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\title{Innovation-driven local government competition and new quality productive forces: Evidence from urban China}
%\footnote{We thank our research assistant Tong Li(Tsinghua) for her diligent work.  \\
%Zhu is at International Business School, 
%Shaanxi Normal University. Email: \url{zyl020414@163.com}.
%Zhang is at the Institute of Economics, Tsinghua University. Email: \url{xiangguozhang@mail.tsinghua.edu.cn}. 
%Jiang is at School of Economics and Management, Tsinghua University. Email: \url{jrn23@mails.tsinghua.edu.cn}.


%\author{Yaling Zhu \and Xiangguo Zhang \and Ruining Jiang }
%\version{0.01}
\date{}

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\begin{abstract}
This study investigates how innovation-driven competition among local governments in China shapes the development of New Quality Productive Forces (NQPF). Leveraging panel data from 281 prefecture-level cities over the period 2009–2022, our key findings reveal that innovation-driven local government competition significantly promotes NQPF development. This effect operates primarily through enhanced factor mobility and technological innovation, rather than industrial upgrading, and exhibits positive spatial spillovers to neighboring regions. Further, the spatial distribution of NQPF and government competition intensity demonstrates marked heterogeneity, with high-value clusters in eastern coastal provinces and decaying core-periphery structures. These insights underscore the need for policy interventions to optimize competitive strategies, enhance technological innovation incentives, and build collaborative regional platforms.

\textbf{Keywords:} Local government competition; New quality productive forces(NQPF); Technological innovation; Industrial upgrading \\

\textbf{JEL classification:} H11, O43, R12, R58	

\end{abstract}

\newpage

\section{Introduction}

Accelerating global technological innovation is fundamentally reshaping competitive dynamics, positioning new quality productive forces(NQPF) as pivotal for national competitive advantage, which are characterized by enhancing total factoe productivity and driven by disruptive technological breakthroughs \citep{Acemoglu2019}. Within China's unique system of fiscal decentralization and political promotion incentives, local governments engage in competition through policy supply, resource allocation, and institutional innovation to foster economic growth, profoundly shaping regional innovation ecosystems \citep{xu2011fundamental}. However, traditional GDP-driven competition often leads to fragmented innovation resources, industrial isomorphism, and redundant investments, failing to meet the intrinsic demands of NQPF for breakthroughs in fundamental research, synergistic factor allocation, and improved industrial chain resilience\citep{liu2024}.

In recent years, empirical evidence indicates a strategic transformation in local government competition from a ``GDP-driven" to an ``innovation-driven" approach. On one hand, competition in fiscal science and technology expenditure leverages the signal effect of innovation subsidies to stimulate corporate research and development investment \citep{agihon2015industrial}, though excessive subsidies risk distorting market selection mechanisms. On the other hand, cultivating strategic emerging industrial clusters, particularly in digital infrastructure and green technology, is redefining local competition \citep{Hausmann2007,Liyinge2026}. This transition requires establishing a dynamic equilibrium between efficient markets and proactive government, using institutional openness to dismantle local protectionism barriers to innovation factor mobility and strengthening intellectual property protection and innovation risk sharing \citep{akcigit2016}.

Further, innovation-driven local government competition directly shapes NQPF. Some scholars explore pathways through digital government construction, arguing that it fosters NQPF via technological innovation, policy support, developmental leadership, and innovation guarantees\citep{WU2025}. %some scholars参考文献
The optimization of governance constitutes a key outcome of local government competition. Consequently, the impact of digital governance—which synthesizes administrative frameworks with technological infrastructures—on NQPF partially reflects the evolving strategies of such competition within the digital intelligence era. Furthermore,  some studies consensus emphasizes that establishing a mature domestic unified market and a robust modern industrial system are important prerequisites for cultivating NQPF \citep{LIU2024103373}
Factor market integration, talent cultivation frameworks, technological innovation, and industrial structure optimization constitute actionable pathways toward this end. Crucially, institutional arrangements, factor endowments, technological advancement, and infrastructural development serve as both the constitutive elements targeted by, and the consequential outcomes generated through, innovation-driven competition among local governments.
%这一段需要修改提升下，审稿人reviewer2第1条。我们补充了参考文献，关于NQPF的选题。


\hyperref[Fig1]{Figure 1}  shows the significant growth over the decade from 2009 to 2022. Direct empirical investigation into the nexus between innovation-driven local government competition and the development of NQPF remains conspicuously absent in the extant literature. Predominantly, studies focus on the formation of NQPF indirectly, mediated by competition-induced green innovation\citep{ZHANG2025104315}, resilience of industrial chain\citep{CUI2025104245}, synergistic industrial agglomeration of manufacturing industry\citep{LIU2024103373}. This fragmented approach lacks a cohesive analytical framework and crucially overlooks the fundamental mechanisms underpinning this relationship. Furthermore, current research relies heavily on qualitative approaches, such as theoretical discourse and case analyses, resulting in conclusions that often lack empirically robust foundations; this limitation constrains the practical applicability of the findings.
%将这部分（fig1的notes）进行整理写作，在intro中增加一段，作为分析government competition和 NQPF的协同增长关系。
%During the first six years, government competition intensity (avg. growth 2.7\%) consistently exceeded NQPF development (1.1\%), widening the gap from 0.06 to 0.10. This reflects the limited efficacy of institutional competition in stimulating innovation under factor-driven growth paradigms. The 2015–2019 witnessed accelerated NQPF growth (4.3\% avg.) alongside moderated competition (1.8\%, index 0.16→0.17), narrowing the differential to 0.11. This marked the incipient transformation from competition dividends to innovation dividends, evidenced by NQPF's trajectory slope surpassing competition post-2017. In the Synergistic Intensification Phase (2020–2022), NQPF surged (8.4\% avg. growth to 0.069) with steady competition growth (3.5\% to 0.195), while the elasticity coefficient (\textbackslash{}varepsilon\_{N|G\}) rose from 0.41 to 2.40. This confirms the strategic pivot from policy tournaments to innovation ecosystem cultivation.


%Despite substantial literature on the relationship between innovation-driven local government competition and new quality productive forces, significant lacunae remain. First, research analyzing new quality productive forces specifically through the lens of government competition is scarce. The predominant focus on digital government limits understanding of the spatial dynamics of new quality productive forces development and hampers targeted policy formulation. Second, \textcolor{red}{literature directly examining the link between innovation-driven local government competition and new quality productive forces is virtually non-existent. Most studies approach new quality productive forces formation indirectly through competition-induced institutional changes, factor flows, or technological innovation, lacking a unified analytical framework and especially neglecting the underlying mechanisms. Current studies predominantly rely on theoretical discussions and case analyses, resulting in conclusions often lacking robust empirical support and limiting their practical applicability.}
%以下是针对红色文字优化后的段落，补充相应参考文献。


Addressing these gaps, this study utilizes panel data from 281 prefecture-level cities in China (2009-2022) to examine the development of NQPF under innovation-driven local government competition, analyzing its impact, mechanisms, and spatial effects. Our research offers three marginal contributions: (1) We construct an analytical framework linking innovation-driven competition to NQPF, investigating direct, indirect, and spatial impacts, thereby deepening understanding of post-orientation-shift government competition effects; (2) We identify the pathways in the three application scenarios of factor mobility, technological innovation, and industrial upgrading, through which innovation-driven competition drives NQPF; (3) Employing prefecture-level data and constructing core variables, we provide empirical evidence that innovation-driven competition promotes NQPF primarily via factor mobility, especially data and capital flows and technological innovation, with significant positive spatial spillovers evident in these pathways.

The paper proceeds as follows: 
\hyperref[section2]{Section 2} is research hypothesis; 
\hyperref[section3]{Section 3} details the research design; \hyperref[section4]{Section 4} presents empirical results; 
\hyperref[section5]{Section 5} concludes. 

