The National Innovation City Pilot Policy is the “the highlight” for China to implement its innovation-driven development strategy, and it is also a hot topic widely studied by all sectors of society. In recent years, domestic scholars have actively explored this pilot policy. Quantitative evaluation of the implementation effect of the National Innovation City Pilot Policy, for example, Zou (2012), Zhou and Shen (2013) respectively used cluster analysis and factor analysis to quantitatively evaluate the structure and performance of China’s innovative city construction; Wang et al. (2018) constructed a two-stage DEA model to evaluate the innovation efficiency of innovative pilot cities in the Yangtze River Delta region. On the other hand, scholars use the quasi-natural experiment of the National Innovation City Pilot Policy to focus on the dynamic effects of the policy on urban innovation and entrepreneurship, economic resilience, green development and other fields. Representative studies include: Nie and Liu (2019) who found that the construction of national innovative cities can improve the quality of FDI through the “loop effect”; Xu and Jiang (2020) who found that the national innovative city PILOT POLICY can significantly improve urban innovation, but the policy effect has weakened year by year; Li and Yang (2019) whose research conclusions show that the effectiveness of the innovative city pilot policy on improving the innovation level shows a change characteristic of first strengthening and then weakening; Nie et al. (2021) who found that the construction of innovative cities can significantly improve green total factor productivity; Chen C. et al. (2022), who found that the innovative city pilot policy can improve the level of urban green innovation and has a certain spatial spillover effect; Cao et al. (2022) who based on the framework of the heterogeneous enterprise monopolistic competition model, found that the construction of national innovative pilot cities has significantly improved the level of enterprise technological innovation, including R&D investment, quantity and quality of innovation output, etc.

As a key factor affecting the urban–rural income gap, research on the impact of industrial structure optimization on the urban–rural income gap and its path of action has accumulated rich empirical results. However, existing research has not yet reached a consensus. Scholars have found that changes in industrial structure will widen the income gap between urban and rural residents. For example, Gao (2011) found that there is a long-term stable positive equilibrium relationship between changes in China’s industrial structure and the urban–rural income gap; Lin and Chen (2013) pointed out that the evolution of industrial structure is the core variable for understanding China’s income distribution structure and income gap. The heavy industry priority development strategy will reduce labor demand, thereby reducing equilibrium wages and workers’ income, leading to a widening income gap; Jing and Chen (2017) pointed out that when studying how FDI affects the urban–rural income gap through industrial structure upgrading, production factors such as talents and resources return or gather to the secondary and tertiary industries in cities, thereby forming a “low-end lock” on rural areas. The “center-periphery” phenomenon will widen the income gap. The research conclusions of some scholars show that industrial structure upgrading can narrow the urban–rural income gap. Among them, Liu and Lv (2011) found that the increase in the proportion of the primary and secondary industries can increase employment opportunities for rural residents, improve rural residents’ income, and thus narrow the income gap; Yang et al. (2018) pointed out that with the upgrading of the industrial structure, the prices of production factors such as land and labor in cities have risen, which has promoted the migration of industries to low-cost rural areas, increased employment opportunities and income for rural residents, and helped to slow down the widening of the urban–rural income gap. In addition, Mu and Wu (2016) found that the adjustment of industrial structure and the urban–rural income gap show an inverted “U” relationship, and pointed out that the relationship between the two depends on the relative size of the marginal output elasticity of capital and land. Coincidentally, Gong et al. (2017) and Zhou and Chen (2021) each used a threshold regression model to draw the conclusion that there is a nonlinear relationship between industrial structure upgrading and the urban–rural income gap.

With a deeper understanding of the industrial structure, scholars have begun to conduct research from the perspective of advanced and rationalized industrial structure (Deng et al., 2020; Zhang, 2016; Qian et al., 2011). Research on the impact of industrial structure and urban–rural income gap has also been further enriched and expanded. Representatively, Chen (2014) fully considered the rationalization and upgrading characteristics of the industrial structure. The empirical test results show that the rationalization of the industrial structure is conducive to the narrowing of the urban–rural income gap, and the upgrading of the industrial structure. The income gap between urban and rural areas has significantly widened. When Wu et al. (2018) explored the impact of industrial structure changes on income inequality in the Chinese context, the study found that the rationalization of industrial structure can positively promote income distribution; however, when time trends and regional effects are not controlled, the industrial structure Gentrification has a negative impact on income inequality.

