1. Introduction
China’s traditional economic growth model, reliant on high investment, high consumption, and high emissions, has become unsustainable, necessitating an accelerated transition to new drivers of economic growth and a shift toward green, high-quality development. In this context, General Secretary Xi Jinping introduced the concept of “new quality productive forces (NQPFs)” in 2023. During the 11th group study session of the Politburo of the CPC Central Committee, he underscored that “green development serves as the foundation of high-quality development, and NQPF is essentially green productivity”. The 2024 Government Work Report prioritized the advancement of NQPFs as the foremost among the ten major tasks for the year. In July, the Decision of the CPC Central Committee on Further Deepening Reform and Promoting Chinese-Style Modernization, adopted at the Third Plenary Session of the 20th CPC Central Committee, called for improving institutional mechanisms to foster NQPF development tailored to local conditions and facilitating the convergence of advanced production factors into NQPFs. Productive forces refer to humanity’s capacity to utilize and transform nature in the production process, comprising the following three essential components: laborers, means of labor, and objects of labor [1]. As the core driving forces of social progress, NQPFs signify an advanced and future-oriented form of productive forces designed to achieve breakthroughs in the “new” and “quality” dimensions [2]. They arise from revolutionary technological advancements, the innovative reallocation of production factors, and deep industrial transformation and upgrading [3]. Fundamentally, NQPFs encapsulate the optimized and advanced integration of laborers, means of labor, and objects of labor. They are marked by innovation, defined by a superior quality, and characterized by both green productivity and a modernized production capacity [4,5,6]. As the world’s second-largest economy, China’s pursuit of NQPF development holds significant global implications. It represents not only a necessary step in transitioning from high-speed economic growth to a stage of green, high-quality development, but also a vital strategy for stimulating new momentum in global economic growth. Furthermore, it constitutes a critical pathway for achieving global green and sustainable development. Thus, identifying the core driving forces behind NQPF development in China is of pressing importance.
Urbanization serves as a critical engine for driving productivity development and shares a profound connection with the cultivation of NQPFs. Nevertheless, the rapid global expansion of urbanization has resulted in significant environmental challenges, which constrain sustainable urban development [7,8]. Traditional urbanization models are marked by a high energy consumption, substantial investment, and elevated emissions. These models are predominantly driven by the rapid expansion of urban scale and the concentration of the population, with their objectives centered on maximizing economic benefits. However, such development trajectories tend to neglect essential aspects, including urban–rural integration, human well-being, and environmental preservation [9]. In China, with its large population and limited land resources, the economic, social, and ecological issues arising from traditional urbanization have grown increasingly acute [10], creating substantial obstacles to the advancement of NQPFs. In response to these challenges, China introduced the National New-Type Urbanization Plan (2014–2020) (hereafter referred to as “the Plan”) in 2014. The Plan provided a comprehensive blueprint for new-type urbanization (NTU) development, outlining the key pathways, major objectives, and strategic initiatives required to construct urban areas that are intensive, efficient, compact, green, and low-carbon [11,12]. To realize these objectives, China launched three batches of national-level NTU pilot programs in February 2015, November 2015, and December 2016.
It is important to recognize that China’s urbanization endeavors have not unfolded in isolation. Existing research underscores the strong correlation between urbanization and innovation. For instance, studies utilizing late 19th-century U.S. statistical data demonstrated that innovation activities were predominantly concentrated in urban areas [13,14]. Globally, new urbanization trends, such as “high-tech urbanization” [15], “creative and knowledge-based urbanization” [16], and “entrepreneurial urbanization” [17], are gradually emerging as defining features of NTU [18]. High-tech urbanization, epitomized by the “Silicon Valley model”, has proliferated worldwide, while cities in the third wave of urbanization are widely characterized by significant levels of innovation [18]. These international urbanization experiences provide invaluable lessons for China’s efforts to advance NQPFs. From a global perspective, China must actively draw upon the successful strategies of international urbanization while adapting and innovating them in line with its specific national circumstances. First, China should accelerate the development of green technologies and low-carbon industries, using policy incentives to drive technological innovation and optimize the allocation of production factors. Second, coordinated urban–rural development must be prioritized, with efforts to channel resources from over-concentrated megacities toward small- and medium-sized cities and rural areas, thereby fostering balanced regional development. These measures will enable China to transition from high-speed economic growth to green, high-quality development, while simultaneously contributing to global economic growth and sustainable development.
This raises the following critical questions: Does China’s implementation of the New-Type Urbanization Pilot Policy (NTUPP) influence NQPFs? What are the underlying mechanisms of this influence? Are there heterogeneous effects? Exploring these issues will not only advance the understanding of the relationship between urbanization and NQPFs, but also provide empirical and theoretical insights for policymakers. This research can assist the Chinese government in evaluating and refining the NTUPP, offering a robust foundation for decision making. Furthermore, it will provide valuable evidence and guidance for other nations aiming to leverage urbanization as a driver for economic green, high-quality development.
2. Literature Review
The body of literature closely related to this study can be classified into four key areas.
First, existing studies have extensively examined the connotation, significance, evaluation metrics, and developmental pathways of new quality productive forces (NQPFs) [2,3]. These works provide detailed explanations of the defining characteristics of NQPFs [19]. Additionally, several studies have constructed indicator systems for measuring NQPFs based on the three fundamental elements of laborers, objects of labor, and means of production [20], physical and pervasive factors [21], as well as the dimensions of technological productivity, green productivity, and digital productivity [22]. These studies further analyze the levels and spatiotemporal distribution characteristics of NQPFs [2,23].
Second, some research, through theoretical analyses and empirical methods, investigates the influence of specific factors on NQPFs within a single-factor framework. These factors include institutional openness [24,25], green finance and digital inclusive finance [26], national big data pilot zones [27], and new energy policies [28].
Third, the existing literature highlights the significant role of NQPFs in fostering high-quality and green development. For instance, studies have suggested that NQPFs enhance the level of high-quality agricultural development [29]. Other findings have demonstrated that NQPFs promote green development [30], thereby contributing to carbon emission reductions. Further research reveals that the coupling coordination degree between NQPFs and the carbon emission efficiency of the manufacturing sector is progressively shifting from the confrontation stage to a transitional coordination stage [31].
