Does institutional innovation in trade in services enhance the export propensity of enterprises? Evidence from China’s innovative pilot policy on trade in services

Does institutional innovation in trade in services enhance the export propensity of enterprises? 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Evidence from China’s innovative pilot policy on trade in services Shitong Li, Yaoao Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9188555/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Trade in services constitutes a vital component of international trade and a significant domain of global economic cooperation. Taking China’s innovative pilot policy on trade in services (the TSP policy) as a quasi-natural experiment, this research employs a difference-in-differences (DID) model and Chinese A-share listed enterprises from 2010 to 2023 to investigate the effect of the TSP policy on enterprises’ export propensity. The empirical results reveal three key findings. First, the implementation of the TSP policy significantly enhances the export propensity of enterprises. Second, the impact of the TSP policy on enterprises’ export propensity exhibits substantial heterogeneity. Specifically, the promotional effect is more pronounced in state-owned firms, large-scale firms, firms with low financing constraints, firms with high ESG scores, firms with high asset turnover, and firms in markets with high concentration. Third, artificial intelligence application and digital economic development serve as the critical transmission channels through which the TSP policy affects corporate export propensity. Additionally, the implementation of the TSP policy could also increase export revenues for enterprises. This research contributes to the existing research on the economic effects of the TSP policy and provides valuable insights for policymakers to optimize export promotion policies. Trade in services Export propensity DID model China Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Exports are among the three engines driving economic development (Usman 2023 ). Leveraging exports to support the economy is a key focus of China’s current economic development. The practical achievements of the reform and opening-up of China for more than 40 years have proved that the expansion of exports could provide enterprises with the capital needed for development, increase the scale of foreign exchange reserves, and strengthen the wealth base of economic growth and risk resistance (Claessens and Van Horen 2021 ). In 2024, China’s exports reached 25.45 trillion RMB, an increase of 7.1% year-on-year. However, with the rise of protectionism and hegemony, economic globalization faces great challenges, which have brought unprecedented pressure on the exports of China’s enterprises (O’Rourke 2019 ). Accompanied by the upgrading of the world industrial structure and international industrial transfer, trade in services has become an important driving force of global trade growth (Bekkers et al. 2024 ). From 2013 to 2023, the share of trade in services in global trade rose from 20.6% to 25%, and the World Trade Organization (WTO) expects that the share will exceed 30% by 2040. Trade in services is an important way for China to deepen international economic and trade cooperation (Wu 2015 ). Over the past few years, the scale of China’s trade in services has been expanding, with the total import and export of services reaching RMB 7,523.8 billion in 2024, a year-on-year growth of 14.4%. However, China’s trade in services also faces challenges such as regional development imbalances and an irrational trade structure, which constrain the long-term development of the sector (Jiang and Lin 2020 ). The Chinese government launched the TSP policy in February 2016, aiming to deepen reforms in trade in services and advance high-level opening up. Since the establishment of the TSP policy, its policy effects have garnered extensive attention from the academic community. Most researchers have focused on topics such as environmental quality (Xu et al. 2025 ), carbon total factor productivity (Yang and Zhu 2025 ), and service industry productivity (Fu et al. 2023 ). However, there is little research examining the effects of TSP policy on corporate export propensity. Based on this, this research aims to utilize the data of Chinese A-share listed companies from 2010 to 2023 to investigate the influences of the TSP policy on corporate export propensity, and further explore the underlying mechanisms and heterogeneity of these effects. The main contributions of this research are as follows. First, this study explores the effects of the TSP policy on the export propensity of enterprises, which contributes to the body of research on the relationship between the TSP policy and enterprise exports. Second, this study examines the mechanism through which the TSP policy promotes corporate exports across two dimensions, namely artificial intelligence application and digital economic development, clarifying the theoretical logic of the TSP policy’s impact on the export propensity of enterprises. Third, this research conducts heterogeneity analysis based on the dimensions of enterprise type, enterprise scale, financing constraints, ESG performance, total asset turnover, and market concentration, which provides a theoretical basis for the exploration of differentiated pathways of trade in services. 2. Literature review 2.1 The influencing factors of corporate export As international trade becomes increasingly frequent, factors affecting corporate exports have become a hotspot for academia. Many scholars have found that trade obstacles are a key factor affecting corporate export (Wei et al. 2023 ; Kong et al. 2024 ), such as distance, economic size, and comparative advantage (Chen and Li 2014). Non-tariff barriers such as import licenses (Imbruno 2016 ), export subsidies (Defever and Riaño 2017 ), import discrimination (Evenett 2019 ), technological barriers (Bao and Chen 2013 ), anti-dumping (Felbermayr & Sandkamp 2020 ; Su et al. 2025 ), and import and export quotas (Khandelwal et al. 2013 ) also impact corporate export activities. Therefore, trade liberalization and facilitation have also become a subject of widespread attention (Hendy and Zaki 2021 ). Most scholars have found that environmental regulations (Ye et al. 2025 ), transportation infrastructure (Wang et al. 2018 ), local government debt (Li & Qin 2024 ), business credit environment (Liu et al. 2025a ), and tax revenue (Federici et al. 2020 ) are critical factors influencing corporate exports. For example, Seck ( 2017 ) utilized the World Bank’s Enterprise Surveys data to find that improving customs clearance, the regulatory environment, trade financing conditions, and energy and telecommunication infrastructure not only increases the probability of enterprises engaging in import-export trade but also expands the scale of trade activities. De Matteis et al. ( 2019 ) based on Italian manufacturing enterprise data and found that local environments and supporting services significantly influence the export performance of enterprises. Some scholars have also explored factors influencing corporate exports from an enterprise standpoint. For instance, Edeh et al. ( 2020 ) studied the effects of technological and non-technological innovations on the enterprises’ export performance using the firm-level data of Nigeria. Kostevc ( 2022 ) examined the relationship between corporate ownership structures and participation in foreign trade activities using data from Slovenian enterprises during the period from 2005 to 2012. Yu & Tian ( 2025 ) investigated the impact of corporate financialization on export activity behavior using data from Chinese companies spanning 2010 to 2022. Furthermore, some scholars have found that ESG performance (Ma et al. 2024 ), digital transformation (Guo et al. 2024 ), operating funds management (Mansilla-Fernández and Milgram-Baleix 2023 ), corporate governance (Ramzan 2024 ), and corporate financial structure (Miravitlles et al. 2018 ) are also important influencing factors of corporate exports. 2.2 The policy effects of the TSP policy Since the implementation of the TSP policy, its policy effects have attracted extensive attention from academics, with relevant studies focusing on the development of the service industry, environmental performance, and so on. For example, Li ( 2022 ) employed a DID model and Chinese urban data from 2006 to 2019, finding that the TSP policy exerts a remarkable positive influence on the optimization of the service industry structure. Fu et al. ( 2023 ) held the view that the TSP policy positively impacts service industry productivity by utilizing urban data from 2006to 2019. Yang & Zhu ( 2025 ) examined the relation of the implementation of the TSP policy and carbon total factor productivity and discovered that the TSP policy increases the total factor productivity by 6.82%, which is mainly driven by the technological innovation effect. Xu et al. ( 2025 ) found that the TSP policy substantially promotes significant improvements in regional environmental performance. Huo et al. ( 2025 ) revealed that the TSP policy significantly increased green energy consumption in pilot cities. In summary, the factors influencing corporate exports have been extensively explored, and the policy effects of the TSP policy have been thoroughly examined. However, existing literature has not sufficiently investigated how the TSP policy impacts corporate export. Therefore, this research aims to reveal the impact of the TSP policy on corporate export propensity, thereby enriching the existing literature in the fields of corporate exports and service trade. 3. Policy background and theoretical mechanism 3.1 Policy background To foster the transformation and upgrading of foreign trade and accelerate the development of trade in services, China has implemented the TSP policy in batches (see Fig. 1 ). In February 2016, the Chinese government initiated the TSP policy in 10 provinces, as well as 5 national new areas, focusing on exploring the system construction across 8 aspects including the management system, development model, and facilitation of trade in services. In June 2018, the Chinese government was determined to expand the pilot scope of the TSP policy. Based on the original pilot areas, Beijing, Harbin, Nanjing, and Xiong’an New Area were added to the TSP policy. Meanwhile, 6 liberalization and facilitation initiatives, as well as 34 policy safeguard measures, were introduced. In August 2020, the Chinese government further added Chongqing, Dalian, Xiamen, and other cities to the scope of pilot cities, and put forward 8 pilot tasks and 122 specific initiatives. China is committed to developing the pilot areas into new frontiers for the international opening-up of the services industry, aiming to continuously invigorate new dynamics and optimize the business environment for trade in services. 3.2 Theoretical mechanism 3.2.1 Application of artificial intelligence technology The TSP policy promotes the adoption of artificial intelligence technologies by enterprises through enhanced financial support, the establishment of infrastructure and public platforms, and the improvement of institutional standards. First, the TSP policy encourages financial institutions to develop innovative products and supports enterprises in securing financing through multi-tiered capital markets, thereby alleviating funding pressures for artificial intelligence technology R&D and application. Second, the TSP Policy supports the development of digital infrastructure and public service outsourcing platforms, promoting data sharing among enterprises, governments, and specialized institutions, and provides data support for the implementation of artificial intelligence technologies. Third, the TSP policy establishes a compliance regulatory framework for artificial intelligence technology applications, clarifying rules on cross-border data flows, algorithmic security, and other areas to provide a stable institutional environment for enterprises to implement artificial intelligence technologies. The application of artificial intelligence technology enables enterprises to gain precise insights into overseas market demands, optimize the efficiency of cross-border marketing outreach, reduce cross-border trade operational costs, and thereby enhance their propensity to export. First, by applying artificial intelligence technologies to analyze target market consumption preferences and industry trends, enterprises could identify high-potential export sectors. This helps businesses clarify their market positioning, enabling products and services to better align with the needs of overseas customers (Haleem et al. 2022 ). Second, artificial intelligence technology could lower the barriers to cross-cultural communication, enhance brand visibility and customer response speed, and efficiently establish channels for connecting with overseas clients (Deryl et al. 2023). Third, artificial intelligence technology enables end-to-end optimization of supply chains, logistics, and inventory management, reducing stockpiles through demand forecasting and saving time and costs by intelligently planning logistics routes (Lalla-Ruiz and Mes 2025 ). Simultaneously, it automates processes such as trade documentation and compliance reviews, eliminating redundant manual operations and lowering the operational costs of export activities. 