In the innumerable volumes of existing documents, most of the research on the impact of the national innovative city PILOT POLICY focuses on quantitative evaluation and the dynamic effect analysis of the policy on the innovation level of cities and enterprises. Few literatures pay attention to and systematically examine the impact of this policy shock on the urban–rural income gap. This study is the first to explore whether there is a causal effect between the pilot policy and the urban–rural income gap. As a key factor affecting the urban–rural income gap and a core variable of the national innovative city PILOT POLICY, the industrial structure is naturally an important bridge, what roles do the advanced and rational industrial structure play in this impact mechanism? In summary, based on the research perspective of industrial structure optimization, this paper deeply explores the impact and internal mechanism of the national innovative pilot policy on the urban–rural income gap, and at the same time provides a new research perspective for existing research in this field.

The creation of national innovative city pilots is a major measure for China to implement the innovation-driven development strategy. This pilot policy impact has not only achieved technological innovation effects and results, but also had an impact on the urban–rural income gap that cannot be ignored. Zeng et al. (2022) found that in developing countries, the improvement of regional innovation levels will lead to the widening of the urban–rural income gap through two mechanisms: the “output effect” of innovation activities and the “erosion effect” on unskilled labor. In the process of creating national innovative pilot cities, the most direct result is to improve innovation efficiency and output and promote technological progress. However, the bias of technological progress will have a profound impact on the demand structure of the labor market and the polarization of the labor employment structure (Zeng et al., 2022; Acemoglu et al., 2012). Currently, it is a well-established fact that skilled labor is predominantly concentrated in urban areas, whereas rural regions are largely composed of unskilled workers (Li and Yan, 2016). Skill-biased technological progress not only increases the demand for skilled urban labor, but also triggers a skill premium (Dong and Wang, 2011; Wilfred, 2005). On the one hand, technological innovation will endogenously favor urban labor with higher skill levels (Acemoglu et al., 2012), which will reduce the employment opportunities and income levels of rural unskilled labor, and the wage gap between urban high-skilled labor and rural migrant workers with lower skill levels will widen accordingly; on the other hand, under China’s “urban–rural dual structure” of innovation, the remuneration growth of urban high-skilled labor engaged in innovation activities is faster than that of rural low-skilled labor engaged in traditional industries (Glaeser, 1998), and the wage gap between urban and rural labor will gradually widen (Shin, 2016). Based on the above analysis, this paper proposes the following research hypothesis:

According to the latest the national innovative city PILOT POLICY has effectively promoted the industrial upgrading of cities and the optimization of regional industrial structure. The pilot policy has led to the concentration and aggregation of innovation resources including science, technology, education, and talent, which is not only conducive to the learning and diffusion of technology and knowledge, and the improvement of industrial production and innovation efficiency, but also the aggregation of innovation factors has reduced market uncertainty to a certain extent, which can stimulate the enthusiasm for independent innovation and achieve the transfer to high-efficiency production departments, thereby promoting the upgrading of industrial structure (Yang and Fu, 2017). The impact of innovative city construction on the optimization and upgrading of industrial structure is mainly reflected in the two channels of industrial structure upgrading and rationalization (Wang et al., 2020; Zhao and Jia, 2019). Among them, industrial structure upgrading refers to the gradual shift of the focus of the industrial structure from the primary industry to the secondary and tertiary industries, which is reflected in the increase of industrial output value and the ratio of industrial employment; Industrial structure rationalization refers to the process of adjusting the unreasonable industrial structure based on scientific and technological level, consumption structure and resources to achieve the rational allocation and coordinated development of production factors, reflecting the degree of coordination between industries and the degree of coupling between factor input structure and output structure (Gan et al., 2011).