Furthermore, some studies have begun to examine the effects of NTU on the ecological environment. One line of research constructs NTU indicator systems and employs panel fixed-effect models to assess NTU’s environmental impact [32,33,34]. However, the presence of endogeneity within these models may introduce biases in the results [34]. To effectively address endogeneity concerns, the difference-in-differences (DID) model has emerged as a more robust empirical tool. By comparing the differences between treatment and control groups before and after policy implementation, the DID model eliminates the influences of unobservable individual heterogeneity and common time trends. The phased implementation of NTU and regional disparities provide a suitable setting for identifying “treatment” and “control” groups within the DID framework. Compared to conventional Ordinary Least Squares approaches, the DID model allows for a more precise estimation of NTU’s policy effects. As a result, another stream of research treats the NTUPP as a quasi-natural experiment and employs the DID model to evaluate NTU’s impact on the ecological environment. For example, a number of studies have demonstrated that the NTUPP significantly reduces urban carbon emissions [35,36] and alleviates county-level haze pollution [37].
In conclusion, this study considers the implementation of NTU as a quasi-natural experiment. Utilizing panel data from 283 Chinese cities spanning the years from 2009 to 2022, the study employs a DID model to evaluate both the impact and the underlying mechanisms of the NTUPP on NQPFs. The key contributions of this research are as follows:
(1) NQPFs fundamentally reflect the transition from traditional to new economic growth drivers. In contrast to existing studies that predominantly focus on the environmental impacts of the NTUPP, this research examines the economic effects of the NTUPP through the lens of transforming economic growth momentum. Additionally, it enriches the literature on the determinants of NQPFs by incorporating an urbanization development perspective. (2) While the DID model mitigates endogeneity to some extent, it does not entirely resolve the issue. To address this limitation, the study employs urban slope, undulation, and altitude as instrumental variables for the NTUPP, effectively addressing endogeneity concerns and enhancing the robustness and credibility of the model estimations. (3) The study elucidates the mechanisms through which the NTUPP influences NQPFs, focusing on talent agglomeration and the allocation of production factors. It further explores the heterogeneous impacts of the NTUPP across different levels of NQPF development and NTU characteristics. This analysis provides differentiated and pragmatic policy pathways for optimizing NTU’s role in driving NQPF development.
3. Theoretical Analysis
The effective agglomeration of talent can overcome spatial and temporal barriers, reduce knowledge acquisition costs, accelerate knowledge dissemination, and amplify knowledge spillover effects [38], thereby driving the development of NQPFs. In regions with a high concentration of talent, knowledge, experience, and technology are rapidly exchanged and shared among firms and institutions. On the one hand, talented individuals, with their advanced skills and capabilities, foster the accumulation of intellectual capital through agglomeration. This process accelerates knowledge dissemination and transfer, generating significant spillover effects that expedite the creation of new knowledge, technologies, and products. On the other hand, talent represents the core driver of regional technological innovation [39,40]. By integrating labor with other production factors, talent enhances labor productivity while producing extensive knowledge spillover effects [41]. Additionally, talent agglomeration generates strong social network effects, enabling the rapid identification of emerging technologies and future trends, facilitating timely innovation strategies, and ensuring the efficient coordination of internal and external resources. These dynamics accelerate technological problem solving and further stimulate innovation, ultimately promoting NQPFs.
The agglomeration of talent is influenced by several factors, including economic, innovation, living, ecological, and social environments. According to public economics theory, governments are responsible for providing public goods during urbanization processes. However, earlier performance evaluation systems overly emphasized “GDP-centric” outcomes, leading local governments to favor quick-return, low-risk infrastructure projects while neglecting investments in education, technology, and environmental protection. In contrast to traditional urbanization models, NTU prioritizes not only green, low-carbon development, but also the strategic deployment of technological, educational, and talent resources. This shift enhances economic agglomeration effects [42], thereby fostering talent agglomeration. Compared to general labor, innovative talent demonstrates a greater mobility and places a higher importance on quality of life [43]. Features such as convenient transportation, high-quality public infrastructure, and superior living standards are essential for attracting high-level talent. NTU improves regional living quality by upgrading public services and infrastructure, thereby attracting greater concentrations of highly skilled individuals. Moreover, the ecological environment has become a pivotal determinant of talent flows. Studies show that air pollution significantly increases the likelihood of talent migration; specifically, a 10% rise in PM2.5 concentrations increases migration intentions by 28% [44]. This phenomenon is particularly evident among highly educated groups, who exhibit a heightened sensitivity to environmental conditions. By prioritizing green, low-carbon development, NTU creates an environment conducive to talent agglomeration, thereby facilitating the development of NQPFs. Accordingly, this study proposes the following hypothesis:
NTU facilitates the realization of NQPFs through talent agglomeration.
NTU has been proven to offer significant advantages in enhancing the efficiency of resource allocation [45]. Specifically, NTU facilitates the integration of public service resources, such as education, healthcare, and social security [46], while optimizing mechanisms for the flow of resources between urban and rural areas. This promotes a fairer and more efficient exchange of production factors, including land, capital, population, and natural resources [47]. Such equitable exchange not only addresses imbalances in urban–rural development, but also fosters a broader and more balanced distribution of public resources, which is instrumental in advancing NQPFs. Traditionally, inefficient industries characterized by high pollution and emissions have dominated resource allocation, resulting in substantial resource waste and severe environmental degradation. In contrast, NTU proactively phases out industries that fail to meet sustainability requirements through targeted policies and initiatives [48]. It reallocates limited resources to cleaner, low-carbon, and highly efficient industries with greater growth potential [12], thereby providing crucial support for the realization of NQPFs. Moreover, NTU enhances the rationality and sustainability of resource allocation through scientifically guided spatial planning. In particular, NTU prioritizes the balanced distribution of land, capital, and population across urban clusters and regional economic zones, mitigating the risks of resource over-concentration and unregulated development. By promoting multi-centered urban cluster structures, NTU fosters coordinated regional development, which reduces resource wastage and alleviates environmental pressures. This refined spatial layout ensures the optimal allocation of resources, further contributing to the realization of NQPFs. Accordingly, this study proposes the following hypothesis:
NTU facilitates the realization of NQPFs through resource allocation.
4. Research Design
4.1. Model Setup
This study utilizes a multi-period DID model to evaluate the effects of the NTUPP on NQPFs. The specification of the multi-period DID model is as follows:
(1)
In this model, NQPFit denotes new quality productive forces, and NTUPPit refers to the New-Type Urbanization Pilot Policy, defined as NTUPPit = Treatedi × Timet. Here, Treatedi is a dummy variable indicating whether a city is designated as an NTU pilot city, while Timet is a time dummy variable marking the initiation of the NTUPP. Xit represents a series of control variables affecting NQPFit. captures individual fixed effects, accounts for time-fixed effects, and denotes the random error term.