3.2.2 Empowering effects of the digital economy The TSP policy promotes digital economic development by expanding market access in the digital sector, regulating cross-border data flows, and fostering new forms of digital trade. First, the TSP policy eases foreign investment restrictions in sectors such as telecommunications and the internet, thereby enhancing the ease of foreign investment in the digital domain and stimulating market competitiveness in the digital economy. Second, the TSP policy establishes a robust data cross-border security management system, creates efficient and convenient channels for cross-border data flow, and lays the foundation for cross-border cooperation in the digital economy. Third, the TSP policy encourages the development of new models such as cloud outsourcing, platform subcontracting, and cross-border e-commerce, driving the digital transformation of service outsourcing. It also supports the innovative development of digital product trade and digital technology trade, expands cross-border digital delivery channels, and cultivates new growth points for the digital economy. The development of the digital economy could optimize the efficiency of the entire export process, broaden access channels to overseas markets, and elevate the level of products and value chains, thereby enhancing enterprises’ propensity to export. First, digital technology reshapes the entire trade process chain, enabling the digitization of order processing, document verification, customs declaration, and other procedures. This shortens business processing cycles, reduces redundant manual operations, enhances the efficiency of cross-border trade flows, and lowers the time costs and operational barriers for enterprises (Lu et al. 2025 ). Second, the digital economy has spawned new export channels such as cross-border e-commerce, social media marketing, and online exhibitions (Xia et al. 2024 ). These ways break down geographical and offline resource constraints, enabling businesses to connect directly with overseas end customers and buyers, thereby expanding the reach of their export operations (Lau 2023 ). Third, digital technologies optimize cross-border logistics route planning, dynamic inventory management, and financial settlement processes. By reducing inventory backlog through demand forecasting, leveraging digital tools to lower financing barriers and minimize logistics losses, while simultaneously cutting hidden costs such as marketing promotions and cross-border communication, they enhance the profitability of export operations (Qin et al. 2025 ). 4. Research methods and data sources 4.1 Model setting This paper utilizes the multi-period DID method to test the influence of the TSP policy on the export propensity of enterprises. The model is designed as follows: $$\:{\text{Export}}_{\text{ict}}\text{=}{\text{β}}_{\text{0}}\text{+}{\text{β}}_{\text{1}}{\text{Trade}}_{\text{c}\text{t}}\text{+}{\text{β}}_{\text{2}}{\text{X}}_{\text{ict}}\text{+}{\text{μ}}_{\text{i}}\text{+}{\text{ω}}_{\text{t}}\text{+}{\text{ε}}_{\text{ict}}$$ 1 Here, the subscript i stands for firm, c represents city, and t denotes year. The dependent variable export ict on behalf of the export propensity of firm i located in city c in year t . The independent variable Trade ct denotes whether city c became a pilot city for service trade in year t . X ict refers to control variables affecting firm export propensity. β 0 denotes the constant term. µ i denotes firm fixed effect, ω t represents year fixed effect, and ε ict is the random error term. This paper primarily focuses on the coefficient β 1 of the independent variable. If β 1 exhibits a significantly positive value, it suggests that the TSP policy significantly enhances the export propensity of firms. 4.2 Variable selection 4.2.1 Dependent variable This research takes the export propensity of enterprises as the dependent variable (Lu et al. 2020 ). Specifically, this study determines a firm’s propensity to export based on whether it has overseas sales revenue. If an enterprise has overseas sales revenue, it is assigned a value of 1; otherwise, it is assigned a value of 0. 4.2.2 Independent variable This study employs the implementation of the TSP policy as the independent variable. Specifically, if a sample city is designated as a pilot city and has implemented the TSP policy, the value is 1; otherwise, it is 0. 4.2.3 Control variables To obtain more accurate estimates, this paper also controls for other characteristics influencing that influence export propensity (Li & Qin 2024 ; Cai & Hao 2025 ). The control variables for the enterprise level are listed below. Financing constraints ( fc ) are measured using the WW index. Total factor productivity ( tfp ) measures the overall efficiency of converting production factors into output, calculated using the OLS method. Debt structure ( ds ), measured by the proportion of bank loans in total liabilities. Asset structure ( as ), defined as the proportion of total assets represented by net fixed assets and net inventory. Cash flow ( cf ), gauged by the proportion of net cash flow of operating activities to total assets. Total asset growth rate ( tagr ), measuring the growth pace of a firm’s asset scale. Firm age ( fa ) is expressed as the logarithm of the firm’s duration. Market concentration ( mc ) is gauged by the Herfindahl Index, calculated by summing the squares of the ratios of a firm’s primary revenue to the industry’s total primary revenue. ESG performance ( esg ) is gauged by the ESG score. Shareholding concentration ( sc ) is expressed as the combined ownership share of the top ten shareholders. Long-term debt-to-assets ratio ( ltdar ) is defined as the proportion of aggregate long-term debt to total assets. Additionally, this paper also selects a series of city-level control variables. Economic development level ( lngdp ), expressed as the logarithm of urban gross domestic product. Population size ( lnps ), measured using the logarithm of the total resident population. Freight capacity ( lnfc ), measured using the logarithm of the city’s total freight volume. The level of service industry development ( ser ), measured by the share of tertiary industry output in GDP. The level of industrial structure development ( is ), measured by the ratio of tertiary industry output to secondary industry output. The descriptive statistics are shown in Table 1 . Table 1 Descriptive statistics. Variables Mean SD Min Max export propensity 0.677 0.468 0.000 1.000 trade 0.333 0.471 0.000 1.000 fc −1.019 0.076 −1.245 −0.840 tfp 10.762 1.304 5.897 15.069 ds 0.324 0.210 0.000 1.000 as 0.340 0.171 0.000 0.944 cf 0.047 0.079 −1.938 2.222 tagr 0.207 0.659 -0.928 41.463 fa 2.095 0.920 0.000 3.526 mc 0.198 0.176 0.040 1.000 esg 4.159 0.965 1.000 8.000 sc 0.424 0.195 0.101 0.979 ltdar 0.077 0.099 −0.123 0.846 lngdp 10.673 0.743 6.242 11.818 lnps 8.597 0.668 5.708 9.450 lnfc 8.934 1.076 3.677 10.436 ser 0.771 0.300 0.176 1.897 is 0.391 0.099 0.149 0.620 4.3 Data sources The data at the enterprise level are sourced from the China Stock Market and Accounting Research (CSMAR) database, and city-level data are obtained from the China Urban Statistical Yearbook. Additionally, to secure the effectiveness of the sample data, this study excluded companies with risk warnings (PT, ST, and ST*), companies with a debt-to-asset ratio greater than 1 or less than 0, and companies in the financial industry (Lu et al. 2020 ). 5. Results and discussions 5.1 Baseline regression The baseline results of the TSP policy on the export propensity of enterprises are shown in Table 2 . Column (1) displays the estimation results controlling only for firm-level control variables. We find that the estimation coefficient for the core explanatory variable is 0.026, significant at the 10% level. Column (2) further controls for city-level covariates on top of column (1). We observe that the estimation coefficient for the core independent variable is 0.033, which is significantly positive at the 5% significance level. To summarize, the TSP policy exerts a significant positive influence on enhancing the export propensity of firms. Table 2 The results of baseline regression. Variables (1) (2) Export propensity Export propensity Trade 0.026* 0.033** (0.013) (0.013) Firm controls YES YES City controls NO YES Firm FE YES YES Year FE YES YES Observations 19,593 19,589 Adjusted R 2 0.733 0.734 Notes: *, **, and *** indicate significance at the 10%, 5%, and 1% confidence levels, respectively. The standard error is shown in parentheses. If not specified otherwise, the following tables are the same. 5.2 Robustness tests 5.2.1 Parallel trend test One of the fundamental assumptions required for the DID model is the parallel trends assumption, which ensures comparability between the treatment and control groups before and after policy implementation. Thus, we employ the event study method to conduct the parallel trend test (Xu et al. 2025 ). The model settings are as follows: $$\:{\text{Export}}_{\text{ict}}\text{=}{\text{β}}_{\text{0}}\text{+}\sum\:_{\text{θ=}-\text{6}}^{\text{7}}{\text{β}}_{\theta}\text{×}{\text{treat}}_{\text{t}}\text{×}{\text{post}}_{\text{ic}}\text{+}{\text{β}}_{\text{2}}{\text{X}}_{\text{ict}}\text{+}{\text{μ}}_{\text{i}}\text{+}{\text{ω}}_{\text{t}}\text{+}{\text{ε}}_{\text{ict}}$$ 2 In Eq. ( 2 ), the model constructs the DID estimator through post ic and treat t to represent the DID estimator. post ic signifies the implementation of the TSP policy, and treat t refers to whether or not it is a pilot city. θ signifies the year of implementation for the TSP policy. The coefficient β θ quantifies the disparity in export intentions between the two groups during the implementation year θ . Figure 2 demonstrates the results of the parallel trend test. We observe that none of the estimated coefficients are significant before the TSP policy is introduced, indicating no systematic differences in export intentions between firms in the treatment and control groups. After the TSP policy implementation, all the coefficients β θ exhibit significant positive values. This indicates a marked divergence in export intentions between treated and control group firms since the implementation of the TSP policy, with treatment group firms demonstrating significantly higher export intentions. Therefore, the DID model passed the parallel trends test, making it appropriate to use the DID model to identify the export-promoting effects of the TSP policy. 5.2.2 Heterogeneity treatment effect diagnosis The essence of a multi-period DID estimator is a weighted average. Due to heterogeneity in treatment effects across groups and time periods, the same policy intervention may produce varying outcomes for different individuals. Even when the parallel trends assumption holds, estimates of treatment effects may still be biased (de Chaisemartin and D’Haultfœuille 2020 ). Therefore, this paper diagnoses the treatment effects of heterogeneity. First, we carry out a Bacon decomposition. The results of Bacon decomposition can be seen in Table 3 . We find that the estimated coefficient of the inappropriate treatment effect (Later treated vs Earlier control) is 0.010 with a weight of 5.5%. It indicates that the treatment effect exhibits mild heterogeneity across groups, but does not cause serious bias in the estimation results (Goodman-Bacon 2021 ). Therefore, the estimates obtained in this paper using the multi-period DID model are robust. Table 3 The results of Bacon decomposition. Bacon decomposition (1) (2) Weight Estimated coefficient Earlier treated vs Later control 0.062 −0.056 Later treated vs Earlier control 0.055 0.010 Treated vs Never treated 0.732 0.022 Treated vs Already treated 0.152 0.023 Weighted estimated coefficient 0.015 Second, we conduct a negative weight decomposition. The estimated coefficients of the DID can be viewed as a weighted average of multiple 2×2-DID estimates. Although the sum of the overall weights is 1, its sign can be either positive or negative, and this negative weight is the source of estimation bias. Therefore, this paper employs the method proposed by Chaisemartin & D’Haultfœuille ( 2020 ) to decompose all the weights, and the results are presented in Table 4 . It is found that among all the 2×2DID terms, there are 4283 positive weights and 770 negative weights, and the percentage of negative weights is only 15.24%, which indicates that the results are robust. Table 4 The results of negative weight decomposition . Weight decomposition (1) (2) # ATTs Σweights Positive weights 4283 1.023 Negative weights 770 −0.023 Total 5053 1.000 Trade 0.033 Finally, we employ a heterogeneity robust estimator for regression analysis. To tackle the estimation bias inherent in bidirectional fixed effects models, Borusyak et al. ( 2024 )developed an innovative interpolation-based counterfactual approach. The results of the interpolated estimator are presented in Table 5 . We discover that the coefficient of the independent variable is positive, which provides evidence for the validity of the research conclusions. Table 5 The results of the heterogeneity robust estimator. Variables (1) Export propensity Trade_imputation 0.030** (0.014) Controls YES Firm FE YES Year FE YES 5.2.3 Placebo test To demonstrate that the effect of the TSP policy on corporate export propensity does not stem from other stochastic sources, this study conducts a placebo test. We construct pseudo-policy variables through 500 random samples based on the distribution of the independent variable, and perform regression estimation using the benchmark regression model (Ferrara et al. 2012 ). The results are shown in Fig. 4 . We discover that the pseudo-policy variables generated through randomization are mainly concentrated around 0, which is much smaller than the baseline regression results, and the majority of p-values are larger than 0.1. This confirms the validity of the findings drawn in this research. 