In order to fully implement the innovation-driven development strategy, China has formulated a general plan for building national innovative cities and adopted the implementation method of “pilot first, gradually expanding in batches.” In 2008, the National Development and Reform Commission approved Shenzhen as the first pilot city in the country to create a national innovative city, in order to use the “government licensing” method to create a city with strong independent innovation capabilities and outstanding scientific and technological support and leadership. In 2010, the scope of the pilot was further expanded. In the same year, a total of 44 cities (districts) were approved to enter the list of national innovative cities, reaching the largest scale of the entire gradual reform process. By 2018, 78 cities (districts) had carried out national innovative city pilots and developed into a number of strategic fulcrums for scientific and technological self-reliance. According to the latest “National Innovation City Innovation Capacity Evaluation Report 2022,” by the end of 2022, the Ministry of Science and Technology has supported 103 cities (districts) to build national innovative cities.

In establishing national innovative cities, municipalities and districts employ independent innovation as their primary driving force. While promoting the upgrading of leading industries, they also cultivate strategic emerging industries such as new energy, new materials, and biomedicine, develop high-tech industries and modern service industries, accelerate the process of high-tech transformation of traditional industries, and optimize the industrial structure. It can be seen that the national innovative city PILOT POLICY has significantly promoted the transformation and upgrading of the industrial structure. At the same time, the impact of the evolution of the industrial structure on the urban income distribution structure is also worthy of our attention and concern (Lin and Chen, 2013). This research investigates whether the National Innovative City pilot policy exerts a causal impact on industrial structure at the prefecture‑level and on the urban–rural income gap. Drawing on the relevant literature and policy context, it then undertakes detailed empirical analysis and mechanism testing.

Among them, in Formula 6, y imt represents the proportion of the region’s first industry in the regional GDP during the period; Y imt is the industry’s added value; L imt is the number of employees; in Formula 7, l imt represents the proportion of the number of employees in the m industry in the region i during the period to the total number of employees, and the other parameters are the same as above.

This study selected unbalanced panel data from 280 prefecture-level cities from 2006 to 2018 for empirical research. The data came from the “China City Statistical Yearbook” and “Statistical Communiqué on National Economic and Social Development” of various regions over the years. Among them, this paper did not include the urban areas of the four municipalities directly under the central government in the investigation, because the municipalities directly under the central government only took a certain district as a pilot (i.e., Haidian District, Beijing, Binhai New Area, Tianjin, Yangpu District, Shanghai, and Shapingba District, Chongqing). If it was used as a pilot city, the impact of the pilot policy would be underestimated. In addition, in view of data availability, this paper excluded a total of 14 prefecture-level cities with serious data missing and administrative division adjustments, and two county‑level cities (Changji and Shihezi), to avoid interference in the empirical results arising from differences in cities’ administrative levels.

The descriptive analysis results of the research variables and data in this paper are shown in Table 1 below.

Table 2 shows the regression results of the national innovative city PILOT POLICY on the urban–rural income gap in prefecture-level cities. Among them, M2, M3 and M4 sequentially add control variables, control city fixed effects, and simultaneously control city and year fixed effects on the basis of M1. It can be seen from Table 2 that under each model, the regression coefficient of the core explanatory variable Treat ∗ Post is significantly positive; the results of the two-way fixed effects model (M4) show that the impact coefficient of Treat ∗ Post on the urban–rural income gap is 0.01, passing Significance test at 10% level. This shows that the national innovative city PILOT POLICY has significantly exacerbated the urban–rural income gap, which preliminarily verifies the research hypothesis H1. Coincidentally, Li and Yan (2016) pointed out that under the combined effect of factors such as the urban–rural dual structure of scientific and technological innovation and the bias of technological progress caused by innovation activities, the improvement of innovation investment intensity, innovation capabilities and efficiency will be significantly expanded. Urban–rural income gap.