\section{Research hypothesis}\label{section2}

\subsection{The direct impact of innovation-driven local government competition on the NQPF}

%\textcolor{red}{Amidst central innovation strategies, local governments increasingly prioritize innovation-driven competition. They foster New Quality Productive Forces (NQPF) through digital governance enhancements and competitive innovation policies \citep{YU2025101474}. Digital governments provide efficient administration and infrastructure, establishing institutional and technological foundations for NQPF \citep{yang2024digital}, while optimizing services accelerates innovation. Concurrently, innovation-driven competition incentivizes regions to improve innovation ecosystems, attracting resources and investing in key technologies \citep{su17115165}. Despite potential intervention risks, fiscal expenditure increasingly targets foundational and emerging research, correcting market failures to advance NQPF.}
%In response to central government innovation mandates, local governments are increasingly prioritizing innovation-driven competition. This strategic shift manifests in efforts to cultivate New Quality Productive Forces (NQPF) through enhanced digital governance and competitive innovation policies \citep{YU2025101474}. Digital governance platforms provide efficient administrative services and critical infrastructure, thereby establishing essential institutional and technological foundations for NQPF development\citep{YifanChen2022}; concurrently, service optimization accelerates the innovation process. Furthermore, innovation-driven competition incentivizes regions to upgrade their innovation ecosystems, mobilizing talent and capital while directing investment towards pivotal technologies. \citep{RePEc:taf:flgsxx:v:51:y:2025:i:1:p:156-177} Notwithstanding potential concerns regarding governmental intervention, fiscal expenditures are progressively focused on foundational and emerging research, functioning to rectify market inefficiencies and thereby advance NQPF.

In response to central innovation mandates, local governments are increasingly engaging in innovation-driven competition. This shift promotes NQPF through digital governance and proactive innovation policies \citep{YU2025101474}. Digital platforms improve administrative services and provide crucial infrastructure, supporting institutional and technological foundations for NQPF\citep{YifanChen2022}. Simultaneously, service optimization accelerates innovation. Innovation competition also encourages regions to upgrade their innovation ecosystems, attracting talent and capital toward key technologies\citep{RePEc:taf:flgsxx:v:51:y:2025:i:1:p:156-177}. Although government intervention raises occasional concerns, fiscal spending is increasingly directed toward basic and cutting-edge research to address market failures and advance NQPF.


\begin{hypothesis}\label{Hypothesis1}
  Innovation-driven intergovernmental competition helps promote the development of NQPF. 
\end{hypothesis}


\subsection{The mechanism of innovation-driven local government competition on the development of NQPF}

Political-economic incentives drive dual fiscal-innovation competition. This reshapes public expenditure and stimulates factor mobility, data, capital, and labor, fundamentally enabling NQPF through three dimensions.

\subsubsection{Factor mobility path}

Innovation-driven local government competition activates NQPF through three factor pathways: (1) Data elements, functioning as dual foundations for digital governance and NQPF inputs, leverage dependence-multiplier effects enhancing traditional factors and intensive-substitution properties optimizing allocation to reconfigure production systems during spatial agglomeration, with digital technologies enabling asset-to-value transitions \citep{Chen03062022}; (2) Capital flows, steered by fiscal bias, tax incentives, and FDI, stimulate innovation and industrial upgrading \citep{CAI2025105584} ; Although profit-seeking may cause misallocation, infrastructure investments and innovation funds help mitigate such risks \citep{liu2024}; (3) Labour mobility, optimized through digital databases \citep{ZHANG2024107834}, enhances NQPF via knowledge accumulation, industry-academia synergy, and skills training \citep{CAI2025105584}, though excessive agglomeration risks diminishing returns and labor-capital conflicts \citep{LI2025102009}.

\begin{hypothesis}\label{Hypothesis2}
Innovation-driven local government competition promotes NQPF by enhancing factor mobility (Data, Capital, Labour).
\end{hypothesis}

\subsubsection{Technological innovation path}

Innovation-driven local governments deploy policy bias, digital upgrades, and targeted subsidies to optimize regional innovation ecosystems, positioning technological innovation as the core NQPF engine through a synergistic trinity of effects: (1) Emergence Effect: digital government infrastructure enables systemic productivity breakthroughs \citep{CAI2025105584}; (2) Matching Effect: intelligent platforms optimize factor allocation and industrial layouts \citep{LI2025102009}; (3) Integration Effect: cross-sector synergy networks sustain innovation via lateral technology flows. Collectively, these reconfigure production modes and propel qualitative productivity leaps.


\begin{hypothesis}\label{hypothesis3}
Innovation-driven competition among local governments serves as a catalyst for enterprise technological innovation, thereby accelerating the growth of NQPF. 
\end{hypothesis}


\subsubsection{Industrial upgrading  path}

Under the innovation-driven paradigm, local governments drive industrial upgrading through optimized policy systems, enhanced market regulation, and facilitated factor mobility \citep{HAN2021112286}, constructing high-efficiency modern industrial frameworks. This upgrading empowers NQPF via agglomeration effects and  linkage effects. Strategic emerging industrial clusters accelerate knowledge spillovers and technological synergies \citep{https://doi.org/10.1111/puar.13752}, catalyzing digitalized, green, and integrated industrial forms while incubating new quality factors. Meanwhile, Institutional pressures promote cross-sector collaborative innovation, enabling vertical integration and horizontal fusion of industrial chains to achieve transformative advancements \citep{ZHANG2024107834, Jia2026}.
%These effects jointly establish a transmission mechanism, from policy-driven upgrading to factor recombination to systemic productivity leap,completing NQPF's evolution from elemental accumulation to system-level transformation.这一句要不要？考虑


\begin{hypothesis}\label{hypothesis4}
Innovation-driven local government competition promotes industrial upgrading, thereby accelerating NQPF development.
\end{hypothesis}


\subsection{The spatial effects of innovation-driven local government competition on the development of NQPF}

From the perspective of local government competition, intergovernmental rivalry functions as a strategic interaction  with significant spatial spillovers  effects \citep{CAI2025105584, HONG2020123455}. This operates through dual pathways: Enhanced digital governance and public services generate competitive pressure, prompting neighbouring regions to optimize institutions and infrastructure, thereby synchronizing regional development of NQPF , forming direct spatial spillover effects. Beyond direct competition effects, governmental strategic interactions trigger spatial spillovers through three pathways: (1) Factor Mobility: reshaping regional production scales and efficiency; (2) Technology Diffusion: facilitated through knowledge spillovers, clustering, and cross-regional linkages; (3) Industrial Synergies: Optimizing  resource allocation through agglomeration and chain integration \citep{HAN2021112286}.These mechanisms collectively form a multidimensional spatial spillover network, providing trans-regional synergy for NQPF development.



\begin{hypothesis}
Innovation-driven local government competition generates positive spatial spillovers on NQPF development.
\end{hypothesis}
%Competition-induced investments in digital infrastructure exhibit non-rival characteristics, generating cross-regional spillovers that support adjacent NQPF growth.

The mechanism of the nexus of the innovation-driven local government competition and NQPF is summarized in \hyperref[figure2]{Figure 2}.







\section{Research design}
\label{section3}

\subsection{Variables and data selection}
(1) Dependent variable: New Quality Productivity Forces($NQPF$). %Based on the theory of factors of productivity and considering the role and value of labour objects in the production process,
Referring \citet{Ren2024}'s method, we construct a system of indicators for evaluating NQPF from three aspects: labour force\citep{Liu05032025}, labour objects\citep{Nie20092022, article} and labour materials \citep{LIU2024103373, RePEc:eee:ecanpo:v:85:y:2025:i:c:p:1409-1427}, and measure NQPF using the entropy value method.
\hyperref[tab:nqpf_system]{Table 1} presents the indicators utilized for measuring new quality productive forces in this study.


(2) Independent variable: Local Government Competition ($Gov$) is a comprehensive index system  constructed from five dimensions: expenditure, tax, foreign investment, innovation, and promotion(\hyperref[tab:competitionindexsystem]{Table 2} ). Drawing on the Global Principal Component Analysis (GPCA) method proposed by \citet{bonzo2002clustering}, we calculate the competition index for each prefecture-level city. A higher index value indicates a greater intensity of innovation-driven local government competition. 