5.2.4 Excluding the effect of contemporaneous policies Throughout the observation period, China also implemented policies such as the Free the free trade zone (FTZ) policy and Cross-border e-commerce (CBEC) policy. Their potential influence may potentially confound research outcomes, thereby introducing bias into the analysis. To address these mixed factors, we incorporate the aforementioned policies as control variables into the baseline model for regression analysis, and the results are illustrated in Table 6 . We find that the coefficients of the TSP policy remain significantly positive, whether controlling for individual policies or simultaneously controlling for all policies. Table 6 The results of excluding the effect of contemporaneous policies. Variables (1) (2) (3) Export propensity Export propensity Export propensity Trade 0.031** 0.036** 0.034** (0.013) (0.014) (0.014) FTZ 0.005 0.006 (0.010) (0.010) CBEC −0.006 −0.007 (0.011) (0.011) Controls YES YES YES Firm FE YES YES YES Year FE YES YES YES Observations 19,589 19,589 19,589 Adjusted R 2 0.734 0.734 0.734 5.2.5 Replacing fixed effects and standard errors Since there exist differences in export propensities among firms in different industries, we should exclude the interference of the industry’s characteristics on exports to correctly identify the net effects of the TSP policy. Therefore, we control for industry fixed effects in the benchmark model, with results displayed in column (1) of Table 7 . We find that the estimation coefficient of the TSP policy shows a significant positive value. Differences in economic base, infrastructure, and openness among cities also affect firms’ exports. To remove the confounding role of geographical factors, we further control for city fixed effects in the baseline model, with results presented in column (2) of Table 7 . The estimation results of the TSP policy remain significantly positive. In addition, firms within the same industry may face a similar market environment or policy shocks, and the error term is prone to intra-industry autocorrelation. Industry clustering robust standard errors could correct the estimation bias caused by such correlation and make the statistical inference more reliable. Therefore, we employ industry clustering robust standard errors for estimation while controlling for industry and city fixed effects, and the results are given in column (3) of Table 7 . We discover that the estimation results of the TSP policy remain significantly positive. Table 7 The results of replacing fixed effects and standard errors. Variables (1) (2) (3) Export propensity Export propensity Export propensity Trade 0.031** 0.025* 0.025* (0.013) (0.013) (0.013) (0.396) (0.457) (0.460) Controls YES YES YES Firm FE YES YES YES Year FE YES YES YES Industry FE YES YES YES City FE NO YES YES Industry clustering error NO NO YES Observations 19,588 19,581 19,581 Adjusted R 2 0.744 0.743 0.746 5.2.6 Considering the lag in policy effectiveness Considering that the policy implementation effects of the TSP policy may not materialize immediately, but instead exhibit a certain degree of lag. Therefore, this study performs regression estimation with the independent variable lagged by one year and two years, respectively. The regression results are given in Table 8 . We find that the coefficient of the TSP policy is still notably beneficial, which proves the confidence of the study’s findings. Table 8 The results of replacing fixed effects and standard errors. Variables (1) (2) Export propensity Export propensity L.Trade 0.027** (0.012) L2.Trade 0.022* (0.011) Controls YES YES Firm FE YES YES Year FE YES YES Observations 18,519 16,484 Adjusted R 2 0.756 0.781 5.2.7 Shortening the sample time During the sample observation period, public events such as stock market volatility and the COVID-19 pandemic also occurred, which may have exerted some influence on the estimation results. To mitigate the adverse effects of stock market volatility on the estimation results of this paper, we exclude the 2015 data for re-regression estimation (Chen et al. 2023 ), with the results reported in column (1) of Table 9 . We find that the impact of TSP policy on firms’ propensity to export is positive at the 5% statistical level. In addition, to eliminate the influence of the COVID-19 pandemic on the results, this research performs the regression again by excluding the sample data of 2020, with the results displayed in column (2) of Table 9 . It can be observed that the effects of TSP policy on firms’ propensity to export are still significantly positive. Furthermore, we also exclude both stock market volatility in 2015 and the COVID-19 pandemic in 2020, with results shown in column (3) of Table 9 . We observe that the core explanatory variable still remains significantly positive. Table 9 The results of excluding public event effects. Variables (1) (2) (3) Export propensity Export propensity Export propensity Trade 0.033** 0.033** 0.034** (0.013) (0.014) (0.015) Controls YES YES YES Firm FE YES YES YES Year FE YES YES YES Observations 18,158 17,841 16,410 Adjusted R 2 0.732 0.731 0.728 5.2.8 Endogeneity treatment The choice of pilot regions for the TSP policy is far from random. The government may consider factors such as a city’s economic foundation and level of service industry development when designating pilot areas for the TSP policy. Therefore, this research adopts two methods, namely, propensity score matching (PSM-DID) and entropy balance method (EBM-DID), to overcome endogeneity issues (Liu et al. 2025b ). This research employs the PSM-DID model for regression estimation. First, we utilize propensity score matching to reconstruct the control group. All control variables are used as covariates, and the logit model is used for propensity score matching with kernel matching. Then, the new control group and DID model are used for regression, with results shown in column (1) of Table 10 . We discover that the estimated coefficient of the TSP policy is significantly positive. However, the PSM method focuses only on the propensity score and does not guarantee that the difference in moments of each covariate between the treatment and control groups is reduced. The entropy balancing method is able to balance the distribution of covariates between the treatment and control groups. Therefore, this research adopts the entropy balancing method to re-match the control group. Specifically, all covariates are incorporated as matching variables into the linear entropy balancing process, and the sample is weighted using the weights generated by the entropy balancing method. The results are given in column (2) of Table 10 . We find that the coefficient of the independent variable remains significantly positive, indicating that the TSP policy has a significant promotion effect on the export propensity of enterprises. Table 10 The results of the PSD-DID and EBM-DID models. Variables (1) (2) PSM-DID EBM-DID Trade 0.033** 0.034* (0.013) (0.018) Controls YES YES Firm FE YES YES Year FE YES YES Observations 19,562 19,589 Adjusted R 2 0.734 0.759 5.3 Transmission mechanism test According to the preceding theoretical analysis, the TSP policy may influence corporate export propensity through the application of artificial intelligence technologies and the digital economy. Therefore, this section conducts empirical tests on the aforementioned mechanisms. This study employs machine learning methods to construct an artificial intelligence technology application lexicon, extracting keyword frequencies related to MD&AAI from listed company annual reports. The logarithmic values of the sum of AI-related word frequencies in the MD&A section of these reports serve as a proxy variable for AI technology adoption. The regression result of the TSP policy on corporate AI technology adoption is given in column (1) of Table 11 . We observe that the TSP policy significantly promotes corporate AI technology adoption, indicating that AI application represents a key pathway through which the TSP policy affects corporate export orientation. Furthermore, this study employs the Peking University Digital Inclusive Finance Index to measure urban digital economic development levels. Subsequently, a regression analysis of TSP policy effects on this index is conducted, with results displayed in column (2) of Table 11 . We discover that the TSP policy significantly impacts digital economic development at the 1% statistical significance level. It suggests that TSP policy promotes digital economic growth, thereby enhancing corporate export propensity. Table 11 The results of the transmission mechanism test. Variables (1) (2) AI technology adoption Digital economic development Trade 0.114*** 4.396*** (0.031) (0.426) Controls YES YES Firm FE YES YES Year FE YES YES Observations 19,482 16,808 Adjusted R 2 0.705 0.996 5.4 Heterogeneity analysis 5.4.1 Enterprise type As there are some differences in policy support and resource tilting among enterprises of different ownership, their willingness to export may be different. This paper categorizes the sample enterprises into two groups of state-owned enterprises and non-state-owned enterprises for regression estimation, and the results are shown in Fig. 4 . We find that the TSP policy exerts a significant positive effect on the export propensity of state-owned enterprises, but the impact on non-state-owned enterprises is insignificant. There may be the following reasons. First, state-owned firms are closely linked to the government and are more likely to obtain policy resources such as financial support, approval facilitation, and tax incentives to accompany the TSP policy. Secondly, the institutional mechanism of state-owned enterprises is relatively sound, and they can quickly organize internal resources and formulate corresponding development strategies and implementation plans in response to the TSP policy. 5.4.2 Enterprise scale The impact of the TSP policy on firms’ propensity to export may vary by firm size. Therefore, this paper applies the classification of firms with asset size above the 75% quartile as large enterprises, firms below the 25% quartile as small enterprises, and those between the 25% and 75% quartile as medium-sized enterprises. We examine whether the TSP policy has differential impacts on enterprises of different sizes, with the results shown in Fig. 4 . We find that the influence of the TSP policy on large firms is statistically significantly positive, but not for medium enterprises and small enterprises. This may be due to the following reasons. On one hand, large enterprises possess robust organizational structures and resource reserves, enabling them to swiftly align with various policy support measures, efficiently integrate policy resources into export business advantages, and reduce export costs. On the other hand, leveraging the supply chain integration capabilities, large enterprises can deeply coordinate the benefits from pilot policies with internal production, sales, and service operations. This optimizes the efficiency of the entire export process, enhances profit margins in the export business, and ultimately stimulates export enthusiasm. 5.4.3 Financing constraints Financing constraints also play a significant role in influencing export propensity. This study divides the sample enterprises into two groups, namely high and low financing constraints, according to the median of the WW Index value for regression analysis, and the results are shown in Fig. 5 . It is discovered that the effect of the TSP policy is significantly positive on firms with low financing restrictions, but not on firms with high borrowing restrictions. The TSP policy offers diversified financing services, providing enterprises with additional avenues for capital acquisition. Enterprises with low financing restrictions could leverage the TSP policy to further supplement funds required for export operations, thereby enhancing their propensity to export. 5.4.4 ESG performance This paper divides firms’ ESG scores into two categories of high ESG performance and low ESG performance based on the median and performs regression estimation, with the results shown in Fig. 5 . It is found that the impact of the TSP policy is significantly positive for firms with high ESG performance, but not significant for firms with low ESG performance. This may be due to the following reasons. On the one hand, the TSP policy emphasizes cultivating enterprises’ sustainable development capabilities. Companies with high ESG scores could leverage policy endorsement to further enhance their recognition in international markets and strengthen their competitiveness, thereby more actively expanding export operations. On the other hand, the TSP policy directs resources toward enterprises aligned with green and sustainable development principles. Companies with high ESG ratings, backed by sound governance structures and development philosophies, could more efficiently integrate policy and market resources, providing robust support for export operations and thereby boosting their willingness to export. 5.4.5 Total asset turnover This paper conducts regression estimation by dividing enterprises into high-turnover and low-turnover groups based on median total asset turnover rates (Guo et al. 2023 ), with results presented in Fig. 6 . It is found that the effect of the TSP policy is significantly positive for firms with high-turnover enterprises, while the effect is insignificant for low-turnover enterprises. The TSP policy could reduce export costs through trade facilitation reforms and resource allocation measures. Enterprises with high asset turnover rates could swiftly capitalize on these policy benefits by leveraging efficient resource allocation and operational capabilities, transforming policy advantages into competitive strengths for export operations and thereby enhancing export propensity. 5.4.6 Market concentration This paper categorizes market concentration into high-concentration and low-concentration groups based on the median Herfindahl index, conducting separate regression estimates for each group. Figure 6 presents the estimation results. We find that the impact of the TSP policy is significantly positive for firms with high market concentration, but not for those with low market concentration. Enterprises with high market concentration wield greater influence within their industries and possess stronger international bargaining power. The TSP policy provides trade facilitation support and international cooperation channels that further enhance their pricing advantages, thereby increasing the propensity to export. 5.5 Further analysis The aforementioned research confirms that the implementation of the TSP policy significantly enhances the export propensity of firms. So, does the TSP policy also lead to growth in export revenue for enterprises? This study further incorporates the logarithm of overseas business revenue as the dependent variable into the baseline regression model for estimation, with results shown in Table 12 . It can be observed that the implementation of the TSP policy also significantly increases export revenue for enterprises. Table 12 The results of further analysis. Variables (1) (2) Export revenue Export revenue Trade 0.452*** 0.618** (0.109) (0.261) Controls NO YES Firm FE NO YES Year FE NO YES Observations 32,282 19,590 Adjusted R 2 0.021 0.761 6 Conclusions and policy implications 6.1 Conclusions This paper investigates the influence of the TSP policy on the export propensity of firms by using the data of China’s A-share listed companies from 2010 to 2023 and applying the multi-period DID model. The main findings are as follows. First, the implementation of the TSP policy significantly increases export propensity among enterprises. Second, the impact of the TSP policy on corporate exports is heterogeneous. Specifically, this promotional benefit is more significant in state-owned firms, large-scale firms, low financing constraint firms, high ESG score firms, high asset turnover firms, and high market concentration firms. Third, enterprise artificial intelligence applications and digital economic development serve as key channels through which the TSP policy influences corporate export propensity. In addition, the implementation of the TSP policy also increases the export revenue of enterprises. 