4.2. Variables and Data
4.2.1. Variables Selection
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(1). Dependent variable
Drawing on Marxist classical theory, productive forces refer to humanity’s collective capacity to transform nature under specific historical conditions to satisfy societal needs. The advancement of productive forces is driven by the interaction of laborers, objects of labor, and means of labor. This development spans the entirety of human social history, embodying both its historical and social characteristics.
NQPFs emphasize the integrated development of “new” and “quality”, reflecting the evolving features, expanded connotations, and innovative pathways of productive forces within the context of the new era. Consequently, the fundamental components of NQPFs have been expanded and restructured into the following three elements: new–quality laborers, new–quality objects of labor, and new–quality means of labor.
Under this framework, NQPFs are further delineated into the three following core dimensions: technological productivity, digital intelligence productivity, and green productivity. These dimensions capture the developmental trajectory and essential requirements of modern productive forces. To provide a robust foundation for analyzing the determinants of NQPFs, this study employs the entropy method for objective measurement at the urban level. Table 1 outlines the indicator system for NQPFs.
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(2). Core explanatory variable
Our study aims to assess the impact of the NTUPP on NQPFs. To accomplish this, we construct a robust empirical analysis framework and adopt the lists of NTU pilot cities released by the Chinese government in the following three phases: February 2015, November 2015, and December 2016. To identify the causal effects of the policy, we introduce key dummy variables, structured along the two dimensions of time and region. The variables are defined as follows:
Time Dummy Variable (Time): This variable captures the time effect of policy implementation. For cities in the treatment group, Time takes a value of one in the year when the policy was approved and in all subsequent years, signaling the official activation of the policy. For the years prior to policy implementation, Time is set to zero. In cities assigned to the control group, which are unaffected by the policy, Time remains zero across all years.
Regional Dummy Variable (Treat): This variable indicates whether a city belongs to the treatment group. Cities included in the NTU pilot program are assigned a Treat value of one, while cities excluded from the pilot program (control group) are assigned a value of zero.
We then construct the key interaction term, NTUPP, defined as follows: NTUPP = Treat × Time. The coefficient of the interaction term serves as the primary indicator for evaluating the effect of the NTU policy. This coefficient quantifies the extent of NTU’s influence on NQPFs following policy implementation. By integrating the temporal and spatial dimensions, this design ensures the clear and precise causal identification of the policy’s impact.
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(3). Control variables
Openness Degree (OD): Proxied by the share of foreign investment in GDP.
Industrial Structure Upgrade (INDUSTR): Represented by the share of the tertiary sector in GDP.
Environmental Regulation (ER): Constructed using a composite index derived via the entropy method, which integrates sulfur dioxide removal rates, industrial smoke (dust) removal rates, and the comprehensive utilization rates of industrial solid waste.
Economic Development Level (AGDP): Measured by per capita GDP.
Financial Development Level (FINA): Represented by the ratio of total financial deposits and loans to GDP.
Manufacturing Agglomeration (AGG): Quantified using the location quotient index of manufacturing employment, calculated as follows:
where AGGit is the location quotient for manufacturing in city k at time t; denotes the number of manufacturing employees in city k; represents the total employment in city k; refers to the national number of manufacturing employees; and indicates the total national employment.Government Intervention (GOV): Measured by the share of fiscal expenditure (excluding fiscal allocations for education and science/technology) relative to the total fiscal expenditure.
Population Size (PS): Represented by the population per unit of administrative area.
Infrastructure (INFRA): Proxied by the per capita road area.
4.2.2. Data Source and Statistical Description
To ensure the rigor of our research design and the representativeness of the dataset, this study constructs a panel dataset comprising 283 Chinese cities spanning the years from 2009 to 2022. A systematic data screening and processing procedure is implemented, emphasizing data completeness and accuracy to enhance the reliability and scientific validity of our analytical results.
Data sources: The primary sources for the raw data include the following authoritative statistical publications and databases: China Urban Statistical Yearbook, China Urban Construction Statistical Yearbook, China Energy Statistical Yearbook, China Regional Economic Statistical Yearbook, and China Industrial Statistical Yearbook. Additionally, we utilize data from the CNRDS database, select prefecture-level city statistical yearbooks, and the EPS data platform. NTU pilot data are manually collected, organized, and consolidated from the official government websites of the People’s Republic of China.
Data screening and processing: Cities with substantial missing data are excluded during the data screening phase. For datasets with minor data gaps, appropriate imputation methods—such as linear interpolation and moving averages—are applied. The descriptive statistics for the key variables are provided in Table 2.
5. Empirical Results Analysis
5.1. Parallel Trend Test
In the framework of the DID model, the parallel trend assumption is a critical prerequisite for ensuring the validity of causal inference. This assumption requires that, prior to policy implementation, the NQPFs of the treatment group and the control group exhibit similar temporal trends. To verify this assumption, this study uses panel data spanning the 5 years preceding and the 7 years following the implementation of the NTUPP. A parallel trend test is conducted to assess the dynamic changes in NQPFs between pilot cities (treatment group) and non-pilot cities (control group), and a parallel trend graph is plotted within the 95% confidence interval (see Figure 1).
The results of the parallel trend test demonstrate that, prior to the policy’s implementation, the NQPF trends in the pilot cities and non-pilot cities were highly consistent, with no statistically significant differences observed between the two groups. This finding provides strong evidence that, before the NTUPP’s implementation, the treatment and control groups exhibited parallel trajectories in the evolution of NQPFs. Thus, the parallel trend assumption required for the DID model is satisfied, laying a robust foundation for the accurate identification of the policy’s causal effects.
A further examination of post-policy implementation results reveals that, following the NTUPP’s implementation—particularly over the 6 years after 2015—the NQPF levels in the pilot cities rose significantly, with an increasingly widening gap relative to the non-pilot cities. This outcome indicates that the NTUPP exerted a significant positive impact on improving the NQPFs in the pilot cities. Specifically, the regression coefficient of the interaction term is found to be significantly positive and passes statistical tests, confirming the policy’s positive effects. Moreover, the impact of the policy strengthens progressively over time, reaching its peak in 2021, which further attests to the long-term effectiveness and sustainability of the NTUPP.
5.2. Baseline Analysis
The baseline regression results (Table 3) demonstrate that the NTUPP exerts a significant and positive impact on NQPFs. This finding remains consistent regardless of whether control variables are included or individual and time-fixed effects are accounted for, with the regression coefficients of the NTUPP being consistently positive at the 1% significance level. For instance, in column (4), cities that implemented the NTUPP experienced an average increase in NQPFs of 1.1% compared to non-implementing cities.