(3) Mechanism variables: Data factor mobility ($Da$) is measured by a gravity model\citep{Kaiser2015data}, incorporating regional variables such as the average profit of industrial enterprises above designated size, total telecommunication service volume, number of international internet users, and year-end mobile phone subscribers.
Capital factor mobility($Ka$) is estimated via the reverse “material-to-capital” flow method\citep{Kalemli-Özcan2020labor}, represented as the ratio of interregional capital flow volume to GDP. 
Labor mobility ($La$) is measured as the annual change in employed population within prefectures, based on data from the China City Statistical Yearbook. This value is normalized by the city’s total population to ensure cross-regional comparability. 
Technological innovation ($Tia$) is proxied by the number of invention and utility model patents granted at the prefecture level \citep{Nie2024tia}. A logarithmic transformation is applied to improve normality and mitigate heteroscedasticity.
%. Some scholars measure technological innovation using the number of granted invention patents, utility model patents, and design patents; others assess it based on innovation input, typically through research and development (R\&D) expenditures. However, due to data limitations—China Science and Technology Statistical Yearbook only provides R\&D investment data at the provincial and municipal levels, and the China City Statistical Yearbook includes R\&D data for prefecture-level cities only from 2018 onwards—this study \\
Industrial upgrading ($Inu$) is measured by the ratio of value-added in the tertiary industry to that in the secondary industry for each city\citep{Liu2024ind}.

(4)Control variables: Drawing on \citet{ https://doi.org/10.1111/psj.12226} and \citet{WU2025}, and considering data availability, the following control variables are selected:
Proportion of the Secondary Industry ($Ind2$): Share of secondary industry value-added in GDP; Proportion of the Tertiary Industry ($Ind3$): Share of tertiary industry value-added in GDP; Urbanization Rate ($Urb$):  Proportion of urban permanent residents relative to the total population; Fixed Asset Investment Ratio ($Inv$):Fixed asset investment as a percentage of GDP; Number of Regular Institutions of Higher Education ($Uct$):  the number of regular higher education institutions; Proportion of Science Expenditure ($Ste$): Science and technology spending as a share of total local fiscal expenditure. Descriptive statistics for all variables are presented in \hyperref[table:3]{Table 3}.

To test for severe multicollinearity in the model, \hyperref[table:4]{Table 4} reports the Pearson correlation matrix of core variables and control variables. The results show that the correlation coefficient between the core explanatory variable 
$Gov$ and the dependent variable $NQPF$ is 0.2977, indicating a significantly positive correlation, which preliminarily aligns with the direction of the research hypothesis. The absolute values of correlation coefficients among most control variables are less than 0.5.

From the perspective of spatial distribution in \hyperref[Fig3]{Figure 3}  (using 2022 as a representative case), pronounced regional disparities exist between eastern and western provinces.The distribution of NQFP exhibits a core \textbf{``east-high, west-low"} pattern , closely aligned with the \textit{Hu Huanyong Line} (Heihe-Tengchong Line). Eastern coastal provinces form high-value clusters (\textgreater0.096 in Jiangsu, Guangdong, Zhejiang; 0.052-0.096 in Shanghai and Shandong). Central provinces such as Henan and Hunan)represent a transitional zone with median values (0.036-0.052), while western provinces (e.g., Qinghai and Gansu)  fall within the lowest quantile (\textless0.036). Notably, the southeastern coastal belt south of the \textit{Qinling-Huaihe Line} demonstrates intensified innovation momentum, superimposing a north–south gradient over the predominant east–west divergence.
According to \hyperref[Fig4]{Figure 4}, high-competition polarization zones (indices $\geq$0.12) concentrate primarily in the Yangtze River Delta (Shanghai-Jiangsu-Zhejiang) and Pearl River Delta (Guangdong), with peak values of 0.12–0.20 observed in Jiangsu, Shanghai, and northern Zhejiang. Secondary competition zones (0.08–0.12) encompass the Bohai Rim (Shandong-Hebei), the West Coast Economic Zone (Fujian), and central inland hubs (Hubei-Chongqing), where Chongqing emerges as the sole high-value inland node. Low-competition depressions ($<$0.08) dominate the northwest (Qinghai-Gansu-Ningxia), southwest (Yunnan-Guizhou), and northeast (Heilongjiang-Jilin), demonstrating pronounced fracture along the Hu Huanyong Line—approximately 83\% of the national competition intensity concentrates to its southeast (based on color-grade area-weighted estimates).

%这部分写一下描述，或者将figure3&4的描述放在这里。

\subsection{Empirical model setting}
According to the previous analysis, we construct the econometric model as follows:
\begin{equation}
    NQPF_{it}=\alpha+\beta Gov_{it}+\gamma X+\mu_{i}+\nu_{t}+\epsilon_{it}
    \label{eq:eq1}
\end{equation}
Here $i$ and $t$ denote the prefecture-level city and year, respectively. $NQPF$ represents New Quality Productivity Forces, $Gov$ denotes local government competition, and $X$ is a vector of control variables. $\mu_{i}$ and $\nu_{t}$ represent region and time fixed effects, which control for unobserved factors that do not vary across regions or over time. $\epsilon_{it}$ is the random error term, assumed to follow a normal distribution.
We further examine the mediating roles of factor mobility, technological innovation, and industrial upgrading in the relationship between local government competition and NQPF，with a two-step estimation method for the mediation test. 
\begin{equation}
    M_{i,t} = \alpha + \beta Gov_{{it}} + \gamma X + \mu_i + \nu_t + \epsilon_{it}
\end{equation}
Here $M$ denotes the instrumental variables.
Finally, to test the potential spatial effects of local government competition on NQPF, we construct the following model, where $w_{ij}$ denotes the spatial weight matrix.
\begin{equation}
    NQPF_{it} = \alpha_1 Gov_{it} + \alpha_2 w_{ij} Gov_{it} + \alpha_3 X + \alpha_4 w_{ij} X + \mu_i + \nu_t + \epsilon_{it}
\end{equation}
\begin{equation}
     M_{i,t} = \beta_1 Gov_{it} + \beta_2 w_{ij} Gov_{it} + \beta_3 X + \beta_4 w_{ij} X + \mu_i + \nu_t + \epsilon_{it}
\end{equation}
    




\section{Empirical results}
\label{sec:model}\label{section4}


\subsection{Benchmark regression results} 

According to Equation~(\ref{eq:eq1}), we examine the effect of innovation-driven local government competition on the development of NQPF. Diagnostic tests support the model specification: statistically significant F-statistics for individual fixed effects (4.21) and year fixed effects (17.66), along with a robust Hausman test statistic (14.51), justify the use of a two-way fixed effects estimator. As reported in Column (1) of \hyperref[table:5]{Table 5}, a 1\% increase in the local government competition index raises the NQPF index by 0.337\%, supporting \hyperref[Hypothesis1]{Hypothesis 1}. This result aligns with theoretical expectations that innovation-oriented competition enhances digital governance and institutional support, thereby strengthening the technological and developmental foundations for NQPF.


\subsection{Endogenous test}
To address potential endogeneity due to reverse causality and omitted variables, we employ fiscal decentralization as an instrumental variable (IV). The Hausman test (39.736) confirms endogeneity in the local government competition($Gov$)  under the innovation-driven framework. Fiscal decentralization reflects local fiscal autonomy and influences competition strategies\citep{WU2025} , yet exhibits no significant correlation with NQPF (exogeneity test: 0.046, p = 0.8296), satisfying relevance and exogeneity conditions. As shown in Column (2) of \hyperref[table:5]{Table 5}, after accounting for endogeneity, innovation-driven competition continues to exhibit a significant positive impact on NQPF, though the coefficient decreases to 0.184. The overall consistency with baseline results confirms the robustness of our findings.