6.2 Policy implications First, the government shall deepen institutional reforms of the TSP policy and optimize policy implementation effectiveness. It is essential to continuously refine the policy framework for the TSP policy, deepen reforms to the negative list management system, and fully align with international high-standard economic and trade rules. Expand the coverage of the TSP policy, taking into account development disparities among eastern, central, and western regions, and promote the phased rollout of pilot experiences. Policy precision must be strengthened by formulating differentiated support measures for services trade tailored to the industrial foundations and development needs of various regions, with a focus on optimizing trade facilitation in high-end sectors such as digital services and R&D services. Additionally, there is a need to refine the policy evaluation and dynamic adjustment mechanism, regularly monitor the implementation outcomes of policies, promptly address difficulties encountered by enterprises in benefiting from these policies, and ensure that policy dividends are efficiently transmitted to market entities. Second, develop targeted and differentiated support policies tailored to the development characteristics of different types of enterprises. Specifically, intensify policy guidance for state-owned enterprises, encouraging them to leverage their resource integration and strategic leadership capabilities to proactively expand into international markets. Enhance support for large firms to empower industrial chains, leveraging their scale advantages and international deployment experience to drive upstream and downstream small and medium-sized enterprises into the global division of labor and collaboration. For enterprises with low financing constraints, increase policy support, such as additional deductions for R&D expenses and tax reductions, to encourage them to expand R&D investment and enhance their technological innovation capabilities. Support enterprises with high ESG scores in developing green service trade, encouraging them to expand international operations in energy conservation, environmental protection, clean energy, carbon trading, and related fields. For enterprises with high asset turnover rates, prioritize securing resources such as production land and energy supply to support their expansion of service trade exports. Encourage enterprises with high market concentration to increase investment in technological R&D and innovation, take the lead in establishing industry technical standards and service specifications, and enhance the overall competitiveness of the sector. Third, improve supporting systems to enhance the quality of service in trade development. Optimize conditions for service trade development at the city level, increase investment in transportation and logistics infrastructure, enhance freight capacity and cross-border logistics efficiency, and reduce export logistics costs for enterprises. Improve local service trade statistical systems and regulatory models, refine supporting policies for the tertiary industry, promote industrial structure optimization and upgrading, and lay an industrial foundation for service trade development. Strengthen the business environment by streamlining customs declaration, foreign exchange settlement, and other procedures, shortening processing cycles, and improving efficiency across the entire cross-border trade process. Establish and improve risk prevention and control mechanisms. Utilize technologies like artificial intelligence to enhance cross-border trade compliance and risk management capabilities, assist enterprises in addressing uncertainties in international markets, and strengthen export stability. 6.3 Limitations This study still has some shortcomings that require further refinement. First, although this paper employs methods such as PSM-DID and entropy balancing to mitigate self-selection issues, it does not eliminate the potential influence of omitted variables. Second, due to data availability, this study only selects Chinese A-share listed companies as samples and does not include non-listed enterprises. 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11:20:41","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17748,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-9188555/v1/25261d30ff3bcd2aa833c0b8.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Does institutional innovation in trade in services enhance the export propensity of enterprises? Evidence from China’s innovative pilot policy on trade in services","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eExports are among the three engines driving economic development (Usman \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Leveraging exports to support the economy is a key focus of China\u0026rsquo;s current economic development. The practical achievements of the reform and opening-up of China for more than 40 years have proved that the expansion of exports could provide enterprises with the capital needed for development, increase the scale of foreign exchange reserves, and strengthen the wealth base of economic growth and risk resistance (Claessens and Van Horen \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In 2024, China\u0026rsquo;s exports reached 25.45 trillion RMB, an increase of 7.1% year-on-year. However, with the rise of protectionism and hegemony, economic globalization faces great challenges, which have brought unprecedented pressure on the exports of China\u0026rsquo;s enterprises (O\u0026rsquo;Rourke \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccompanied by the upgrading of the world industrial structure and international industrial transfer, trade in services has become an important driving force of global trade growth (Bekkers et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). From 2013 to 2023, the share of trade in services in global trade rose from 20.6% to 25%, and the World Trade Organization (WTO) expects that the share will exceed 30% by 2040. Trade in services is an important way for China to deepen international economic and trade cooperation (Wu \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Over the past few years, the scale of China\u0026rsquo;s trade in services has been expanding, with the total import and export of services reaching RMB 7,523.8\u0026nbsp;billion in 2024, a year-on-year growth of 14.4%. However, China\u0026rsquo;s trade in services also faces challenges such as regional development imbalances and an irrational trade structure, which constrain the long-term development of the sector (Jiang and Lin \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The Chinese government launched the TSP policy in February 2016, aiming to deepen reforms in trade in services and advance high-level opening up. Since the establishment of the TSP policy, its policy effects have garnered extensive attention from the academic community. Most researchers have focused on topics such as environmental quality (Xu et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), carbon total factor productivity (Yang and Zhu \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and service industry productivity (Fu et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, there is little research examining the effects of TSP policy on corporate export propensity. Based on this, this research aims to utilize the data of Chinese A-share listed companies from 2010 to 2023 to investigate the influences of the TSP policy on corporate export propensity, and further explore the underlying mechanisms and heterogeneity of these effects.\u003c/p\u003e \u003cp\u003eThe main contributions of this research are as follows. First, this study explores the effects of the TSP policy on the export propensity of enterprises, which contributes to the body of research on the relationship between the TSP policy and enterprise exports. Second, this study examines the mechanism through which the TSP policy promotes corporate exports across two dimensions, namely artificial intelligence application and digital economic development, clarifying the theoretical logic of the TSP policy\u0026rsquo;s impact on the export propensity of enterprises. Third, this research conducts heterogeneity analysis based on the dimensions of enterprise type, enterprise scale, financing constraints, ESG performance, total asset turnover, and market concentration, which provides a theoretical basis for the exploration of differentiated pathways of trade in services.\u003c/p\u003e"},{"header":"2. Literature review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 The influencing factors of corporate export\u003c/h2\u003e \u003cp\u003eAs international trade becomes increasingly frequent, factors affecting corporate exports have become a hotspot for academia. Many scholars have found that trade obstacles are a key factor affecting corporate export (Wei et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kong et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), such as distance, economic size, and comparative advantage (Chen and Li 2014). Non-tariff barriers such as import licenses (Imbruno \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), export subsidies (Defever and Ria\u0026ntilde;o \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), import discrimination (Evenett \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), technological barriers (Bao and Chen \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), anti-dumping (Felbermayr \u0026amp; Sandkamp \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Su et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and import and export quotas (Khandelwal et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) also impact corporate export activities. Therefore, trade liberalization and facilitation have also become a subject of widespread attention (Hendy and Zaki \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Most scholars have found that environmental regulations (Ye et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), transportation infrastructure (Wang et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), local government debt (Li \u0026amp; Qin \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), business credit environment (Liu et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e), and tax revenue (Federici et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) are critical factors influencing corporate exports. For example, Seck (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) utilized the World Bank\u0026rsquo;s Enterprise Surveys data to find that improving customs clearance, the regulatory environment, trade financing conditions, and energy and telecommunication infrastructure not only increases the probability of enterprises engaging in import-export trade but also expands the scale of trade activities. De Matteis et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) based on Italian manufacturing enterprise data and found that local environments and supporting services significantly influence the export performance of enterprises.\u003c/p\u003e \u003cp\u003eSome scholars have also explored factors influencing corporate exports from an enterprise standpoint. For instance, Edeh et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) studied the effects of technological and non-technological innovations on the enterprises\u0026rsquo; export performance using the firm-level data of Nigeria. Kostevc (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) examined the relationship between corporate ownership structures and participation in foreign trade activities using data from Slovenian enterprises during the period from 2005 to 2012. Yu \u0026amp; Tian (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) investigated the impact of corporate financialization on export activity behavior using data from Chinese companies spanning 2010 to 2022. Furthermore, some scholars have found that ESG performance (Ma et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), digital transformation (Guo et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), operating funds management (Mansilla-Fern\u0026aacute;ndez and Milgram-Baleix \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), corporate governance (Ramzan \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and corporate financial structure (Miravitlles et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) are also important influencing factors of corporate exports.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 The policy effects of the TSP policy\u003c/h2\u003e \u003cp\u003eSince the implementation of the TSP policy, its policy effects have attracted extensive attention from academics, with relevant studies focusing on the development of the service industry, environmental performance, and so on. For example, Li (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) employed a DID model and Chinese urban data from 2006 to 2019, finding that the TSP policy exerts a remarkable positive influence on the optimization of the service industry structure. Fu et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) held the view that the TSP policy positively impacts service industry productivity by utilizing urban data from 2006to 2019. Yang \u0026amp; Zhu (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) examined the relation of the implementation of the TSP policy and carbon total factor productivity and discovered that the TSP policy increases the total factor productivity by 6.82%, which is mainly driven by the technological innovation effect. Xu et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) found that the TSP policy substantially promotes significant improvements in regional environmental performance. Huo et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) revealed that the TSP policy significantly increased green energy consumption in pilot cities.\u003c/p\u003e \u003cp\u003eIn summary, the factors influencing corporate exports have been extensively explored, and the policy effects of the TSP policy have been thoroughly examined. However, existing literature has not sufficiently investigated how the TSP policy impacts corporate export. Therefore, this research aims to reveal the impact of the TSP policy on corporate export propensity, thereby enriching the existing literature in the fields of corporate exports and service trade.