From M4, it can be seen that regarding the control variables: (1) The regression coefficient between the level of economic development and the urban–rural income gap is significantly negative, and its square term coefficient is significantly positive, indicating that the two have a “U-shaped” relationship. Lin and Chen (2013) further demonstrated that China’s urban–rural income disparity follows a U‑shaped pattern as economic development advances. (2) The regression coefficient of the degree of openness to the outside world is significantly negative at the 1% significance level, indicating that the expansion of the degree of openness to the outside world can narrow the urban–rural income gap. (3) The regression coefficient of the degree of government macro-control is also significantly negative, which indicates that the increase in the level of government intervention is conducive to alleviating the urban–rural income gap. The reason may be that government fiscal expenditures are more biased toward the rural sector, which increases the income level of rural residents, thus exerting a play a role in narrowing the urban–rural income gap (Jiao, 2022; Hong et al., 2014). (4) The Internet penetration rate is significant at the 1% level and the coefficient is negative, which means that increasing the Internet penetration rate can reduce the urban–rural income gap, indicating that China is in an opportunity period to use the Internet and other information technologies to continuously reduce the imbalance between urban and rural development (Cheng and Zhang, 2019). (5) The regression coefficient of urbanization is negative, but it is not significant in the model. A reasonable explanation is that controlling for the spatial spillover effect of urban–rural income distribution will make this narrowing effect insignificant (Li and Yan, 2016).

Since the premise of the difference-in-difference method (DID) estimation is that the experimental group and the control group have the same trend before the policy is implemented, this study tests whether the sample meets the parallel trend hypothesis in order to accurately identify the impact of the national innovative city PILOT POLICY on the urban–rural income gap. This paper uses the coefficients of the regression of the urban–rural income gap and the relative time value dummy variable to depict the trend changes of the experimental group and the control group during the sample period, and uses the event study method to draw the coefficient image, as shown in Figure 1. As mentioned above, and as previously mentioned, the relative time dummy variable represents the values for each city in the i years before, the year of, and the i years after being designated as a pilot city, while non-pilot cities have a value of 0 throughout. Among them, this paper removes the time dummy variables before and during the −4 period of some cities to avoid multicollinearity, and selects the policy −1, −2, and −3 periods as the reference years for the parallel trend test before the policy occurs.

It can be seen from Figure 1 that the coefficients of the relative time dummy variables before the policy occurred are not significant, which shows that before the policy occurred, there was no significant difference in the urban–rural income gap between the experimental group and the control group, that is, the national innovative city PILOT POLICY. The hypothesis of parallel trend test is met. Based on the background that the number of national innovative pilot cities reached the largest scale in 2010 (44 cities/districts), this article chooses 2010 as the starting point to analyze the dynamic effects before and after the pilot policy. The results show that: in the current period when the policy occurred in the first year, the impact of the innovative city pilot policy on the urban–rural income gap was not significant; after 3 years of the implementation of the pilot policy, the impact coefficient stabilized at a significance level of 1% year by year. This shows that the national innovative city PILOT POLICY has had a widening policy effect on the urban–rural income gap at the prefecture-level city level; but there is a certain lag, which is consistent with the reality that the effect of policy implementation will not be immediate. In other words, the development of innovative cities represents a protracted, long‑term endeavor. It takes a certain amount of time from being approved to join the national innovative pilot city to the emergence of the impact of pilot policies, which is specifically reflected in the demonstration and adjustment of industrial policies, high-tech industries Park planning and construction, etc.

In order to further verify the robustness of the benchmark regression conclusion, this paper conducts the following robustness test. It includes constructing a PSM-DID model, replacing the measurement indicators of the explained variables, excluding the impact of other policies, placebo tests, and solving possible heterogeneous treatment effects. The robustness test results are shown in Table 3.

The heterogeneous characteristics of the city itself and the actual situation of regional differences will make the policy effects different (Hu et al., 2020). In order to explore whether there are significant differences in the impact of the national innovative city PILOT POLICY on the urban–rural income gap for urban objects at different administrative levels and different regions, this study conducted heterogeneity analysis, and the results are shown in Table 4.

In Table 5, M10 and M11, respectively, show the results of the mechanism test with industrial structure upgrading and rationalization as mediating variables.

From M10 in Table 5 above, it can be seen that the impact coefficient of the pilot policy ( Treat ∗ Post ) on the upgrading of industrial structure is 0.1389, which is significantly positive at the 1% level. This means that the innovative city pilot policy will promote the upgrading of the industrial structure. Scholars Yang and Li (2023) also found that the implementation of the innovative city pilot policy will allow more resources to be used in industries with higher production efficiency. This will further enhance the upgrading of the industrial structure. In addition, the impact coefficient of industrial structure upgrading ( ais ) on the urban–rural income gap ( gap ) is 0.1140, passing the significance test at the 1% level, which means that the impact of industrial structure upgrading on the urban–rural income gap is significantly positive. In addition, the coefficient φ =0.0341 is significantly positive, consistent with the δ * θ sign. Therefore, the existence of some intermediary effects is confirmed, indicating that the national innovative city PILOT POLICY can widen the urban–rural income gap by improving the upgrading of the industrial structure, and the research hypothesis H2 is verified. After calculation, the partial intermediary effect exerted by the advanced industrial structure contributed 31.71% to the total effect.