These results provide robust evidence that the NTUPP effectively enhances NQPFs. Mechanistically, the NTUPP achieves this through multiple channels. First, the policy optimizes resource allocation by enhancing the efficiency of production factor utilization. It facilitates the efficient flow and rational redistribution of resources across urban and rural areas, within cities, and among urban clusters, thereby laying a solid foundation for NQPFs. Second, the NTUPP upgrades urban infrastructure networks, including transportation, communication, and energy systems, which reduces transaction and production costs for enterprises. This, in turn, enables a more efficient and optimized integration of capital, technology, and labor.
Furthermore, the NTUPP promotes sustainable development and green economic transformation by implementing policies to phase out industries with a high pollution and energy consumption. The reallocation of resources to clean energy, low-carbon industries, and high-technology sectors encourages the adoption and diffusion of green technologies and innovations, generating a lasting positive impact on NQPFs.
An additional examination of the control variables reveals that OD, INDUSTR, ER, FINA, AGG, and GOV have significant negative effects on NQPFs, while PS and INFRA exert significant positive effects. Interestingly, although the coefficient for AGDP does not reach statistical significance, its potential implications merit further investigation and deeper analysis.
5.3. Robustness Analysis
5.3.1. Placebo Test
To confirm the robustness of the NTUPP’s positive effect on NQPFs and to ensure that the results are not driven by unobserved variables or other confounding factors, this study conducts a placebo test for reliability analysis. The placebo test follows the following steps:
First, based on a random selection principle, a set of cities that did not participate in the NTUPP is designated as a “virtual treatment group”, with the assumption that these cities implemented NTU pilot programs. Second, time randomness is introduced by randomly assigning policy implementation years to these cities, thereby mitigating any biases arising from the time dimension. Next, using the virtual treatment group and randomly assigned policy implementation years, we re-estimate the baseline regression model. This process is repeated 1000 times, with the impact coefficient of the NTUPP recorded for each iteration.
The results of the 1000 simulations are then visualized using a kernel density plot (Figure 2). The plot clearly shows that the majority of the impact coefficients under the virtual setting are distributed around zero and follow a near-normal distribution. This indicates that, under randomly assigned treatment groups and policy implementation years, the estimated effects of the policy on NQPFs are largely insignificant. Moreover, the p-values for most of the simulated coefficients exceed the 10% threshold, further confirming the lack of statistical significance under the random conditions.
By contrast, the actual NTUPP impact coefficient from the baseline model remains significantly positive and deviates markedly from the mean of the randomly simulated coefficients. This substantial divergence substantiates the authenticity of the observed policy effect, effectively ruling out the influence of unobserved variables or other potential confounders. These findings reinforce the robustness and causal interpretability of the NTUPP’s positive impact on NQPFs.
5.3.2. Other Robustness Tests
(1) Two-stage DID estimation. The two-stage DID estimation method represents an enhancement of the traditional DID approach, designed to address potential biases that may arise in conventional two-way fixed effects models. The results from the two-stage DID estimation, as shown in column (1) of Table 4, confirm that the NTUPP continues to exert a significant positive effect on NQPFs. This finding indicates that the positive impact of the NTUPP remains robust even after accounting for the dynamic heterogeneity of treatment effects, further validating the reliability and effectiveness of the policy outcomes.
(2) Changing the Measurement Method of the Dependent Variable. This study utilizes the advanced search function of Baidu News to identify 46 keywords associated with “NQPF” and employs total word frequency to reassess the NQPF levels of cities. Column (2) in Table 4 presents the regression results of the model using this new measurement approach for the dependent variable. The analysis reveals that the NTUPP continues to have a significant positive impact on NQPFs, further validating the robustness of the policy effect. This finding indicates that the NTUPP’s beneficial influence on NQPFs remains significant and reliable, even when employing different measurement methods.
(3) Controlling for Other Pilot Policies. To further enhance the baseline regression model, this study incorporates additional pilot policy variables, including the Civilized City Pilot Policy (postwen), the Low-Carbon City Pilot Policy (postdt), the Smart City Pilot Policy (postzh), and the Innovative City Pilot Policy (postcx). In an extended regression model that controls for these variables, we reanalyze NQPFs. The results, presented in column (3) of Table 4 demonstrate that the impact coefficient of the NTUPP remains significantly positive, even after accounting for postwen, postdt, postzh, and postcx.
(4) Propensity Score Matching–DID Test. Propensity Score Matching (PSM) enhances the precision and reliability of policy effect estimation by creating a balanced control group, ensuring that the treatment and control groups exhibit a high degree of similarity across key characteristics. In this study, we implement the 1:1 nearest-neighbor matching method. Specifically, for each study year, cities that did not implement the NTUPP are selected as control group samples based on their closest observed characteristics to those in the treatment group.
By adopting this matching strategy, we successfully balance the distributions of the treatment and control groups across a range of potential confounding variables. This reduces biases stemming from sample selection and minimizes estimation errors, thereby improving the robustness of causal inference. After completing the matching process, we conduct a re-estimation using the matched samples. The results confirm that, even after systematically controlling for systematic differences, the NTUPP continues to exert a significantly positive effect on NQPFs.
(5) Excluding Special Samples. To further mitigate the influence of outlier samples and enhance the accuracy and generalizability of the findings, this study excludes municipalities directly under the central government and sub-provincial cities from the analysis, followed by a re-estimation of the baseline model. This refinement aims to focus on prefecture-level cities, which offer a more representative sample, while removing potential biases stemming from a higher administrative status and disproportionate resource allocation. This approach enables a clearer identification of the NTUPP’s true effect at the prefecture-level city scale. The regression results, after excluding municipalities and sub-provincial cities, confirm that the NTUPP significantly promotes the development of NQPFs.
5.4. Endogeneity Analysis
To enhance the precision and robustness of our model estimation and effectively address potential endogeneity concerns inherent in the traditional DID model, this study employs the instrumental variable (IV) approach within the DID framework. Following prior research [51], city slope is selected as the primary instrumental variable for the NTUPP, supplemented by additional natural geographical features, namely city undulation and elevation, to provide a more comprehensive solution to endogeneity.
City slope, as a natural geographical feature, directly affects a city’s economic development potential. Cities with steeper slopes face substantial challenges in infrastructure construction, industrial layout, and urban expansion, often leading to slower economic growth and a weaker developmental capacity. These geographical limitations reduce the likelihood of such cities being selected for NTU pilot programs, thereby establishing a strong correlation between city slope and the NTUPP, fulfilling the relevance condition of instrumental variables. In contrast, NQPFs reflect new economic growth drivers, which depend on factors such as effective policy implementation, technological innovation, and management practices. Importantly, there is no direct causal relationship between economic growth drivers and static natural geographical features like city slope, thereby satisfying the exogeneity condition of instrumental variables.