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Policy background and theoretical mechanism","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Policy background\u003c/h2\u003e \u003cp\u003eTo foster the transformation and upgrading of foreign trade and accelerate the development of trade in services, China has implemented the TSP policy in batches (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In February 2016, the Chinese government initiated the TSP policy in 10 provinces, as well as 5 national new areas, focusing on exploring the system construction across 8 aspects including the management system, development model, and facilitation of trade in services. In June 2018, the Chinese government was determined to expand the pilot scope of the TSP policy. Based on the original pilot areas, Beijing, Harbin, Nanjing, and Xiong\u0026rsquo;an New Area were added to the TSP policy. Meanwhile, 6 liberalization and facilitation initiatives, as well as 34 policy safeguard measures, were introduced. In August 2020, the Chinese government further added Chongqing, Dalian, Xiamen, and other cities to the scope of pilot cities, and put forward 8 pilot tasks and 122 specific initiatives. China is committed to developing the pilot areas into new frontiers for the international opening-up of the services industry, aiming to continuously invigorate new dynamics and optimize the business environment for trade in services.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Theoretical mechanism\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Application of artificial intelligence technology\u003c/h2\u003e \u003cp\u003eThe TSP policy promotes the adoption of artificial intelligence technologies by enterprises through enhanced financial support, the establishment of infrastructure and public platforms, and the improvement of institutional standards. First, the TSP policy encourages financial institutions to develop innovative products and supports enterprises in securing financing through multi-tiered capital markets, thereby alleviating funding pressures for artificial intelligence technology R\u0026amp;D and application. Second, the TSP Policy supports the development of digital infrastructure and public service outsourcing platforms, promoting data sharing among enterprises, governments, and specialized institutions, and provides data support for the implementation of artificial intelligence technologies. Third, the TSP policy establishes a compliance regulatory framework for artificial intelligence technology applications, clarifying rules on cross-border data flows, algorithmic security, and other areas to provide a stable institutional environment for enterprises to implement artificial intelligence technologies.\u003c/p\u003e \u003cp\u003eThe application of artificial intelligence technology enables enterprises to gain precise insights into overseas market demands, optimize the efficiency of cross-border marketing outreach, reduce cross-border trade operational costs, and thereby enhance their propensity to export. First, by applying artificial intelligence technologies to analyze target market consumption preferences and industry trends, enterprises could identify high-potential export sectors. This helps businesses clarify their market positioning, enabling products and services to better align with the needs of overseas customers (Haleem et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Second, artificial intelligence technology could lower the barriers to cross-cultural communication, enhance brand visibility and customer response speed, and efficiently establish channels for connecting with overseas clients (Deryl et al. 2023). Third, artificial intelligence technology enables end-to-end optimization of supply chains, logistics, and inventory management, reducing stockpiles through demand forecasting and saving time and costs by intelligently planning logistics routes (Lalla-Ruiz and Mes \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Simultaneously, it automates processes such as trade documentation and compliance reviews, eliminating redundant manual operations and lowering the operational costs of export activities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Empowering effects of the digital economy\u003c/h2\u003e \u003cp\u003eThe TSP policy promotes digital economic development by expanding market access in the digital sector, regulating cross-border data flows, and fostering new forms of digital trade. First, the TSP policy eases foreign investment restrictions in sectors such as telecommunications and the internet, thereby enhancing the ease of foreign investment in the digital domain and stimulating market competitiveness in the digital economy. Second, the TSP policy establishes a robust data cross-border security management system, creates efficient and convenient channels for cross-border data flow, and lays the foundation for cross-border cooperation in the digital economy. Third, the TSP policy encourages the development of new models such as cloud outsourcing, platform subcontracting, and cross-border e-commerce, driving the digital transformation of service outsourcing. It also supports the innovative development of digital product trade and digital technology trade, expands cross-border digital delivery channels, and cultivates new growth points for the digital economy.\u003c/p\u003e \u003cp\u003eThe development of the digital economy could optimize the efficiency of the entire export process, broaden access channels to overseas markets, and elevate the level of products and value chains, thereby enhancing enterprises\u0026rsquo; propensity to export. First, digital technology reshapes the entire trade process chain, enabling the digitization of order processing, document verification, customs declaration, and other procedures. This shortens business processing cycles, reduces redundant manual operations, enhances the efficiency of cross-border trade flows, and lowers the time costs and operational barriers for enterprises (Lu et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Second, the digital economy has spawned new export channels such as cross-border e-commerce, social media marketing, and online exhibitions (Xia et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These ways break down geographical and offline resource constraints, enabling businesses to connect directly with overseas end customers and buyers, thereby expanding the reach of their export operations (Lau \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Third, digital technologies optimize cross-border logistics route planning, dynamic inventory management, and financial settlement processes. By reducing inventory backlog through demand forecasting, leveraging digital tools to lower financing barriers and minimize logistics losses, while simultaneously cutting hidden costs such as marketing promotions and cross-border communication, they enhance the profitability of export operations (Qin et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Research methods and data sources","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Model setting\u003c/h2\u003e \u003cp\u003eThis paper utilizes the multi-period DID method to test the influence of the TSP policy on the export propensity of enterprises. The model is designed as follows:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{\\text{Export}}_{\\text{ict}}\\text{=}{\\text{\u0026beta;}}_{\\text{0}}\\text{+}{\\text{\u0026beta;}}_{\\text{1}}{\\text{Trade}}_{\\text{c}\\text{t}}\\text{+}{\\text{\u0026beta;}}_{\\text{2}}{\\text{X}}_{\\text{ict}}\\text{+}{\\text{\u0026mu;}}_{\\text{i}}\\text{+}{\\text{\u0026omega;}}_{\\text{t}}\\text{+}{\\text{\u0026epsilon;}}_{\\text{ict}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eHere, the subscript \u003cem\u003ei\u003c/em\u003e stands for firm, \u003cem\u003ec\u003c/em\u003e represents city, and \u003cem\u003et\u003c/em\u003e denotes year. The dependent variable \u003cem\u003eexport\u003c/em\u003e\u003csub\u003e\u003cem\u003eict\u003c/em\u003e\u003c/sub\u003e on behalf of the export propensity of firm \u003cem\u003ei\u003c/em\u003e located in city \u003cem\u003ec\u003c/em\u003e in year \u003cem\u003et\u003c/em\u003e. The independent variable \u003cem\u003eTrade\u003c/em\u003e\u003csub\u003e\u003cem\u003ect\u003c/em\u003e\u003c/sub\u003e denotes whether city \u003cem\u003ec\u003c/em\u003e became a pilot city for service trade in year \u003cem\u003et\u003c/em\u003e. \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003eict\u003c/em\u003e\u003c/sub\u003e refers to control variables affecting firm export propensity. \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e denotes the constant term. \u003cem\u003e\u0026micro;\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e denotes firm fixed effect, \u003cem\u003eω\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e represents year fixed effect, and \u003cem\u003eε\u003c/em\u003e\u003csub\u003e\u003cem\u003eict\u003c/em\u003e\u003c/sub\u003e is the random error term. This paper primarily focuses on the coefficient \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e of the independent variable. If \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e exhibits a significantly positive value, it suggests that the TSP policy significantly enhances the export propensity of firms.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Variable selection\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e4.2.1 Dependent variable\u003c/h2\u003e \u003cp\u003eThis research takes the export propensity of enterprises as the dependent variable (Lu et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Specifically, this study determines a firm\u0026rsquo;s propensity to export based on whether it has overseas sales revenue. If an enterprise has overseas sales revenue, it is assigned a value of 1; otherwise, it is assigned a value of 0.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e4.2.2 Independent variable\u003c/h2\u003e \u003cp\u003eThis study employs the implementation of the TSP policy as the independent variable. Specifically, if a sample city is designated as a pilot city and has implemented the TSP policy, the value is 1; otherwise, it is 0.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e4.2.3 Control variables\u003c/h2\u003e \u003cp\u003eTo obtain more accurate estimates, this paper also controls for other characteristics influencing that influence export propensity (Li \u0026amp; Qin \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Cai \u0026amp; Hao \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The control variables for the enterprise level are listed below. Financing constraints (\u003cem\u003efc\u003c/em\u003e) are measured using the WW index. Total factor productivity (\u003cem\u003etfp\u003c/em\u003e) measures the overall efficiency of converting production factors into output, calculated using the OLS method. Debt structure (\u003cem\u003eds\u003c/em\u003e), measured by the proportion of bank loans in total liabilities. Asset structure (\u003cem\u003eas\u003c/em\u003e), defined as the proportion of total assets represented by net fixed assets and net inventory. Cash flow (\u003cem\u003ecf\u003c/em\u003e), gauged by the proportion of net cash flow of operating activities to total assets. Total asset growth rate (\u003cem\u003etagr\u003c/em\u003e), measuring the growth pace of a firm\u0026rsquo;s asset scale. Firm age (\u003cem\u003efa\u003c/em\u003e) is expressed as the logarithm of the firm\u0026rsquo;s duration. Market concentration (\u003cem\u003emc\u003c/em\u003e) is gauged by the Herfindahl Index, calculated by summing the squares of the ratios of a firm\u0026rsquo;s primary revenue to the industry\u0026rsquo;s total primary revenue. ESG performance (\u003cem\u003eesg\u003c/em\u003e) is gauged by the ESG score. Shareholding concentration (\u003cem\u003esc\u003c/em\u003e) is expressed as the combined ownership share of the top ten shareholders. Long-term debt-to-assets ratio (\u003cem\u003eltdar\u003c/em\u003e) is defined as the proportion of aggregate long-term debt to total assets. Additionally, this paper also selects a series of city-level control variables. Economic development level (\u003cem\u003elngdp\u003c/em\u003e), expressed as the logarithm of urban gross domestic product. Population size (\u003cem\u003elnps\u003c/em\u003e), measured using the logarithm of the total resident population. Freight capacity (\u003cem\u003elnfc\u003c/em\u003e), measured using the logarithm of the city\u0026rsquo;s total freight volume. The level of service industry development (\u003cem\u003eser\u003c/em\u003e), measured by the share of tertiary industry output in GDP. The level of industrial structure development (\u003cem\u003eis\u003c/em\u003e), measured by the ratio of tertiary industry output to secondary industry output. The descriptive statistics are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eexport propensity\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003etrade\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003efc\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;1.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;1.245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.840\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003etfp\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eds\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eas\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.944\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ecf\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;1.938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003etagr\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41.463\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003efa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.526\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003emc\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eesg\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003esc\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.979\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eltdar\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003elngdp\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.818\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003elnps\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.450\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003elnfc\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.436\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eser\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.897\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.620\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Data sources\u003c/h2\u003e \u003cp\u003eThe data at the enterprise level are sourced from the China Stock Market and Accounting Research (CSMAR) database, and city-level data are obtained from the China Urban Statistical Yearbook. Additionally, to secure the effectiveness of the sample data, this study excluded companies with risk warnings (PT, ST, and ST*), companies with a debt-to-asset ratio greater than 1 or less than 0, and companies in the financial industry (Lu et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Results and discussions","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Baseline regression\u003c/h2\u003e \u003cp\u003eThe baseline results of the TSP policy on the export propensity of enterprises are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Column (1) displays the estimation results controlling only for firm-level control variables. We find that the estimation coefficient for the core explanatory variable is 0.026, significant at the 10% level. Column (2) further controls for city-level covariates on top of column (1). We observe that the estimation coefficient for the core independent variable is 0.033, which is significantly positive at the 5% significance level. To summarize, the TSP policy exerts a significant positive influence on enhancing the export propensity of firms.