To further mitigate endogeneity concerns, this paper employs the Innovative Cities pilot policy as an exogenous shock to conduct a Difference-in-Differences (DID) test. This policy is coordinated at the national level, and the selection of pilot cities is both policy-oriented and random. Moreover, there is no systematic difference in the level of NQPF between pilot and non-pilot cities before the policy implementation, which satisfies the exogenous attribute requirements of a quasi-natural experiment. The DID model is constructed as follows:

$$NQPF_{it} = \alpha + \beta \cdot DID_{it} + \gamma \cdot Controls_{it} + \mu_i + \lambda_t + \varepsilon_{it}$$

Where \( DID_{it} \) is the interaction term of “policy dummy variable (pilot city = 1, non-pilot city = 0) × time dummy variable (post-policy implementation = 1, pre-policy implementation = 0)".

Column (3) of \hyperref[table:5]{Table 5} reports the DID estimation results. The coefficient of $DID$ is 0.021, indicating that at the 1\% significance level, the Innovative Cities pilot policy significantly improves the level of urban NQPF. This result is consistent with the core conclusion of the baseline regression, meaning that under the scenario of a quasi-natural experiment with exogenous policy shocks, the promoting effect of local government competition on NQPF remains robust, further mitigating the interference of endogeneity bias.

Besides, according to \hyperref[Fig5]{Figure 5}, by calculating the average value of NQPF in pilot and non-pilot cities and winsorization, it is found that the average value of the effect of local government competition of the treatment group and the control group before the Innovative Cities pilot policy maintains the consistency of the overall change, and the coefficient estimates of the dummy variables are larger after the Innovative Cities pilot policy than before the implementation and are significant, which is in line with the parallel trend test. 



\subsection{Robustness Check}

To ensure robustness, we take robustness tests from the dimensions of dynamic evolution and time heterogeneity. 
Firstly, we adopt a system GMM dynamic panel model to capture lagged effects (Columns (4)-(5) of \hyperref[table:6]{Table 6}). Lagged first- and second-period government competition both exert significant positive impacts on NQPF, reflecting the persistent and dynamic nature of competitive strategies—shaped by leadership transitions and policy adjustments—rather than static effects. 
Secondly, considering the 2013 18th National Congress explicitly proposed the innovation-driven development strategy and the Ministry of Science and Technology issued the Guidelines for Building Innovative Cities, which strengthened pilot construction and clarified indicators, we divide the sample into 2009–2013 and 2014–2022. The results shown in Columns (6)–(7) of \hyperref[table:6]{Table 6} confirm the stable positive effect of government competition on NQPF across both periods, verifying robustness amid changes in central policy focus. 
These dynamic panel tests and time-grouped regression validate the robustness of the core conclusion—that innovation-driven local government competition stably promotes NQPF by capturing the temporal and cross-sectional variations in the relationship between the two variables.

Furthermore,  we conduct checks through methodological adjustments, variable substitution, and sample modification. Specifically, we employ a system GMM estimator to account for the dynamic nature of NQPF, replace the main explanatory variable with the proportion of innovation-related keywords in government work reports (2009–2022) extracted via Python text mining, and restrict the sample to 102 large and medium-sized cities more likely to pursue NQPF development. Results in column (1) and (8) of \hyperref[table:6]{Table 6} consistently show a significant positive effect of innovation-driven local government competition on NQPF, confirming baseline estimates. Moreover, system GMM results reveal strong path dependency in NQPF and show that lagged competition also exerts a significant positive effect, indicating persistent impacts over time.

To further verify the reliability of the core conclusion, we conduct additional variable substitution tests by adopting two widely used $NQPF$ indicators from existing literature and authoritative databases. The regression results in columns (2)–(3) of \hyperref[table:6]{Table 6} show that the coefficient of $Gov$ remains significantly positive at the 1\% significance level, indicating that regardless of whether we adopt the NQPF indicator from representative academic research or an authoritative professional database, innovation-driven local government competition still has a stable promoting effect on NQPF. The consistency of the results across different indicator construction methods and data sources further eliminates the interference of potential measurement errors in the core explanatory variable, fully verifying the robustness of the study’s core conclusion.

%Table 6的（8）没有介绍，需要在note中提一下？

\subsection{Mechanism tests}
We employ a two-step approach to examine the mechanisms through which innovation-driven local government competition influences NQPF. The results, presented in \hyperref[table:7]{Table 7}, show that this competition significantly promotes NQPF and positively affects the mobility of data, capital and labour factors, as well as technological innovation, thus supporting \hyperref[Hypothesis2]{Hypothesis 2} and \hyperref[hypothesis3]{Hypothesis 3}. However, its impact on industrial upgrading is statistically insignificant, likely due to the long-term gradual nature of structural transformation and policy inconsistencies caused by frequent leadership changes \citep{agihon2015industrial}. In terms of effect magnitude, competition facilitates the most technological innovation, data mobility and capital inflows, while its influence on  labour mobility remains limited. This may reflect a policy focus more on innovation-driven enterprises, data value and high-skilled talent rather than broader labour markets \citep{CAI2025105584}, suggesting space for strategies that  attract greater participation of workforce.

Further, we introduce three moderating variables—regional technological innovation level, resource structure (classified based on the National Sustainable Development Plan for Resource-Based Cities (2013-2020)), and business environment—to construct a more nuanced theoretical framework, exploring the nonlinear relationships and contextual dependencies between innovation-driven local government competition and NQPF. The results in \hyperref[table:8]{Table 8}, show that regional technological innovation level positively moderates the promotion effect of local government competition on NQPF, as a strong innovation foundation enhances the efficiency of competition-driven innovation investment and technology conversion; the business environment not only directly promotes NQPF but also positively moderates the above relationship by constraining rent-seeking and avoiding inefficient resource competition; while the resource structure itself exerts a certain impact on NQPF, it does not significantly adjust the effect of local government competition on NQPF, possibly due to convergent competition strategies across regions or the offsetting effect of the "resource curse".

\subsection{Spatial effects test}
To examine spatial effects, we employ a spatial econometric model. Significant Moran’s I indices confirm spatial autocorrelation. Specification tests  support adopting a Spatial Durbin Model (SDM), further validated via degeneration tests, with statistics all showing significance, confirming that the Spatial Durbin Model is appropriate for capturing the spatial dynamics. Results in \hyperref[table:9]{Table 9} show that innovation-driven local government competition exerts significant positive direct and spatial spillover effects on NQPF, supporting \hyperref[hypothesis4]{Hypothesis 5}. The spatial autocorrelation coefficient  ($\rho$) is significantly positive, indicating regional interdependence in NQPF development. Mediation effects remain robust spatially: technological innovation and factor mobility, especially data and capital, show significant indirect spillovers, while industrial upgrading is insignificant. Moreover, spatial-lagged mediators reveal that data mobility ($W.Da$) and technological innovation ($W.Tia$) produce positive spillovers, whereas capital and labor mobility show weak negative effects due to factor rivalry and displacement effects. This confirms data and innovation drive regional convergence through collaborative spillovers\citep{CAI2025105584}, while capital mobility may induce development imbalances through resource competition. Overall, innovation-driven competition promotes NQPF in neighboring regions primarily through technology and data mobility channels.



\subsection{Heterogeneity analysis}

To further explore the contextual differences in the impact of innovation-driven local government competition on NQPF, we conduct heterogeneity analysis by grouping cities based on five key dimensions: region, city types,  technological innovation level, resource development stage, and business environment quality (\hyperref[table:10]{Table 10} and \hyperref[table:11]{Table 11}). The results show that the promoting effect of local government competition on NQPF exhibits significant heterogeneous characteristics across different groups.

From a regional perspective, endowed with a solid industrial foundation, highly agglomerated innovative factors and mature market-oriented mechanisms, eastern China sees local government competition more oriented toward the core dimensions of NQPF such as innovation-driven growth and industrial upgrading to the high end; the precise policy implementation and efficient resource integration here render the enabling effect of government competition on new-quality productive forces the most prominent. Central China, by virtue of its geographical advantage of connecting eastern and western China and a certain level of industrial supporting capacity, can effectively undertake the transfer of innovative factors and industries from the east, leading to a moderate driving effect of local government competition on new-quality productive forces. In contrast, constrained by lagging industrial structural transformation and insufficient supply of innovative factors, Northeastern China, together with western China which suffers from inadequate infrastructure support and limited capacity for agglomerating high-end factors, witnesses a relatively weaker positive driving effect of local government competition on the development of NQPF. 