Given that city slope remains constant over time, we construct an interaction term between city slope and the time dummy variable to enable its reasonable application in panel data analysis. To further strengthen the robustness of the instrumental variable, city undulation and elevation are included to enhance comprehensiveness.
The validity of these instrumental variables is rigorously verified through statistical tests (see Table 5). The results show that the Kleibergen–Paap rk LM statistic rejects the null hypothesis of under-identification at the 1% significance level. Moreover, the Cragg–Donald Wald F statistic substantially exceeds the critical value of 11.490, effectively eliminating concerns regarding weak instruments. These outcomes confirm the validity and suitability of city slope, city undulation, and elevation as instrumental variables.
After incorporating these instrumental variables and addressing endogeneity, we re-estimate the regression model. The results demonstrate that the NTUPP continues to have a significant positive effect on the development of NQPFs.
5.5. Testing the Impact Mechanism
Building on model (1), we develop a panel mediation effect model. The specific formulation of the model is as follows:
(2)
(3)
In this context, M denotes the mediator variable, which represents a factor influencing the mechanism. The meanings of the other symbols are consistent with model (1). Given that information technology professionals typically possess high levels of education and substantial expertise in IT, the concentration of such individuals can partially capture the effect of information talent agglomeration. Referring to prior studies [52], this research measures talent agglomeration (ITG) by taking the logarithm of the workforce employed in information transmission, computer services, and software industries. Additionally, drawing on the prior literature [45], total factor productivity is used to measure resource element allocation (ARE). Table 6 presents the results of the mechanism analysis, demonstrating that the NTUPP significantly facilitates the achievement of NQPFs through both talent agglomeration and resource allocation mechanisms.
Firstly, a fundamental aspect of the NTUPP is attracting and concentrating highly skilled talent, thereby infusing new dynamism into economic and social development. By enhancing urban living conditions, employment opportunities, and social welfare policies, the NTUPP succeeds in attracting a diverse pool of talent, which, in turn, provides the intellectual capital essential for technological advancement and industrial transformation. Talent agglomeration not only boosts labor productivity, but also fosters a variety of innovative outcomes, thereby continually propelling NQPFs. For instance, high-caliber research teams and entrepreneurial individuals can drive breakthroughs in cutting-edge technologies, enhancing the city’s competitiveness and sustainable development prospects. Secondly, the NTUPP enhances resource utilization and optimizes allocation through the rational management of resources. This encompasses infrastructure improvements, such as the expansion of transportation networks and the enhancement of public service systems, as well as the efficient use of land and strategic adjustments in industrial layouts. Through the scientific allocation of resources, cities can create favorable conditions for the growth of high-end and emerging sectors, facilitating economic restructuring and advancing innovation capabilities. Efficient resource allocation reduces production costs, enhances overall economic performance, and establishes a robust foundation for the advancement of NQPFs. The findings of this study provide robust evidence supporting Hypotheses 1 and 2.
5.6. Analysis of Heterogeneity
5.6.1. NQPF Level
Table 7 illustrates the heterogeneity test results for NQPF levels. The data indicate that the promotive impact of the NTUPP on NQPFs progressively declines across the 15%, 35%, 55%, 75%, and 95% quantiles, with corresponding effects measured at 0.0182, 0.0175, 0.0166, 0.0157, and 0.0142, respectively.
This declining trend can primarily be attributed to the law of diminishing marginal returns. As the NQPF level rises, the NTUPP’s ability to promote NQPFs demonstrates diminishing marginal effects. At lower NQPF quantiles, the NTUPP contributes significantly to NQPFs by improving infrastructure, optimizing resource allocation, and fostering an enhanced innovation environment. Nevertheless, as the NQPF level advances, the marginal benefits from urban resource improvements and innovation capacity gradually wane, leading to a reduced marginal contribution from the NTUPP.
Additionally, this pattern is linked to the efficiency of resource allocation. At higher NQPF quantiles, resource distribution and innovation capabilities often reach a state of maturity and saturation, constraining the enhancement effect of the NTUPP on NQPFs. In other words, cities characterized by a high productivity may encounter resource bottlenecks and environmental constraints, thereby diminishing the NTUPP’s impact. Moreover, the advancement of NQPFs necessitates higher technological and resource support, imposing greater demands on the NTUPP as the NQPF level rises.
Lastly, the weakening effect of talent agglomeration also plays a role. At lower quantiles, the NTUPP effectively promotes NQPFs by attracting substantial talent and enhancing urban environments. However, at higher NQPF levels, attracting top-tier talent and driving innovation becomes increasingly difficult, reducing the marginal efficacy of the talent advantage. Consequently, the NTUPP’s promotive effect diminishes accordingly.
5.6.2. Characteristics of NTU
In its efforts to advance NTU, the Chinese government has prioritized low-carbon, smart, and civilized characteristics as central features, underscoring a profound recognition of their critical importance. This recognition arises not only from clear directives outlined in national policies, but also from the practical challenges and developmental needs faced by cities. In light of this, the present study examines how governmental emphasis on low-carbon, smart, and civilized characteristics moderates the relationship between the NTUPP and NQPFs, thereby revealing the mechanisms through which these core features influence policy outcomes.
More specifically, we analyze the moderating effects of low-carbon city initiatives, civilized city initiatives, and smart city initiatives on the NTUPP–NQPF relationship. The regression results, presented in columns (1), (2), and (3) of Table 8, indicate the following: the interaction term between the NTUPP and low-carbon city initiatives (postdt) is significantly positive, suggesting that governmental focus on low-carbon development amplifies the positive impact of the NTUPP on NQPFs. Likewise, the interaction term between the NTUPP and civilized city initiatives (postwen) is significantly positive, implying that efforts to foster a favorable social and cultural environment through civilized city construction significantly enhance the NTUPP’s positive effects on NQPFs. By contrast, the interaction term between the NTUPP and smart city initiatives (postzh) is significantly negative, indicating that, under certain conditions, the development of smart cities may hinder or attenuate the effectiveness of the NTUPP in promoting NQPFs. Low-carbon city development amplifies the NTUPP’s impact on NQPFs by promoting sustainable economic growth and efficient resource management. Policies fostering low-carbon urbanization drive efficiency in energy use, infrastructure, and industrial distribution. The adoption of green energy and stringent carbon emission controls reduces environmental pollution and enhances residents’ quality of life while stimulating innovation in green technologies. This self-reinforcing cycle improves urban livability and productivity, thereby magnifying the benefits of the NTUPP. Hence, a strong governmental emphasis on low-carbon principles significantly facilitates NQPF improvement.