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe results of baseline regression.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExport propensity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExport propensity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTrade\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.026*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.033**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.013)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFirm controls\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCity controls\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFirm FE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eYear FE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eObservations\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19,593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19,589\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAdjusted R\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.734\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNotes: *, **, and *** indicate significance at the 10%, 5%, and 1% confidence levels, respectively. The standard error is shown in parentheses. If not specified otherwise, the following tables are the same.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Robustness tests\u003c/h2\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e5.2.1 Parallel trend test\u003c/h2\u003e \u003cp\u003eOne of the fundamental assumptions required for the DID model is the parallel trends assumption, which ensures comparability between the treatment and control groups before and after policy implementation. Thus, we employ the event study method to conduct the parallel trend test (Xu et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The model settings are as follows:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{\\text{Export}}_{\\text{ict}}\\text{=}{\\text{\u0026beta;}}_{\\text{0}}\\text{+}\\sum\\:_{\\text{\u0026theta;=}-\\text{6}}^{\\text{7}}{\\text{\u0026beta;}}_{\\theta}\\text{\u0026times;}{\\text{treat}}_{\\text{t}}\\text{\u0026times;}{\\text{post}}_{\\text{ic}}\\text{+}{\\text{\u0026beta;}}_{\\text{2}}{\\text{X}}_{\\text{ict}}\\text{+}{\\text{\u0026mu;}}_{\\text{i}}\\text{+}{\\text{\u0026omega;}}_{\\text{t}}\\text{+}{\\text{\u0026epsilon;}}_{\\text{ict}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), the model constructs the DID estimator through \u003cem\u003epost\u003c/em\u003e\u003csub\u003e\u003cem\u003eic\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003etreat\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e to represent the DID estimator. \u003cem\u003epost\u003c/em\u003e\u003csub\u003e\u003cem\u003eic\u003c/em\u003e\u003c/sub\u003e signifies the implementation of the TSP policy, and \u003cem\u003etreat\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e refers to whether or not it is a pilot city. \u003cem\u003eθ\u003c/em\u003e signifies the year of implementation for the TSP policy. The coefficient \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003eθ\u003c/em\u003e\u003c/sub\u003e quantifies the disparity in export intentions between the two groups during the implementation year \u003cem\u003eθ\u003c/em\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e demonstrates the results of the parallel trend test. We observe that none of the estimated coefficients are significant before the TSP policy is introduced, indicating no systematic differences in export intentions between firms in the treatment and control groups. After the TSP policy implementation, all the coefficients \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003eθ\u003c/em\u003e\u003c/sub\u003e exhibit significant positive values. This indicates a marked divergence in export intentions between treated and control group firms since the implementation of the TSP policy, with treatment group firms demonstrating significantly higher export intentions. Therefore, the DID model passed the parallel trends test, making it appropriate to use the DID model to identify the export-promoting effects of the TSP policy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e5.2.2 Heterogeneity treatment effect diagnosis\u003c/h2\u003e \u003cp\u003eThe essence of a multi-period DID estimator is a weighted average. Due to heterogeneity in treatment effects across groups and time periods, the same policy intervention may produce varying outcomes for different individuals. Even when the parallel trends assumption holds, estimates of treatment effects may still be biased (de Chaisemartin and D\u0026rsquo;Haultfœuille \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, this paper diagnoses the treatment effects of heterogeneity.\u003c/p\u003e \u003cp\u003eFirst, we carry out a Bacon decomposition. The results of Bacon decomposition can be seen in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. We find that the estimated coefficient of the inappropriate treatment effect (Later treated vs Earlier control) is 0.010 with a weight of 5.5%. It indicates that the treatment effect exhibits mild heterogeneity across groups, but does not cause serious bias in the estimation results (Goodman-Bacon \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, the estimates obtained in this paper using the multi-period DID model are robust.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe results of Bacon decomposition.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBacon decomposition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstimated coefficient\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEarlier treated vs Later control\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLater treated vs Earlier control\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTreated vs Never treated\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTreated vs Already treated\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eWeighted estimated coefficient\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSecond, we conduct a negative weight decomposition. The estimated coefficients of the DID can be viewed as a weighted average of multiple 2\u0026times;2-DID estimates. Although the sum of the overall weights is 1, its sign can be either positive or negative, and this negative weight is the source of estimation bias. Therefore, this paper employs the method proposed by Chaisemartin \u0026amp; D\u0026rsquo;Haultfœuille (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) to decompose all the weights, and the results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. It is found that among all the 2\u0026times;2DID terms, there are 4283 positive weights and 770 negative weights, and the percentage of negative weights is only 15.24%, which indicates that the results are robust.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe results of negative weight decomposition .\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWeight decomposition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e# ATTs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eΣweights\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePositive weights\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNegative weights\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTotal\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTrade\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFinally, we employ a heterogeneity robust estimator for regression analysis. To tackle the estimation bias inherent in bidirectional fixed effects models, Borusyak et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)developed an innovative interpolation-based counterfactual approach. The results of the interpolated estimator are presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. We discover that the coefficient of the independent variable is positive, which provides evidence for the validity of the research conclusions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe results of the heterogeneity robust estimator.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExport propensity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTrade_imputation\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.030**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.014)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eControls\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFirm FE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eYear FE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e5.2.3 Placebo test\u003c/h2\u003e \u003cp\u003eTo demonstrate that the effect of the TSP policy on corporate export propensity does not stem from other stochastic sources, this study conducts a placebo test. We construct pseudo-policy variables through 500 random samples based on the distribution of the independent variable, and perform regression estimation using the benchmark regression model (Ferrara et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. We discover that the pseudo-policy variables generated through randomization are mainly concentrated around 0, which is much smaller than the baseline regression results, and the majority of p-values are larger than 0.1. This confirms the validity of the findings drawn in this research.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e5.2.4 Excluding the effect of contemporaneous policies\u003c/h2\u003e \u003cp\u003eThroughout the observation period, China also implemented policies such as the Free the free trade zone (FTZ) policy and Cross-border e-commerce (CBEC) policy. Their potential influence may potentially confound research outcomes, thereby introducing bias into the analysis. To address these mixed factors, we incorporate the aforementioned policies as control variables into the baseline model for regression analysis, and the results are illustrated in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. We find that the coefficients of the TSP policy remain significantly positive, whether controlling for individual policies or simultaneously controlling for all policies.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe results of excluding the effect of contemporaneous policies.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExport propensity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExport propensity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExport propensity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTrade\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.031**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.036**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.034**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.014)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFTZ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.010)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCBEC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.011)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eControls\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFirm FE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eYear FE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eObservations\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19,589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19,589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19,589\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAdjusted R\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.734\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e5.2.5 Replacing fixed effects and standard errors\u003c/h2\u003e \u003cp\u003eSince there exist differences in export propensities among firms in different industries, we should exclude the interference of the industry\u0026rsquo;s characteristics on exports to correctly identify the net effects of the TSP policy. Therefore, we control for industry fixed effects in the benchmark model, with results displayed in column (1) of Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. We find that the estimation coefficient of the TSP policy shows a significant positive value. Differences in economic base, infrastructure, and openness among cities also affect firms\u0026rsquo; exports. To remove the confounding role of geographical factors, we further control for city fixed effects in the baseline model, with results presented in column (2) of Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The estimation results of the TSP policy remain significantly positive. In addition, firms within the same industry may face a similar market environment or policy shocks, and the error term is prone to intra-industry autocorrelation. Industry clustering robust standard errors could correct the estimation bias caused by such correlation and make the statistical inference more reliable. Therefore, we employ industry clustering robust standard errors for estimation while controlling for industry and city fixed effects, and the results are given in column (3) of Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. We discover that the estimation results of the TSP policy remain significantly positive.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe results of replacing fixed effects and standard errors.