From the perspective of urban hierarchy, as national innovation cores and global resource allocation hubs, first-tier cities rely more on a market-oriented, high-end endogenous innovation system for the development of NQPF, with their development positioning focusing more on the national top-level layout and coordinated development of innovation; this weakens the endogenous motivation for inter-local government competition, resulting in the minimal marginal impact of such competition on NQPF. Second-tier cities are in a critical stage of industrial structure optimization and upgrading as well as the accelerated agglomeration of innovative factors, where local government competition takes attracting high-end factors, cultivating emerging industries and improving the innovation ecosystem as core approaches; the high pertinence and implementability of policy incentives here make second-tier cities the core carriers for government competition to boost the development of NQPF, thus leading to the most pronounced impact. Third-tier cities possess a certain industrial foundation and factor undertaking capacity, and can leverage local government competition to undertake industrial and innovation spillovers from second-tier cities, yet their level of factor agglomeration and demand for industrial upgrading are both weaker than those of second-tier cities, hence the suboptimal driving effect of local government competition on NQPF in such cities.

In terms of technological innovation level, the promotion effect is stronger in regions with high innovation capacity than in those with low innovation capacity, reflecting that a solid innovation foundation can better amplify the dividend of competitive policies. Regarding resource development stage, resource-developing cities show a more prominent promotion effect compared to resource-mature cities, which may be related to the policy tilt and sustainable development incentives for emerging resource-based cities. In terms of business environment quality, both regions with excellent and poor business environments show significant positive effects, but the relative strength difference is mild, indicating that even in regions with suboptimal institutional environments, innovation-driven government competition can still effectively promote NQPF through targeted policy interventions. These heterogeneous findings provide empirical support for formulating differentiated regional development strategies.
%这段的补充是正确的，关于异质性检验的结论。



\section{Conclusions and implications}
\label{section5}
Based on panel data from 281 prefecture-level cities (2009–2022), this study employs fixed-effects models, mediation analysis, and spatial econometric methods to evaluate the impact of innovation-driven local government competition on the development of NQPF, including its transmission mechanisms and spatial dynamics. Empirical results indicate that: (1) Such competition significantly promotes NQPF, with notable immediate, lagged, and spatial effects; (2) The positive influence operates mainly through factor mobility and technological innovation, rather than industrial upgrading; (3) Significant spatial spillover effects are observed, particularly through technological innovation, data factor mobility, and, to a lesser extent, capital mobility, while labor mobility shows limited spillovers.

These findings offer relevant policy insights: First, local governments should refine competition strategies to emphasize differentiated and innovation-oriented regulation. In particular, efforts should strengthen mechanisms for data factor allocation, such as upgrading digital infrastructure, establishing innovation-focused fiscal instruments, and developing intelligent policy platforms that foster industry–university–research collaboration and cross-regional factor flows. Place-based performance indicators and regional coordination mechanisms can further help amplify positive spillovers and curb destructive competition.

Second, policy should prioritize enhancing innovation incentives and optimizing factor supply systems. Building digital innovation ecosystems that incorporate global data resources and standards, advancing domestic data trading markets, and improving innovation infrastructure are essential to stimulate factor mobility and knowledge diffusion. Additionally, advancing digital penetration across society and implementing AI-supported talent platforms can significantly improve labor matching efficiency.

Third, it is recommended to establish collaborative platforms that integrate industrial chains, innovation networks, and shared services. Such platforms can support the coordinated development of NQPF across regions by leveraging regional comparative advantages, facilitating resource redistribution, especially between urban and rural areas, and providing integrated support services, including technology, capital, and talent development.
\bigskip


%\section*{Data availability statement}
%The data that support the findings of this study are available from the corresponding authors, upon reasonable request.

%\section*{Funding}
%This work was supported by the Project of the National Natural Science Foundation of China (NSFC) (No:72373079), and Social Science Foundation of Shaanxi Province(2021D050,2024F014).

%\section*{Disclosure statement}
%No potential conflict of interest was reported by the author(s).
 
\newpage
\bibliographystyle{apalike}
\bibliography{refs(NQPF)}

%cas-model2-names
%\bibliograpystyle{apacite}   % name your BibTeX data base


\newpage


\section{Appendix}

\subsection{Appendix A: The Construction of the $NQPF$ Index}
This appendix elaborates on the comprehensive process of constructing the New Quality Productive Forces ($NQPF$) index, the core explanatory variable in this study. The methodology follows the entropy weight method referenced from \citet{Ren2024}, ensuring an objective assignment of weights based on the informational content of each indicator.

\item\textbf{Step 1: Indicator System Construction and Data Collection}
Based on the theoretical framework of new quality productive forces, we constructed a multidimensional evaluation system encompassing three primary dimensions: new quality labor force, new quality labor objects, and new quality labor materials. A total of 17 specific indicators were selected under these dimensions, as detailed in \hyperref[tab:nqpf_system]{Table 1} of the main text.
Our initial sample covered 281 prefecture-level cities in China from 2009 to 2022. Missing data was addressed using linear interpolation or trend extrapolation to ensure a balanced panel. The raw data for these indicators were meticulously collected from authoritative sources, including:
\begin{itemize}
    \item {China City Statistical Yearbook} (2009--2022)
    \item {China Environmental Statistical Yearbook} (2009--2022)
    \item Provincial and municipal statistical bulletins (2009--2022)
    \item Patent data from the China National Intellectual Property Administration (CNIPA)
\end{itemize}
\item\textbf{Step 2: Data Standardization}
To eliminate the influence of differing units and scales across indicators, we performed a min-max normalization on the original data matrix. For a given indicator \( j \) across \( n \) cities, the standardized value \( x_{ij}' \) for city \( i \) is calculated as follows:
For positive indicators (where a higher value is better):
\begin{equation}
x_{ij}' = \frac{x_{ij} - \min(x_j)}{\max(x_j) - \min(x_j)}
\end{equation}
For negative indicators (where a lower value is better):
\begin{equation}
x_{ij}' = \frac{\max(x_j) - x_{ij}}{\max(x_j) - \min(x_j)}
\end{equation}
where \( x_{ij} \) is the original value of indicator \( j \) for city \( i \), and \( \min(x_j) \) and \( \max(x_j) \) are the minimum and maximum values of indicator \( j \) across all cities, respectively.
\item\textbf{Step 3: Entropy Weight Calculation}
The entropy weight method was then employed to determine the objective weight for each indicator based on its variability, which reflects the amount of information it provides.
\begin{enumerate}
    \item Calculate the proportion \( p_{ij} \) of city \( i \) in indicator \( j \):
    \begin{equation}
    p_{ij} = \frac{x_{ij}'}{\sum_{i=1}^{n} x_{ij}'}
    \end{equation}
    \item Calculate the entropy value \( e_j \) for indicator \( j \):
    \begin{equation}
    e_j = -\frac{1}{\ln n} \sum_{i=1}^{n} p_{ij} \ln p_{ij}
    \end{equation}
    (Note: If \( p_{ij} = 0 \), we define \( \lim_{p_{ij} \to 0} p_{ij} \ln p_{ij} = 0 \).)
    \item Calculate the divergence degree \( d_j \) for indicator \( j \):
    \begin{equation}
    d_j = 1 - e_j
    \end{equation}
    A larger \( d_j \) indicates a greater discriminatory power of the indicator and thus a greater importance in the evaluation.
    \item Determine the objective weight \( w_j \) for indicator \( j \):
    \begin{equation}
    w_j = \frac{d_j}{\sum_{j=1}^{m} d_j}
    \end{equation}
\end{enumerate}
\item\textbf{Step 4: Composite Score Calculation}
The final NQPF index for each city \( i \) was computed as the weighted sum of all standardized indicators using the entropy-derived weights:
\begin{equation}\mathrm{NQPF}i = \sum_{j=1}^{m} w_j \cdot x_{ij}'\end{equation}