Civilized city construction also enhances the NTUPP’s promotive effects by fostering a robust socio-cultural environment. Efforts in this area include advancing social governance, fostering community harmony, and elevating cultural and civic standards among residents. These initiatives create a stable and conducive environment for innovation and economic activities. Effective governance minimizes societal disruptions, attracting high-caliber talent and innovation resources to urban areas. This supportive social ecosystem not only increases citizen well-being, but also offers the optimal conditions for productivity enhancement, amplifying the NTUPP’s efficacy.
Conversely, smart city construction introduces challenges that can reduce the NTUPP’s effectiveness. The implementation of cutting-edge technologies such as the Internet of Things, big data, and artificial intelligence requires extensive investment and precise resource management. Inefficiencies or mismanagement can lead to resource wastage, undermining productivity gains. Additionally, the complex data infrastructure essential to smart city functionality often faces issues such as data fragmentation, privacy concerns, and cybersecurity threats, which can impede operational efficiency. The benefits of smart city technologies are typically realized over the long term, and the initial stages may encounter technological limitations or mismatches. Thus, the delayed realization of benefits and existing technological challenges may contribute to the NTUPP’s diminished impact. Only through long-term optimization and technological refinement can smart cities reach their full potential.
6. Discussion
6.1. Interpretation of Findings
This research investigates the effects of the NTUPP on NQPFs and the mechanisms driving these impacts, thereby contributing to the body of literature examining the relationship between urbanization and new economic growth drivers. Theoretically, this study presents a comprehensive and nuanced analytical framework to understand how NTU policies specifically foster new economic growth dynamics. While existing studies often address the aggregate effects of NTU policies on economic growth and environmental outcomes, few have explored the specific pathways—such as talent agglomeration and the efficient allocation of resources—through which NTU enhances NQPFs, critical indicators of modern productivity. By emphasizing innovation-driven strategies and resource optimization, this study elucidates the multifaceted benefits of the NTUPP, extending economic growth theories and providing a robust foundation for future investigations. Furthermore, through an analysis of NTU policy heterogeneity, particularly in terms of low-carbon, smart, and civilized urban features, this research deepens the understanding of the multi-tiered impacts of NTU policies and introduces new perspectives for examining the complexity of policy outcomes.
Practically, the findings of this study provide significant insights for policymakers aiming to optimize NTU policy frameworks. First, the evidence that the NTUPP markedly improves NQPFs underscores the importance of continuing NTU initiatives, with an emphasis on attracting high-caliber talent and optimizing resource distribution to facilitate economic transition and sustainable development. Additionally, the study highlights that low-carbon and civilized urban characteristics amplify policy effectiveness, suggesting that urban planning should prioritize environmentally sustainable and socially cohesive strategies to foster long-term economic resilience. Moreover, in addressing the potential drawbacks of smart city development, this research advocates for more strategic and deliberate planning to align technological advancements with urban needs, thereby avoiding inefficiencies and resource mismanagement. By offering detailed empirical support, this study equips governments and stakeholders with refined, targeted strategies to enhance the design and implementation of NTU policies, facilitating the full activation of new economic growth engines.
6.2. Policy Implications
To maximize the NTUPP’s promotion of NQPFs, the government must implement a series of systematic and integrated policy measures. First, as this study reveals that the green and low-carbon attributes of NTU significantly amplify the NTUPP’s positive impact on NQPFs, enhancing policy support for sustainable urban development is crucial. This could involve increased investment in renewable energy, green infrastructure, and energy-efficient technologies, alongside the enforcement of stricter environmental regulations to facilitate a green transition in urban energy use and resource management, thereby strengthening the NTUPP’s policy effectiveness. Additionally, the government should actively advance the development of civilized cities to improve social governance and public service quality. This requires greater investment in social welfare, public services, and cultural education to enhance urban governance and promote civic awareness, creating a harmonious and inclusive society. Such efforts would make cities more attractive and cohesive for high-quality talent, thus ensuring the sustainable advancement of NQPFs. Although smart city development may diminish the NTUPP’s impact on NQPFs in the short term, its long-term benefits remain significant. Therefore, policymakers should adopt scientifically rigorous and cautious planning strategies, with a focus on building robust smart infrastructure. Special attention should be given to data security, privacy protection, and the efficient management of resources. Establishing a unified data-sharing platform and a comprehensive urban information management system can ensure that smart technologies align with urban needs, thereby avoiding resource inefficiencies and facilitating long-term positive outcomes.
Furthermore, the NTUPP’s role in enhancing NQPFs through talent agglomeration necessitates intensified efforts to attract and nurture innovative talent. The government should provide competitive compensation, excellent working environments, and high-quality education and healthcare resources. Policies promoting collaboration between universities, research institutions, and urban industrial clusters should be enacted to spur technological innovation and industrial upgrading, thereby reinforcing and expanding the NTUPP’s beneficial impacts. Since the NTUPP’s effectiveness is closely tied to resource allocation efficiency, the government should accelerate industrial restructuring, steering traditional sectors toward high-value-added, technology-intensive activities. Optimizing industrial layouts and enhancing the allocation efficiency of resources such as land, capital, and technology are critical steps. Additionally, directing investments into innovation-driven and sustainable sectors will comprehensively strengthen new economic growth engines. Recognizing that the NTUPP’s impact is more substantial in regions with lower NQPF levels, the government should emphasize support for economically underdeveloped areas. Policy measures, including financial subsidies, tax incentives, and infrastructure development, should aim to promote balanced regional growth. Attracting high-tech enterprises and skilled talent to these regions would elevate NQPF levels and foster more equitable economic development. To ensure the effectiveness of the NTUPP, it is essential to establish a robust policy monitoring and evaluation system. Regular assessments of policy outcomes and resource allocation efficiency would allow for timely adjustments and refinements, enabling a flexible response to challenges in the urbanization process and ensuring the NTUPP’s sustained effectiveness and alignment with overarching policy goals.
