export quality through labor market integration.

7.5. Future Prospects

Future research can be expanded in the following directions: First, expanding the time window of the study to analyze the impact of the epidemic shock on the effect of labor market integration. Second, constructing a more comprehensive evaluation system for labor market integration, incorporating soft factors such as institutional quality, cultural identity, etc. Third, carrying out cross-regional comparative studies to validate the applicability of the study’s conclusions in other areas. Fourth, to deeply analyze the new features and mechanisms of labor market integration in the context of digital economy, so as to provide theoretical support for the construction of a more sustainable regional development model.

8. Conclusions and Policy Implications

8.1. Conclusions

Based on the panel data of the Beijing-Tianjin-Hebei region from 2017 to 2022, this study deeply analyzes the impact mechanism of labor market integration on export quality and its sustainable development effect in the context of demographic transition. It is discovered that labor market integration significantly contributes to the improvement of regional export quality, with each 1% increase in the level of integration able to bring about 0.184% improvement in export quality. After incorporating the interaction term between aging and labor market integration into the model, the study further confirmed the important role of labor market integration in the context of demographic transition. This effect is mainly realized through two major mechanisms, namely innovation synergy and labor cost, which contribute 27% and 8% of the impact, respectively. Meanwhile, labor market integration not only enhances internal economic efficiency, but also promotes external economic sustainability, mitigating the adverse effects of U.S. tariff escalations on export quality, proving the importance of coordinated development both inside and outside the Beijing-Tianjin-Hebei region for economic sustainability.

The study further reveals the heterogeneous characteristics of labor market integration impacting export quality. At the trade mode level, the elasticity coefficient for general trade products is 0.154 (p < 0.01), whereas that for processing trade products is not statistically significant. Regarding trading partners, the elasticity coefficient for high-income countries stands at 0.208 (p < 0.01), while those for low-income, lower-middle-income, and upper-middle-income countries lack statistical significance. Furthermore, feature fact analysis reveals that the three regions of Beijing, Tianjin, and Hebei have varying degrees of labor market integration: Beijing (0.038) > Tianjin (0.037) > Hebei (0.034); nevertheless, the export product quality gradient is reversed: Beijing (0.617) < Tianjin (0.665) < Hebei (0.669). These findings provide micro evidence for precise regional integration policy design.

8.2. Policy Recommendations

(1)

Micro-level: Promote the Optimal Allocation of Labor Resources among Enterprises. Establish a labor market information hub in the Beijing-Tianjin-Hebei region that integrates and disseminates data on corporate staffing needs and the supply of labor skills throughout the area, while ensuring essential services such as cross-enterprise social security coordination. By adopting a post-event subsidy approach, encourage industry leaders to take the initiative in creating skilled talent pools. Guide enterprises in independently conducting cross-enterprise and cross-industry talent allocation to achieve optimal utilization of labor resources. Offer labor cost subsidies based on the efficiency of talent deployment. Furthermore, incentivize enterprises to implement “mentor-apprentice” skill inheritance programs through tax incentives, thereby fully leveraging the knowledge spillover effects of experienced employees.

(2)

Meso-level: Optimizing Industrial Collaborative Development Policies. Dismantle barriers to industrial collaborative innovation in the Beijing-Tianjin-Hebei region by streamlining approval procedures for cross-provincial R&D projects in high-tech manufacturing and promoting the open sharing of scientific and technological infrastructure among the three areas. Encourage high-tech manufacturing enterprises to independently seek cross-regional cooperation based on technological requirements and cost-effectiveness. Additionally, leverage the regional development gradients to facilitate the orderly transfer of industries from Beijing and Tianjin to Hebei, thereby enhancing the quality and efficiency of manufacturing.

(3)

Macro level: Improve Regional Labor Market Integration Strategies. The core of labor market integration lies in fostering the optimal allocation of human resources. Given the varying levels of integration among Beijing, Tianjin, and Hebei (Beijing 0.038 > Tianjin 0.037 > Hebei 0.034), it is essential to craft tailored, region-specific strategies aligned with each area’s unique development needs. Beijing should capitalize on its status as a hub for high-end factors by encouraging the outward flow of advanced talent to Tianjin and Hebei. Tianjin must relax residency requirements, advance social insurance reforms, and enhance the feasibility of cross-regional insurance enrollment to energize its labor market. For Hebei, strengthening infrastructure, vigorously promoting local economic growth, and maintaining housing price controls to ensure affordability will significantly enhance its attractiveness to prospective workers.

(4)

Sustainable Development Policy: Building a Dual Evaluation System. Establish a dual evaluation system that harnesses the synergistic effects of improved internal economic efficiency and bolstered external economic sustainability stemming from labor market integration. Create a dedicated sustainable development fund for the Beijing-Tianjin-Hebei region, prioritizing support for high-value-added industrial upgrading, diversification of export markets, and the enhancement of supply chain resilience to mitigate the impacts of external trade policy shocks and strengthen regional economic sustainability. Simultaneously, implement skill training subsidies and a high-end manufacturing talent recruitment program to direct labor toward high-value-added industries. Furthermore, in alignment with the requirements for upgrading export quality, low-carbon standards should be integrated into industrial support policies, guiding labor toward low-carbon, high-quality export sectors and fostering a virtuous cycle between economic and environmental sustainability.