\subsection{Appendix B: The Construction of the Local Government Competition ($Gov$) Index}
\item\textbf{Step 1: Data Collection and Processing of Indicator Variables}
We first constructed a multidimensional evaluation system to capture the intensity of innovation-driven competition among local governments. The system comprises a total of 16 specific indicators across five key dimensions, as detailed in \hyperref[tab:competitionindexsystem]{Table 2} of the main text.
The raw data for these indicators were meticulously collected from authoritative sources, including:
\begin{itemize}
    \item {China City Statistical Yearbook (2009-2022)}
    \item {China Fiscal Yearbook (2009-2022)}
    \item Provincial and municipal statistical bulletins and government work reports (2009-2022)
    \item Database of national development zones and pilot zones
\end{itemize}
To ensure comparability across different units and scales, all indicators were subjected to a min-max normalization process. For a given indicator $j$ across $n$ city-year observations, the standardized value $x_{ij}'$ for city $i$ in year $t$ is calculated as follows:
For positive indicators (where a higher value indicates stronger competition):
\begin{equation}
x_{ij}' = \frac{x_{ij} - \min(x_j)}{\max(x_j) - \min(x_j)}
\end{equation}
For negative indicators (where a lower value indicates stronger competition):
\begin{equation}
x_{ij}' = \frac{\max(x_j) - x_{ij}}{\max(x_j) - \min(x_j)}
\end{equation}
\item\textbf{Step 2: Composite Index Calculation using Global Principal Component Analysis (GPCA)}
To synthesize the information from the 16 normalized indicators into a single, comparable composite index ($Gov$), we employ the Global Principal Component Analysis (GPCA) method. This technique, following the approach of \citet{bonzo2002clustering}, objectively derives weights based on the statistical characteristics of the data, effectively handling potential multicollinearity among indicators.
The construction process involves the following steps:
\begin{enumerate}
    \item \textbf{Component Extraction:} The GPCA is performed on the standardized data matrix for all city-year observations. This involves solving the eigenvalue problem for the correlation matrix of the indicators, resulting in a set of principal components (PCs) that are linear combinations of the original variables.
    \item \textbf{Component Selection:} Principal components are selected based on the Kaiser criterion (eigenvalue $> 1$). The first $m$ components that collectively explain a substantial majority of the total variance in the original dataset are retained.
    \item \textbf{Index Calculation:} The composite $Gov$ index for each city-year observation is calculated as the weighted sum of the scores of the retained principal components. The weight for each PC is determined by the proportion of the total variance it explains.
    The formula is given by:
    \begin{equation}
    \mathrm{Gov}_{it} = \sum_{p=1}^{m} \left( \frac{\lambda_p}{\sum_{p=1}^{m} \lambda_p} \right) \cdot \mathrm{PC}_{pit}
    \end{equation}
    where:
    \begin{itemize}
        \item[--] $\mathrm{Gov}_{it}$ is the government competition index for city $i$ in year $t$.
        \item[--] $\lambda_p$ is the eigenvalue of the $p$-th principal component.
        \item[--] $\mathrm{PC}_{pit}$ is the score of the $p$-th principal component for city $i$ in year $t$.
    \end{itemize}
\end{enumerate}

This procedure results in a continuous, cross-sectionally and temporally comparable index. A higher value of the $Gov$ index indicates a higher intensity of innovation-driven competition among local governments.


\begin{landscape}

\newlength{\totalwidth}
\setlength{\totalwidth}{\linewidth}

\newcolumntype{C}{>{\raggedright\arraybackslash}p{0.15\totalwidth}} % Dimension 15%
\newcolumntype{N}{>{\raggedright\arraybackslash}p{0.25\totalwidth}} % Component 20%
\newcolumntype{Y}{>{\raggedright\arraybackslash}p{0.5\totalwidth}} % Definition 55%
\newcolumntype{D}{>{\centering\arraybackslash}p{0.1\totalwidth}}   % Direction 10%

\begin{table}[ht]
  \centering
  \caption{NQPF Indicator System}
  \label{tab:nqpf_system}
  \begin{tabular}{C N Y D}
    \toprule
    \textbf{Dimension} & \textbf{Component} & \textbf{Definition} & \textbf{Direction} \\
    \midrule
    \textbf{New Quality Labor Force} & Employees in Emerging Industries & Total employees of listed companies in strategic emerging and future industries, aggregated by city registration. & Positive \\
    & Individual Employee Ability & Average on-the-job wage (RMB), reflecting local talent’s average ability. & Positive \\
    & High-Quality Employees & Number of regular higher education institutions, representing local capacity to cultivate talent. & Positive \\
    \cmidrule(lr){1-4}
    \textbf{New Quality Labor Objects} & Broadband Users & Broadband internet users (thousands). & Positive \\
    & Telecom Business Volume & Total telecommunication business volume (100 million RMB). & Positive \\
    & Robot Density & Robot installation density. & Positive \\
    & Pollution Control Investment & Investment in environmental pollution control (100 million RMB). & Positive \\
    & Carbon and Emission Trading & Carbon, energy use, and emission rights trading (100 million RMB). & Positive \\
    & Waste Treatment Rate & Harmless treatment rate of domestic waste (\%). & Positive \\
    \cmidrule(lr){1-4}
    \textbf{New Quality Labor Materials} & Scientific Research Spending & Proportion of science expenditure in local fiscal spending. & Positive \\
    & Invention Patents & Number of invention patent applications in the year. & Positive \\
    & Utility Model Patents & Number of utility model patent applications in the year. & Positive \\
    & AI Enterprises & Number of artificial intelligence enterprises. & Positive \\
    & Green Invention Patents & Number of green invention patent applications in the year. & Positive \\
    & Green Utility Model Patents & Number of green utility model patent applications in the year. & Positive \\
    & Data Utilization Level & Logarithm of (data asset keyword frequency + 1) for listed companies by city; proxy based on Yuan Zeming (2022). & Positive \\
    & Data Trading Platform Presence & Manually collected per city (1 = yes, 0 = no). & Positive \\
    \bottomrule
  \end{tabular}
\end{table}
\end{landscape}
