6.3. Limitations and Future Research
While this study offers valuable insights into the mechanisms through which the NTUPP promotes NQPFs, it is not without limitations, and several areas warrant further investigation and refinement in future research. (1) The study’s reliance on empirical data from Chinese cities inherently constrains the international applicability of its findings. Given the substantial differences in urbanization policies and implementation contexts across countries and regions, future research should consider broadening the analytical scope to include an international dimension. Cross-national comparative studies could elucidate how NTU policies perform under diverse developmental and institutional settings, thereby testing the robustness and generalizability of the findings. Such an approach would not only strengthen the empirical foundation of the study, but also offer practical insights for global urbanization policy formulation and execution. (2) The analysis primarily examines the NTUPP’s effects on NQPFs through talent agglomeration and resource allocation, yet it does not delve into other potential mediating mechanisms. For example, the construction of innovation ecosystems, the accumulation of social capital, and industrial synergy effects may also significantly contribute to NQPFs. Future research should explore these additional dimensions to comprehensively map the pathways through which the NTUPP influences NQPFs. Expanding the analysis in this manner would deepen the theoretical understanding of the NTUPP’s impact and provide more nuanced guidance for scholars and policymakers. (3) This study strengthens the validity and robustness of its findings by employing rigorous tests, including placebo tests and PSM–DID. Nonetheless, the current presentation predominantly relies on textual descriptions and tables, with a limited utilization of visual tools. Future research could benefit from incorporating simplified graphical representations, digital visualizations, and other techniques to more intuitively convey key results and trends. Enhancing visual presentation would improve the accessibility and readability of the research, facilitating the broader dissemination and application of the findings among academics and policymakers alike.
7. Conclusions
Utilizing panel data from 283 Chinese cities spanning from 2009 to 2022, this study employs a DID model to empirically assess the impact of the NTUPP on NQPFs, arriving at the following key conclusions:
(1) The NTUPP has a significant positive effect on NQPFs, a finding that is robust to various tests of endogeneity and sensitivity analyses. Specifically, the implementation of the NTUPP is associated with an average 1.1% increase in NQPFs.
(2) The NTUPP facilitates NQPFs through mechanisms such as talent agglomeration and the strategic allocation of resources, with a more pronounced effect on cities with initially lower levels of NQPFs.
(3) Given that NQPFs embody green productivity, the green, low-carbon, and civilized attributes of NTU policies significantly amplify the NTUPP’s impact on NQPFs. In contrast, NTU’s smart attributes are found to diminish the policy’s effectiveness in promoting NQPFs.
Conceptualization, X.G. and N.X.; methodology, X.G. and X.Y.; validation, X.G. and N.X.; formal analysis, X.G. and X.Y.; investigation, X.G. and S.S.; resources, X.G.; writing—original draft preparation, X.G. and N.X.; visualization, X.G. and X.Y.; supervision, X.G. and S.S.; project administration, X.G. and N.X. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
Data will be made available on request.
The authors declare no conflicts of interest.
Footnotes
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NQPF indicator system.
| Dimension | Primary Indicators | Indicator Description | Data Source | Trend |
|---|---|---|---|---|
| New–Quality Labor Force | Technological Productivity | Total workforce of publicly listed companies in strategic and emerging industries, including future sectors | Compiled from annual reports of publicly listed firms | + |
| Digital Intelligence Productivity | Employment figures in the information transmission, computer services, and software sectors | China Urban Statistical Yearbook | + | |
| Green Productivity | Workforce in electricity, heat, gas, and water production and supply sectors | China Urban Statistical Yearbook | + | |
| Workforce in water conservancy, environmental management, and public facility sectors | China Urban Statistical Yearbook | + | ||
| New–Quality Labor Objects | Technological Productivity | Share of new material output value as a percentage of regional GDP | Compiled from annual reports of publicly listed firms | + |
| Number of companies specializing in new materials | Compiled from annual reports of publicly listed firms | + | ||
| Digital Intelligence Productivity | Number of broadband internet subscribers | China Urban Statistical Yearbook | + | |
| Total telecommunications business volume | China Urban Statistical Yearbook | + | ||
| Year-end mobile phone subscriber count | China Urban Statistical Yearbook | + | ||
| Number of artificial intelligence companies | Sourced from Tianyancha | + | ||
| Green Productivity | Investment in environmental pollution mitigation | China Urban Statistical Yearbook | + | |
| Carbon trading, energy use rights trading, and emission rights trading activities | Derived from municipal portal disclosures | + | ||
| New–Quality Labor Resources | Technological Productivity | Proportion of scientific research expenditure on local fiscal spending | China Urban Statistical Yearbook | + |
| Annual number of invention patent applications | National Patent Office | + | ||
| Annual number of utility model patent applications | National Patent Office | |||
| Green Productivity | Annual number of green invention patent applications | National Patent Office | + | |
| Annual number of green utility model patent applications | National Patent Office | + | ||
| Digital Intelligence Productivity | Data element utilization level | Following the approach in the existing literature [ | + | |
| Existence of a data exchange platform | Assigned a value of 1 if a city has data exchange, otherwise 0 | + | ||
| Robot installation density | Drawing on the existing literature [ | + |
Descriptive statistics for variables.
| Variable | N | Mean | SD | Min | Max |
|---|---|---|---|---|---|
| NQPF | 3948 | 0.050 | 0.065 | 0 | 0.636 |
| NTUPP | 4811 | 0.130 | 0.330 | 0 | 1 |
| OD | 4811 | 1.960 | 2.100 | 0 | 15.320 |
| INDUSTR | 4811 | 41.810 | 11.910 | 8.580 | 89.520 |
| ER | 4811 | 0.620 | 0.190 | 0.060 | 0.990 |
| AGDP | 4811 | 10.510 | 0.820 | 7.920 | 13.190 |
| FINA | 4811 | 2.430 | 1.350 | 0.560 | 8.980 |
| AGG | 4811 | 0.860 | 0.500 | 0.020 | 3.520 |
| GOV | 4811 | 0.810 | 0.050 | 0.610 | 0.980 |
| PS | 4811 | 5.740 | 0.920 | 1.610 | 7.940 |
| INFRA | 4811 | 4.890 | 6.370 | 0 | 75.040 |
Baseline regression.
| (1) | (2) | (3) | (4) | |
|---|---|---|---|---|
| NTUPP | 0.044 *** | 0.017 *** | 0.012 *** | 0.011 *** |
| (10.363) | (15.306) | (10.237) | (9.753) | |
| OD | −0.001 *** | −0.001 *** | ||
| (−3.809) | (−5.451) | |||
| INDUSTR | 0.0001 *** | −0.0001 *** | ||
| (8.071) | (−4.388) | |||
| ER | −0.002 | −0.009 *** | ||
| (−0.806) | (−3.481) | |||
| AGDP | 0.012 *** | 0.0001 | ||
| (13.209) | (0.086) | |||
| FINA | 0.002 *** | −0.002 *** | ||
| (4.701) | (−4.679) | |||
| AGG | −0.009 *** | −0.006 *** | ||
| (−6.233) | (−4.885) | |||
| GOV | −0.119 *** | −0.170 *** | ||
| (−12.077) | (−17.654) | |||
| PS | 0.148 *** | 0.110 *** | ||
| (20.075) | (15.302) | |||
| INFRA | 0.001 *** | 0.0001 *** | ||
| (7.485) | (3.920) | |||
| _cons | 0.044 *** | −0.843 *** | 0.048 *** | −0.421 *** |
| (38.322) | (−19.791) | (149.478) | (−9.279) | |
| Urban fixed effect | NO | YES | YES | YES |
| Year fixed effect | NO | NO | YES | YES |
| N | 3948 | 3948 | 3948 | 3948 |
| r2 | 0.046 | 0.955 | 0.952 | 0.961 |
| r2_a | 0.046 | 0.952 | 0.948 | 0.958 |
| F | 107.386 | 245.089 | 104.788 | 94.773 |
Note: The values in parenthesis are t-statistics. *** denotes significance levels of 1%.