Similarly, according to the M11 test results in Table 5, it can be seen that the regression coefficient of pilot policy ( Treat ∗ Post ) on industrial structure rationalization ( ris ) is −0.1161, which is significantly negative at the 1% level, indicating that the national innovative city PILOT POLICY is not conducive to industry Structural rationalization. This may be related to the failure to take into account the professional division of labor and cooperation between industries before the design and creation of the pilot policy, leading to misallocation of resources, convergence of industrial structures, low correlation and complementarity (Feng, 2017). The impact coefficient of industrial structure rationalization ( ris ) on the urban–rural income gap ( gap ) is −0.1502, and passed the significance test at the 1% level, indicating that the reduction in the degree of industrial structure rationalization is not conducive to alleviating urban–rural income inequality. In summary, both coefficients are significant, indicating that the mechanism test using industrial structure rationalization as the intermediary variable has passed. The contribution of part of the intermediary effect is 35.13%, and the research hypothesis H3 has been verified.

In top-level design, the goals of innovation-driven growth and balanced development must be integrated organically. Metrics such as urban–rural income levels, equity of income distribution, and common prosperity should be incorporated into the planning, oversight, and evaluation framework for building innovative cities. For example, in accordance with relevant directives, performance appraisals could include hard targets like the narrowing of urban–rural income gaps to ensure that the benefits of innovation reach all segments of society. At the same time, complementary support policies should be refined—using fiscal transfers, tax incentives, and increased investment in education and healthcare—to guide factors of production (technology, capital, and talent) toward rural areas, thereby enhancing the inclusive effects of innovation. Empirical studies indicate that pilot policies for innovative cities help advance common prosperity; accordingly, a dual emphasis on innovation and sharing is required, with a tiered implementation mechanism that combines central coordination and local execution to ensure that innovation and reductions in urban–rural disparity proceed in tandem.

Tailored implementation plans should be developed for regions and city types with different characteristics. The literature highlights that the common-prosperity effects of pilot innovative-city policies are particularly pronounced in the central and western regions and in prefecture-level cities, and that policy impacts vary by urban location and administrative level. Therefore, following the principle of “central coordination, local implementation,” the central government should establish an overall framework and allocate additional resources to the central and western regions and lower-tier cities. Local governments, in turn, should propose specific measures based on their development foundations and factor endowments, avoiding one-size-fits-all approaches. For instance, graded support funds could be established, and greater efforts made to attract talent, capital, and innovation services to small and medium-sized cities and the central and western regions. Such a differentiated, tiered execution mechanism will enable place-based policymaking and fully leverage the ability of innovative-city pilots to support balanced regional development.

Industrial upgrading and employment generation should be key levers for narrowing the urban–rural divide. On the one hand, high-tech industries and modern service clusters must be vigorously cultivated, while labor-intensive manufacturing should be encouraged to provide more job opportunities and wage income for surplus rural labor. For example, pilot cities could establish industrial parks and innovation incubation platforms that foster industry–university–research integration and urban–rural collaboration, promoting the spillover of knowledge, technology, and capital into rural areas to enhance productivity and wages. On the other hand, the linkage and complementarity between upstream and downstream segments of industrial chains, as well as between horizontal industries, should be reinforced. By optimizing factor allocation and fostering collaborative innovation, resources can flow rationally across industries, achieving coordinated industrial-structure development. Research shows that innovative-city policies influence common prosperity primarily through urbanization and industrial upgrading. Therefore, at the macro level, industrial policies and incentive mechanisms should be perfected to ensure a balanced portfolio of industries, so that the gains from industrial upgrading more effectively contribute to income growth for both urban and rural residents.