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% Please add the following required packages to your document preamble:
% \usepackage{graphicx}
\begin{landscape}
\begin{table}[ht]
  \centering
  \caption{Innovation-driven local government competition index system}
  \label{tab:competitionindexsystem}
  \renewcommand{\arraystretch}{1.2}  % Optional: increases row height
  \begin{tabularx}{\linewidth}{l l X c}
    \toprule
    \textbf{Dimension} & \textbf{Sub-Dimension} & \textbf{Basic Indicator} & \textbf{Attribute} \\
    \midrule
    \textbf{Economic Incentives} & Expenditure Competition & Per capita total fiscal expenditure & Positive \\
    & & Proportion of per capita economic expenditure in total fiscal expenditure & Positive \\
    & & Proportion of per capita social expenditure in total fiscal expenditure & Positive \\
    & & Proportion of per capita maintenance expenditure in total fiscal expenditure & Positive \\
    & & Proportion of per capita investment expenditure in total fiscal expenditure & Positive \\
    & & Proportion of per capita administrative expenditure in total fiscal expenditure & Positive \\
    & & Proportion of local fiscal revenue to local GDP & Negative \\
    & Tax Competition & Ratio of central government fiscal transfers to local GDP & Positive \\
    & & Proportion of innovation- and tax-related phrases in government reports & Positive \\
    & FDI Competition & Ratio of FDI to GDP & Positive \\
    & & Proportion of innovation- and capital-related phrases in government reports & Positive \\
    & Innovation Competition & Innovation capital mobility & Positive \\
    & & Innovation personnel mobility & Positive \\
    & & Number of pilot zones & Positive \\
    & & Number of development zones & Positive \\
    \textbf{Political Incentives} & Promotion Competition & Index of officials' promotion incentives & Positive \\
    \bottomrule
  \end{tabularx}
\end{table}
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\begin{table}[htbp]
\label{table:3}
\caption{Descriptive Statistics of Main Variables}
\small 
\centering
\setlength{\tabcolsep}{4pt} 
\begin{tabularx}{0.98\textwidth}{@{} l >{\raggedright\arraybackslash}p{1.8cm} c D{.}{.}{2} D{.}{.}{2} D{.}{.}{2} D{.}{.}{2} @{}} 
\toprule[1.2pt] 
\multicolumn{1}{c}{\textbf{Variable}} & \textbf{Type} & \textbf{Symbol} & \multicolumn{1}{c}{\textbf{Obs.}} & \multicolumn{1}{c}{\textbf{Mean}} & \multicolumn{1}{c}{\textbf{Std.Dev}} & \multicolumn{1}{c}{\textbf{Min}} & \multicolumn{1}{c}{\textbf{Max}} \\
\midrule[0.8pt] 
New Quality Productive Forces & Dependent & $NQPF$ & 3934 & 0.0484 & 0.0708 & 0.0019 & 0.6182 \\
Local Government Competition & Independent & $Gov$ & 3934 & 0.0344 & 0.0517 & 0.0007 & 1.0478 \\
Data Factor Mobility & Mediating & $Da$ & 3934 & 1.0490 & 0.5306 & 0.0000 & 2.3287 \\
Capital Factor Mobility & Mediating & $Ka$ & 3934 & 2.5668 & 2.1358 & 0.0665 & 13.3216 \\
Labor Factor Mobility & Mediating & $La$ & 3934 & 0.0027 & 0.0034 & -0.0006 & 0.0168 \\
Technological Innovation & Mediating & $Tia$ & 3934 & 6.8177 & 1.7526 & 0.0000 & 11.8949\\
Industrial Upgrading & Mediating & $Inu$ & 3934 & 0.8950 & 0.4781 & 0.1310 & 4.9322 \\
Secondary Industry Share & Control & $Ind2$ & 3934 & 0.4801 & 0.1077 & 0.1068 & 0.8564 \\
Tertiary Industry Share & Control & $Ind3$ & 3934 & 0.3904 & 0.0934 & 0.1122 & 0.7923 \\
Urbanization Rate & Control & $Urb$ & 3934 & 0.5489 & 0.1523 & 0.1513 & 1.0000 \\
Fixed Asset Investment Ratio & Control & $Inv$ & 3934 & 0.5549 & 0.3740 & 0.0000 & 2.1969 \\
No. of Higher Education Institutions & Control & $Pub$ & 3934 & 8.3129 & 13.6881 & 0.0000 & 84.0000 \\
Science Expenditure Ratio & Control & $Stu$ & 3934 & 0.0165 & 0.0171 & 0.0005 & 0.2068 \\
\bottomrule[1.2pt]
\end{tabularx}
\end{table}

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\begin{landscape}
\begin{table}[htbp]
\caption{Pearson Correlation Matrix}
\label{table:4}
\begin{center}
\footnotesize
\begin{tabular}{l c c c c c c c c c c c c c}
\toprule
\textbf{Variables} & \textbf{$NQPF$} & \textbf{$Gov$} & \textbf{$Da$} & \textbf{$Ka$} & \textbf{$La$} & \textbf{$Rd$} & \textbf{$Inu$} & \textbf{$Ind21$} & \textbf{$Ind31$} & \textbf{$Urb$} & \textbf{$Inv$} & \textbf{$Uct$} & \textbf{$Ste$} \\
\midrule
$NQPF$ & 1 & & & & & & & & & & & & \\
$Gov$ & 0.2977\textsuperscript{***} & 1 & & & & & & & & & & & \\
$Da$ & 0.2797\textsuperscript{***} & 0.0586 & 1 & & & & & & & & & & \\
$Ka$ & 0.0493\textsuperscript{**} & 0.0378\textsuperscript{**} & -0.0149 & 1 & & & & & & & & & \\
$La$ & -0.0404\textsuperscript{***} & 0.0572\textsuperscript{***} & -0.0208 & -0.0214 & 1 & & & & & & & & \\
$Tia$ & 0.4955\textsuperscript{***} & -0.1441\textsuperscript{***} & 0.3768\textsuperscript{***} & 0.0364\textsuperscript{**} & -0.0598\textsuperscript{***} & 1 & & & & & & & \\
$Inu$ & 0.1761\textsuperscript{***} & 0.0543 & -0.0251 & -0.0194 & 0.0103 & -0.0302 & 1 & & & & & & \\
$Ind2$ & -0.0348\textsuperscript{***} & -0.0353\textsuperscript{***} & 0.1250\textsuperscript{***} & 0.0229 & 0.0408\textsuperscript{**} & 0.1336\textsuperscript{***} & -0.8392\textsuperscript{***} & 1 & & & & & \\
$Ind3$ & 0.3988\textsuperscript{***} & 0.0991\textsuperscript{***} & 0.1645\textsuperscript{***} & 0.0116 & 0.0069 & 0.2021\textsuperscript{***} & 0.7873\textsuperscript{***} & -0.6653\textsuperscript{***} & 1 & & & & \\
$Urb$ & 0.4569\textsuperscript{***} & 0.0928\textsuperscript{***} & 0.2048\textsuperscript{***} & 0.1127\textsuperscript{***} & 0.0365\textsuperscript{**} & 0.3667\textsuperscript{***} & 0.0349\textsuperscript{**} & 0.2090\textsuperscript{***} & 0.2868\textsuperscript{***} & 1 & & & \\
$Inv$ & -0.0824\textsuperscript{***} & -0.0190 & -0.0747\textsuperscript{***} & 0.0151 & 0.0244 & -0.1315\textsuperscript{***} & -0.2051\textsuperscript{***} & 0.1507\textsuperscript{***} & -0.3078\textsuperscript{***} & -0.1323 & 1 & & \\
$Uct$ & 0.7514\textsuperscript{***} & 0.3243\textsuperscript{***} & 0.2119\textsuperscript{***} & 0.0812\textsuperscript{***} & -0.0465\textsuperscript{**} & 0.3484\textsuperscript{***} & 0.2067\textsuperscript{***} & -0.0834 & 0.4251\textsuperscript{***} & 0.3949\textsuperscript{***} & -0.0356 & 1 & \\
$Ste$ & 0.5325\textsuperscript{***} & 0.0727\textsuperscript{***} & 0.2352\textsuperscript{***} & 0.0792\textsuperscript{***} & 0.0196 & 0.5385\textsuperscript{***} & -0.0177 & 0.1537\textsuperscript{***} & 0.1940\textsuperscript{***} & 0.4448\textsuperscript{***} & -0.0636\textsuperscript{***} & 0.3640\textsuperscript{***} & 1 \\
\bottomrule
\end{tabular}
\end{center}
\vspace*{2pt}
\raggedright\scriptsize
\emph{Note:} Standard errors clustered by city in parentheses. 
Significance levels: \textsuperscript{*} 10\%, \textsuperscript{**} 5\%, \textsuperscript{***} 1\%. 
Same convention follows in subsequent tables.
\end{table}
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\begin{table}[htbp]
\caption{Estimation Results: Local Government Competition and New Quality Productive Forces}
\label{table:5}
\centering
\footnotesize
\begin{tabular}{l c c c}
\toprule
 & \multicolumn{3}{c}{\textbf{Dependent Variable: NQPF}} \\
\cmidrule(lr){2-4}
\textbf{Variable} & (1) & (2) & (3) \\
 & FE & IV & DID \\
\midrule
Local Gov. Competition ($Gov$) & 0.337\textsuperscript{***} & 0.184\textsuperscript{***} &  \\
 & (0.0181) & (0.0276) &  \\
$DID$ &  &  & 0.021\textsuperscript{***} \\
 &  &  & (0.0023) \\
\midrule
Secondary Industry Share ($Ind2$) & 0.011\textsuperscript{*} & 0.013\textsuperscript{*} & -0.006 \\
 & (0.0063) & (0.0072) & (0.0107) \\
Tertiary Industry Share ($Ind3$) & 0.006 & 0.069\textsuperscript{***} & 0.035\textsuperscript{***} \\
 & (0.0112) & (0.0119) & (0.0136) \\
Urbanization Rate ($Urb$) & 0.031\textsuperscript{***} & 0.038\textsuperscript{***} & 0.031\textsuperscript{***} \\
 & (0.0044) & (0.0063) & (0.0060) \\
Fixed Asset Inv. Ratio ($Inv$) & 0.000 & 0.002 & -0.004\textsuperscript{*} \\
 & (0.0010) & (0.0017) & (0.0020) \\
Higher Education Inst. ($Uct$) & 0.003\textsuperscript{***} & 0.003\textsuperscript{***} & 0.003\textsuperscript{***} \\
 & (0.0002) & (0.0001) & (0.0001) \\
Science Expenditure Ratio ($Ste$) & 0.779\textsuperscript{***} & 1.076\textsuperscript{***} & 1.011\textsuperscript{***} \\
 & (0.0323) & (0.1188) & (0.0466) \\
\midrule
Constant & 0.003 & 0.052\textsuperscript{***} & -0.022\textsuperscript{**} \\
 & (0.0099) & (0.0072) & (0.0088) \\
\midrule
Time Fixed Effects & Yes & Yes & Yes \\
Region Fixed Effects & Yes & Yes & Yes \\
Observations & 3,934 & 3,934 & 3,934 \\
Adjusted R$^2$ & 0.942 & 0.654 & 0.658 \\
F Statistic & 254.72 & - & 1003.25 \\
\bottomrule
\end{tabular}
\end{table}