Other robustness tests.
| (1) | (2) | (3) | (4) | (5) | |
|---|---|---|---|---|---|
| Two-Stage DID | Changing the Measurement Method of the Dependent Variable | Exclude Other Policy | PSM–DID | Excluding Special Samples | |
| NTUPP | 0.0130 *** | 0.096 *** | 0.0089 *** | 0.0107 *** | 0.0081 *** |
| (3.173) | (2.914) | (8.077) | (9.737) | (11.612) | |
| postwen | 0.0056 *** | ||||
| (5.073) | |||||
| postdt | 0.0084 *** | ||||
| (7.181) | |||||
| postcx | 0.0079 *** | ||||
| (5.099) | |||||
| postzh | −0.0030 *** | ||||
| (−2.771) | |||||
| controls | YES | YES | YES | YES | YES |
| _cons | - | 0.520 | −0.4113 *** | −0.4324 *** | 0.0086 |
| - | (0.428) | (−9.170) | (−9.441) | (0.284) | |
| N | 3948 | 4811 | 3948 | 3925 | 3458 |
| r2 | - | 0.917 | 0.9622 | 0.9608 | 0.9538 |
| r2_a | - | 0.911 | 0.9590 | 0.9575 | 0.9499 |
| F | - | 11.310 | 78.4844 | 96.5071 | 54.7572 |
Note: the values in parenthesis are t-statistics. *** denotes significance levels of 1%. City-fixed effects and year-fixed effects have been controlled.
Endogeneity test results.
| (1) | (2) | (3) | |
|---|---|---|---|
| Slope | Relief | Elevation | |
| NTUPP | 0.0351 *** | 0.0372 *** | 0.0484 *** |
| (3.611) | (4.420) | (4.898) | |
| Controls | YES | YES | YES |
| N | 3102 | 3102 | 3102 |
| r2 | 0.3845 | 0.3635 | 0.2252 |
| r2_a | 0.3181 | 0.2949 | 0.1416 |
| F | 23.6778 | 23.6289 | 23.0505 |
| Kleibergen–Paap rk LM statistic | 248.888 *** | 347.530 *** | 278.927 *** |
| [0.000] | [0.000] | [0.000] | |
| Cragg–Donald Wald F statistic | 25.753 | 36.605 | 29.017 |
| {11.49} | {11.49} | {11.49} |
Note: [ ] indicates p-values, and { } indicates the critical values for the Stock–Yogo weak identification test at the 10% significance level. The values in parenthesis are t-statistics. *** denotes significance levels of 1%. City-fixed effects and year-fixed effects have been controlled.
Mechanism of action test.
| (1) | (2) | (3) | (4) | |
|---|---|---|---|---|
| ITG | ARE | |||
| NTUPP | 0.1757 *** | 0.0077 *** | 0.0316 *** | 0.0106 *** |
| (4.287) | (8.372) | (2.948) | (9.723) | |
| ITG | 0.0080 *** | |||
| (20.536) | ||||
| ARE | 0.0036 ** | |||
| (2.079) | ||||
| controls | YES | YES | YES | YES |
| _cons | −7.1432 *** | −0.2218 *** | 1.2084 *** | −0.4084 *** |
| (−4.139) | (−4.840) | (3.233) | (−8.928) | |
| N | 3962 | 3102 | 4811 | 3948 |
| r2 | 0.6670 | 0.9756 | 0.2081 | 0.9609 |
| r2_a | 0.6392 | 0.9730 | 0.1541 | 0.9577 |
| F | 27.5873 | 104.0717 | 11.9143 | 86.6288 |
Note: The values in parenthesis are t-statistics. *** and ** denote significance levels of 1% and 5%, respectively. City-fixed effects and year-fixed effects have been controlled.
Results of the heterogeneity test for NQPF levels.
| (1) | (2) | (3) | (4) | (5) | |
|---|---|---|---|---|---|
| 15% | 35% | 55% | 75% | 95% | |
| NTUPP | 0.0182 *** | 0.0175 *** | 0.0166 ** | 0.0157 | 0.0142 |
| (6.759) | (5.945) | (2.423) | (1.422) | (0.786) | |
| Controls | YES | YES | YES | YES | YES |
| N | 3948 | 3948 | 3948 | 3948 | 3948 |
Note: The values in parenthesis are z-statistics. *** and ** denote significance levels of 1% and 5%, respectively. City-fixed effects and year-fixed effects have been controlled.
Results of the heterogeneity test for NTU characteristics.
| (1) | (2) | (3) | |
|---|---|---|---|
| NTUPP | 0.0040 *** | 0.0155 *** | 0.0016 |
| (4.152) | (9.157) | (1.613) | |
| postdt | 0.0501 *** | ||
| (6.555) | |||
| NTUPP ×postdt | 0.0143 *** | ||
| (6.234) | |||
| postzh | 0.0008 | ||
| (0.752) | |||
| NTUPP ×postzh | −0.0112 *** | ||
| (−5.438) | |||
| postwen | 0.0012 | ||
| (0.780) | |||
| NTUPP ×postwen | 0.0193 *** | ||
| (7.874) | |||
| Controls | YES | YES | YES |
| _cons | 0.3606 | 0.3818 | 0.3465 |
| (1.071) | (1.136) | (1.019) | |
| N | 4811 | 4811 | 4811 |
| r2 | 0.6346 | 0.6296 | 0.6322 |
| r2_a | 0.6095 | 0.6042 | 0.6070 |
| F | 31.6781 | 23.7927 | 26.5985 |
Note: The values in parenthesis are t-statistics. *** denotes significance levels of 1%. City-fixed effects and year-fixed effects have been controlled.
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; Song, Sheng 1 ; Xu, Ning 3 1 School of Marxism, North China University of Water Resources and Electric Power, Zhengzhou 450046, China;
2 School of Economics, Zhejiang University of Finance & Economics, Hangzhou 310018, China
3 School of Political Science and Public Administration, Henan Normal University, Xinxiang 453007, China;