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExport propensity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExport propensity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExport propensity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTrade\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.031**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.025*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.025*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.013)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.396)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.457)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.460)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eControls\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFirm FE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eYear FE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eIndustry FE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCity FE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eIndustry clustering error\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eObservations\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19,588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19,581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19,581\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAdjusted R\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.746\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e5.2.6 Considering the lag in policy effectiveness\u003c/h2\u003e \u003cp\u003eConsidering that the policy implementation effects of the TSP policy may not materialize immediately, but instead exhibit a certain degree of lag. Therefore, this study performs regression estimation with the independent variable lagged by one year and two years, respectively. The regression results are given in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. We find that the coefficient of the TSP policy is still notably beneficial, which proves the confidence of the study\u0026rsquo;s findings.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe results of replacing fixed effects and standard errors.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExport propensity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExport propensity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eL.Trade\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.027**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eL2.Trade\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.022*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.011)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eControls\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFirm FE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eYear FE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eObservations\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18,519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16,484\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAdjusted R\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.781\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e5.2.7 Shortening the sample time\u003c/h2\u003e \u003cp\u003eDuring the sample observation period, public events such as stock market volatility and the COVID-19 pandemic also occurred, which may have exerted some influence on the estimation results. To mitigate the adverse effects of stock market volatility on the estimation results of this paper, we exclude the 2015 data for re-regression estimation (Chen et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), with the results reported in column (1) of Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e. We find that the impact of TSP policy on firms\u0026rsquo; propensity to export is positive at the 5% statistical level. In addition, to eliminate the influence of the COVID-19 pandemic on the results, this research performs the regression again by excluding the sample data of 2020, with the results displayed in column (2) of Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e. It can be observed that the effects of TSP policy on firms\u0026rsquo; propensity to export are still significantly positive. Furthermore, we also exclude both stock market volatility in 2015 and the COVID-19 pandemic in 2020, with results shown in column (3) of Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e. We observe that the core explanatory variable still remains significantly positive.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe results of excluding public event effects.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExport propensity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExport propensity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExport propensity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTrade\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.033**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.033**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.034**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.015)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eControls\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFirm FE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eYear FE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eObservations\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18,158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17,841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16,410\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAdjusted R\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.728\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003e5.2.8 Endogeneity treatment\u003c/h2\u003e \u003cp\u003eThe choice of pilot regions for the TSP policy is far from random. The government may consider factors such as a city\u0026rsquo;s economic foundation and level of service industry development when designating pilot areas for the TSP policy. Therefore, this research adopts two methods, namely, propensity score matching (PSM-DID) and entropy balance method (EBM-DID), to overcome endogeneity issues (Liu et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis research employs the PSM-DID model for regression estimation. First, we utilize propensity score matching to reconstruct the control group. All control variables are used as covariates, and the logit model is used for propensity score matching with kernel matching. Then, the new control group and DID model are used for regression, with results shown in column (1) of Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e. We discover that the estimated coefficient of the TSP policy is significantly positive. However, the PSM method focuses only on the propensity score and does not guarantee that the difference in moments of each covariate between the treatment and control groups is reduced. The entropy balancing method is able to balance the distribution of covariates between the treatment and control groups. Therefore, this research adopts the entropy balancing method to re-match the control group. Specifically, all covariates are incorporated as matching variables into the linear entropy balancing process, and the sample is weighted using the weights generated by the entropy balancing method. The results are given in column (2) of Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e. We find that the coefficient of the independent variable remains significantly positive, indicating that the TSP policy has a significant promotion effect on the export propensity of enterprises.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe results of the PSD-DID and EBM-DID models.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePSM-DID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEBM-DID\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTrade\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.033**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.034*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.018)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eControls\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFirm FE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eYear FE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eObservations\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19,562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19,589\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAdjusted R\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.759\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Transmission mechanism test\u003c/h2\u003e \u003cp\u003eAccording to the preceding theoretical analysis, the TSP policy may influence corporate export propensity through the application of artificial intelligence technologies and the digital economy. Therefore, this section conducts empirical tests on the aforementioned mechanisms.\u003c/p\u003e \u003cp\u003eThis study employs machine learning methods to construct an artificial intelligence technology application lexicon, extracting keyword frequencies related to MD\u0026amp;AAI from listed company annual reports. The logarithmic values of the sum of AI-related word frequencies in the MD\u0026amp;A section of these reports serve as a proxy variable for AI technology adoption. The regression result of the TSP policy on corporate AI technology adoption is given in column (1) of Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e. We observe that the TSP policy significantly promotes corporate AI technology adoption, indicating that AI application represents a key pathway through which the TSP policy affects corporate export orientation. Furthermore, this study employs the Peking University Digital Inclusive Finance Index to measure urban digital economic development levels. Subsequently, a regression analysis of TSP policy effects on this index is conducted, with results displayed in column (2) of Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e. We discover that the TSP policy significantly impacts digital economic development at the 1% statistical significance level. It suggests that TSP policy promotes digital economic growth, thereby enhancing corporate export propensity.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe results of the transmission mechanism test.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI technology adoption\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDigital economic development\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTrade\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.114***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.396***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.031)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.426)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eControls\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFirm FE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eYear FE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eObservations\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19,482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16,808\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAdjusted R\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Heterogeneity analysis\u003c/h2\u003e \u003cdiv id=\"Sec30\" class=\"Section3\"\u003e \u003ch2\u003e5.4.1 Enterprise type\u003c/h2\u003e \u003cp\u003eAs there are some differences in policy support and resource tilting among enterprises of different ownership, their willingness to export may be different. This paper categorizes the sample enterprises into two groups of state-owned enterprises and non-state-owned enterprises for regression estimation, and the results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. We find that the TSP policy exerts a significant positive effect on the export propensity of state-owned enterprises, but the impact on non-state-owned enterprises is insignificant. There may be the following reasons. First, state-owned firms are closely linked to the government and are more likely to obtain policy resources such as financial support, approval facilitation, and tax incentives to accompany the TSP policy. Secondly, the institutional mechanism of state-owned enterprises is relatively sound, and they can quickly organize internal resources and formulate corresponding development strategies and implementation plans in response to the TSP policy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section3\"\u003e \u003ch2\u003e5.4.2 Enterprise scale\u003c/h2\u003e \u003cp\u003eThe impact of the TSP policy on firms\u0026rsquo; propensity to export may vary by firm size. Therefore, this paper applies the classification of firms with asset size above the 75% quartile as large enterprises, firms below the 25% quartile as small enterprises, and those between the 25% and 75% quartile as medium-sized enterprises. We examine whether the TSP policy has differential impacts on enterprises of different sizes, with the results shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. We find that the influence of the TSP policy on large firms is statistically significantly positive, but not for medium enterprises and small enterprises. This may be due to the following reasons. On one hand, large enterprises possess robust organizational structures and resource reserves, enabling them to swiftly align with various policy support measures, efficiently integrate policy resources into export business advantages, and reduce export costs. On the other hand, leveraging the supply chain integration capabilities, large enterprises can deeply coordinate the benefits from pilot policies with internal production, sales, and service operations. This optimizes the efficiency of the entire export process, enhances profit margins in the export business, and ultimately stimulates export enthusiasm.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section3\"\u003e \u003ch2\u003e5.4.3 Financing constraints\u003c/h2\u003e \u003cp\u003eFinancing constraints also play a significant role in influencing export propensity. This study divides the sample enterprises into two groups, namely high and low financing constraints, according to the median of the WW Index value for regression analysis, and the results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. It is discovered that the effect of the TSP policy is significantly positive on firms with low financing restrictions, but not on firms with high borrowing restrictions. The TSP policy offers diversified financing services, providing enterprises with additional avenues for capital acquisition. Enterprises with low financing restrictions could leverage the TSP policy to further supplement funds required for export operations, thereby enhancing their propensity to export.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec33\" class=\"Section3\"\u003e \u003ch2\u003e5.4.4 ESG performance\u003c/h2\u003e \u003cp\u003eThis paper divides firms\u0026rsquo; ESG scores into two categories of high ESG performance and low ESG performance based on the median and performs regression estimation, with the results shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. It is found that the impact of the TSP policy is significantly positive for firms with high ESG performance, but not significant for firms with low ESG performance. This may be due to the following reasons. On the one hand, the TSP policy emphasizes cultivating enterprises\u0026rsquo; sustainable development capabilities. Companies with high ESG scores could leverage policy endorsement to further enhance their recognition in international markets and strengthen their competitiveness, thereby more actively expanding export operations. On the other hand, the TSP policy directs resources toward enterprises aligned with green and sustainable development principles. Companies with high ESG ratings, backed by sound governance structures and development philosophies, could more efficiently integrate policy and market resources, providing robust support for export operations and thereby boosting their willingness to export.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section3\"\u003e \u003ch2\u003e5.4.5 Total asset turnover\u003c/h2\u003e \u003cp\u003eThis paper conducts regression estimation by dividing enterprises into high-turnover and low-turnover groups based on median total asset turnover rates (Guo et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), with results presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. It is found that the effect of the TSP policy is significantly positive for firms with high-turnover enterprises, while the effect is insignificant for low-turnover enterprises. The TSP policy could reduce export costs through trade facilitation reforms and resource allocation measures. Enterprises with high asset turnover rates could swiftly capitalize on these policy benefits by leveraging efficient resource allocation and operational capabilities, transforming policy advantages into competitive strengths for export operations and thereby enhancing export propensity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec35\" class=\"Section3\"\u003e \u003ch2\u003e5.4.6 Market concentration\u003c/h2\u003e \u003cp\u003eThis paper categorizes market concentration into high-concentration and low-concentration groups based on the median Herfindahl index, conducting separate regression estimates for each group. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents the estimation results. We find that the impact of the TSP policy is significantly positive for firms with high market concentration, but not for those with low market concentration. Enterprises with high market concentration wield greater influence within their industries and possess stronger international bargaining power. The TSP policy provides trade facilitation support and international cooperation channels that further enhance their pricing advantages, thereby increasing the propensity to export.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec36\" class=\"Section2\"\u003e \u003ch2\u003e5.5 Further analysis\u003c/h2\u003e \u003cp\u003eThe aforementioned research confirms that the implementation of the TSP policy significantly enhances the export propensity of firms. So, does the TSP policy also lead to growth in export revenue for enterprises? This study further incorporates the logarithm of overseas business revenue as the dependent variable into the baseline regression model for estimation, with results shown in Table\u0026nbsp;\u003cspan refid=\"Tab12\" class=\"InternalRef\"\u003e12\u003c/span\u003e. It can be observed that the implementation of the TSP policy also significantly increases export revenue for enterprises.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab12\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 12\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe results of further analysis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExport revenue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExport revenue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTrade\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.452***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.618**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.261)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eControls\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFirm FE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eYear FE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eObservations\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32,282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19,590\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAdjusted R\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.761\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"6 Conclusions and policy implications","content":"\u003cdiv id=\"Sec38\" class=\"Section2\"\u003e \u003ch2\u003e6.1 Conclusions\u003c/h2\u003e \u003cp\u003eThis paper investigates the influence of the TSP policy on the export propensity of firms by using the data of China\u0026rsquo;s A-share listed companies from 2010 to 2023 and applying the multi-period DID model. The main findings are as follows. First, the implementation of the TSP policy significantly increases export propensity among enterprises. Second, the impact of the TSP policy on corporate exports is heterogeneous. Specifically, this promotional benefit is more significant in state-owned firms, large-scale firms, low financing constraint firms, high ESG score firms, high asset turnover firms, and high market concentration firms. Third, enterprise artificial intelligence applications and digital economic development serve as key channels through which the TSP policy influences corporate export propensity. In addition, the implementation of the TSP policy also increases the export revenue of enterprises.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec39\" class=\"Section2\"\u003e \u003ch2\u003e6.2 Policy implications\u003c/h2\u003e \u003cp\u003eFirst, the government shall deepen institutional reforms of the TSP policy and optimize policy implementation effectiveness. It is essential to continuously refine the policy framework for the TSP policy, deepen reforms to the negative list management system, and fully align with international high-standard economic and trade rules. Expand the coverage of the TSP policy, taking into account development disparities among eastern, central, and western regions, and promote the phased rollout of pilot experiences. Policy precision must be strengthened by formulating differentiated support measures for services trade tailored to the industrial foundations and development needs of various regions, with a focus on optimizing trade facilitation in high-end sectors such as digital services and R\u0026amp;D services. Additionally, there is a need to refine the policy evaluation and dynamic adjustment mechanism, regularly monitor the implementation outcomes of policies, promptly address difficulties encountered by enterprises in benefiting from these policies, and ensure that policy dividends are efficiently transmitted to market entities.\u003c/p\u003e \u003cp\u003eSecond, develop targeted and differentiated support policies tailored to the development characteristics of different types of enterprises. Specifically, intensify policy guidance for state-owned enterprises, encouraging them to leverage their resource integration and strategic leadership capabilities to proactively expand into international markets. Enhance support for large firms to empower industrial chains, leveraging their scale advantages and international deployment experience to drive upstream and downstream small and medium-sized enterprises into the global division of labor and collaboration. For enterprises with low financing constraints, increase policy support, such as additional deductions for R\u0026amp;D expenses and tax reductions, to encourage them to expand R\u0026amp;D investment and enhance their technological innovation capabilities. Support enterprises with high ESG scores in developing green service trade, encouraging them to expand international operations in energy conservation, environmental protection, clean energy, carbon trading, and related fields. For enterprises with high asset turnover rates, prioritize securing resources such as production land and energy supply to support their expansion of service trade exports. Encourage enterprises with high market concentration to increase investment in technological R\u0026amp;D and innovation, take the lead in establishing industry technical standards and service specifications, and enhance the overall competitiveness of the sector.\u003c/p\u003e \u003cp\u003eThird, improve supporting systems to enhance the quality of service in trade development. Optimize conditions for service trade development at the city level, increase investment in transportation and logistics infrastructure, enhance freight capacity and cross-border logistics efficiency, and reduce export logistics costs for enterprises. Improve local service trade statistical systems and regulatory models, refine supporting policies for the tertiary industry, promote industrial structure optimization and upgrading, and lay an industrial foundation for service trade development. Strengthen the business environment by streamlining customs declaration, foreign exchange settlement, and other procedures, shortening processing cycles, and improving efficiency across the entire cross-border trade process. Establish and improve risk prevention and control mechanisms. Utilize technologies like artificial intelligence to enhance cross-border trade compliance and risk management capabilities, assist enterprises in addressing uncertainties in international markets, and strengthen export stability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec40\" class=\"Section2\"\u003e \u003ch2\u003e6.3 Limitations\u003c/h2\u003e \u003cp\u003eThis study still has some shortcomings that require further refinement. First, although this paper employs methods such as PSM-DID and entropy balancing to mitigate self-selection issues, it does not eliminate the potential influence of omitted variables. Second, due to data availability, this study only selects Chinese A-share listed companies as samples and does not include non-listed enterprises.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eShitong Li: Writing-original draft, Methodology, Conceptualization, Formal analysis. Yaoao Li: Writing-original draft, Software, Data curation, Project administration, Formal analysis.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBao X, Chen W (2013) The Impacts of Technical Barriers to Trade on Different Components of International Trade. Review Development Economics 17:447\u0026ndash;460. https://doi.org/10.1111/rode.12042\u003c/li\u003e\n\u003cli\u003eBekkers E, Corong E, M\u0026eacute;tivier J, Orlov D (2024) How will global trade patterns evolve in the long run? 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Journal of Cleaner Production 290:125868. https://doi.org/10.1016/j.jclepro.2021.125868\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"the-annals-of-regional-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"arsc","sideBox":"Learn more about [The Annals of Regional Science](https://link.springer.com/journal/168)","snPcode":"168","submissionUrl":"https://submission.springernature.com/new-submission/168/3","title":"The Annals of Regional Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Trade in services, Export propensity, DID model, China","lastPublishedDoi":"10.21203/rs.3.rs-9188555/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9188555/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTrade in services constitutes a vital component of international trade and a significant domain of global economic cooperation. Taking China\u0026rsquo;s innovative pilot policy on trade in services (the TSP policy) as a quasi-natural experiment, this research employs a difference-in-differences (DID) model and Chinese A-share listed enterprises from 2010 to 2023 to investigate the effect of the TSP policy on enterprises\u0026rsquo; export propensity. The empirical results reveal three key findings. First, the implementation of the TSP policy significantly enhances the export propensity of enterprises. Second, the impact of the TSP policy on enterprises\u0026rsquo; export propensity exhibits substantial heterogeneity. Specifically, the promotional effect is more pronounced in state-owned firms, large-scale firms, firms with low financing constraints, firms with high ESG scores, firms with high asset turnover, and firms in markets with high concentration. Third, artificial intelligence application and digital economic development serve as the critical transmission channels through which the TSP policy affects corporate export propensity. Additionally, the implementation of the TSP policy could also increase export revenues for enterprises. This research contributes to the existing research on the economic effects of the TSP policy and provides valuable insights for policymakers to optimize export promotion policies.\u003c/p\u003e","manuscriptTitle":"Does institutional innovation in trade in services enhance the export propensity of enterprises? Evidence from China’s innovative pilot policy on trade in services","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-09 11:20:37","doi":"10.21203/rs.3.rs-9188555/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"133817595430031565262281340328093538629","date":"2026-04-27T05:00:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"184940715151739594767601951655718076784","date":"2026-04-20T12:33:32+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-02T11:17:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-25T14:52:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-25T09:41:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"The Annals of Regional Science","date":"2026-03-22T01:49:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-annals-of-regional-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"arsc","sideBox":"Learn more about [The Annals of Regional Science](https://link.springer.com/journal/168)","snPcode":"168","submissionUrl":"https://submission.springernature.com/new-submission/168/3","title":"The Annals of Regional Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"09ddc856-fcec-4af6-932f-c08ca3d9e813","owner":[],"postedDate":"April 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-09T11:20:37+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-09 11:20:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9188555","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9188555","identity":"rs-9188555","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}