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\begin{landscape}
\begin{table}[htbp]
\caption{Robustness Test Results}
\label{table:6}
\begin{center}
\footnotesize
\begin{tabular}{l c c c c c c c c c}
\toprule
\multirow{2}{*}{\textbf{Variable}} & \multicolumn{8}{c}{\textbf{Dependent Variable: New Quality Productive Forces (NQPF)}} \\
\cmidrule(lr){2-9}
& (1) & (2) & (3) & (4) & (5) & (6) & (7) & (8) \\
& Variable  Replacement & NQPF\citep{LIU2024103373} & NQPF(CNRDS) & SYS-GMM & SYS-GMM(Extended) & 2009-2013 & 2014-2022 & Sample Change \\
\midrule
$NQPF_{t-1}$ &  &  &  & 0.223\textsuperscript{***} & 0.191\textsuperscript{***} &  &  &  \\
 &  &  &  & (0.0126) & (0.0087) &  &  &  \\
$Gov$ & 0.050\textsuperscript{***} & 0.234\textsuperscript{***} & 0.140\textsuperscript{***} & 0.186\textsuperscript{***} & 0.242\textsuperscript{***} & 0.370\textsuperscript{***} & 0.321\textsuperscript{***} & 0.260\textsuperscript{***} \\
 & (0.0011) & (0.0395) & (0.0287) & (0.0145) & (0.0147) & (0.0252) & (0.0285) & (0.0269) \\
$Gov_{t-1}$ &  &  &  & -0.150\textsuperscript{***} & 0.162\textsuperscript{***} &  &  &  \\
 &  &  &  & (0.0164) & (0.0123) &  &  &  \\
$Gov_{t-2}$ &  &  &  &  & 0.010\textsuperscript{*} &  &  &  \\
 &  &  &  &  & (0.0052) &  &  &  \\
\midrule
$Ind2$ & 0.018\textsuperscript{*} & 0.180\textsuperscript{***} & 0.068\textsuperscript{***} & 0.015\textsuperscript{*} & 0.022\textsuperscript{**} & 0.009 & 0.017 & 0.065\textsuperscript{**} \\
 & (0.0093) & (0.0310) & (0.0230) & (0.0081) & (0.0085) & (0.0152) & (0.0181) & (0.0262) \\
$Ind3$ & 0.011\textsuperscript{***} & 0.275\textsuperscript{***} & 0.118\textsuperscript{***} & 0.011\textsuperscript{***} & 0.023\textsuperscript{***} & -0.011 & 0.013 & 0.013\textsuperscript{***} \\
 & (0.0032) & (0.0389) & (0.0289) & (0.0031) & (0.0083) & (0.0151) & (0.0180) & (0.0027) \\
$Urb$ & 0.032\textsuperscript{**} & 0.063\textsuperscript{***} & 0.035\textsuperscript{***} & 0.035\textsuperscript{***} & 0.008\textsuperscript{**} & -0.002 & 0.003 & 0.038\textsuperscript{***} \\
 & (0.0037) & (0.0174) & (0.0129) & (0.0040) & (0.0040) & (0.0060) & (0.0073) & (0.0088) \\
$Inv$ & 0.000 & -0.044\textsuperscript{***} & -0.032\textsuperscript{***} & 0.001 & 0.002\textsuperscript{***} & -0.000 & 0.000 & 0.001 \\
 & (0.0008) & (0.0059) & (0.0043) & (0.0007) & (0.0006) & (0.0014) & (0.0016) & (0.0024) \\
$Uct$ & 0.002\textsuperscript{***} & -0.002\textsuperscript{***} & -0.001\textsuperscript{***} & 0.002\textsuperscript{***} & 0.002\textsuperscript{***} & 0.002\textsuperscript{***} & 0.004\textsuperscript{***} & 0.002\textsuperscript{***} \\
 & (0.0001) & (0.0002) & (0.0001) & (0.0002) & (0.0002) & (0.0002) & (0.0003) & (0.0003) \\
$Ste$ & 0.536\textsuperscript{**} & 2.308\textsuperscript{***} & 1.924\textsuperscript{***} & 1.140\textsuperscript{***} & 1.306\textsuperscript{***} & 0.833\textsuperscript{***} & 0.698\textsuperscript{***} & 1.539\textsuperscript{***} \\
 & (0.0273) & (0.1374) & (0.1024) & (0.0865) & (0.0617) & (0.0456) & (0.0482) & (0.0737) \\
\midrule
Constant & 0.012\textsuperscript{**} & 0.700\textsuperscript{***} & 0.102\textsuperscript{***} & 0.019\textsuperscript{**} & -0.028\textsuperscript{***} & 0.004 & -0.022 & 0.036\textsuperscript{***} \\
 & (0.0052) & (0.0252) & (0.0187) & (0.0076) & (0.0134) & (0.0159) & (0.0078) & (0.0214) \\
\midrule
Time FE & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes \\
Region FE & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes \\
Observations & 3,934 & 3,934 & 3,934 & 3,653 & 3,372 & 2,529 & 1,405 & 1,253 \\
Adjusted R$^2$ & 0.961 & 0.269 & 0.305 &  - & - & 0.938 & 0.951 & 0.951 \\
F Statistic & 603.07 & 107.18 & 97.67 & - & - & 143.24 & 100.27 & 138.97\\
\bottomrule
\end{tabular}
\end{center}
\vspace*{2pt}
\raggedright\scriptsize
\emph{Note:}Column (2) adopts the NQPF indicator constructed by \citet{LIU2024103373}, which focuses on synergistic industrial agglomeration and high-quality development of the manufacturing industry with mature and rigorous indicator construction logic. 
Column (3) uses the NQPF index from the CNRDS database—an authoritative database in economics and finance jointly built by senior professionals from domestic and foreign capital markets and renowned scholars, and operated by Shanghai Jinghe Information Technology Co., Ltd.—characterized by comprehensive data coverage, standardized statistical standards, and strong objectivity.
Columns (4)-(5) report results of system GMM dynamic panel models (Column (5) includes two lagged terms of local government competition to capture dynamic persistence).
Columns (6)-(7) present time-grouped regression results (divided by the 2013 18th National Congress, when the innovation-driven development strategy was explicitly proposed).
Same convention follows in subsequent tables.
\end{table}

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\begin{table}[htbp]
\caption{Mechanism Test Results}
\label{table:7}
\begin{center}
\footnotesize
\begin{tabular}{l c c c c c c}
\toprule
 & \multicolumn{6}{c}{\textbf{Dependent Variables}} \\
\cmidrule(lr){2-7}
\textbf{Independent Variables} & (1) & (2) & (3) & (4) & (5) & (6) \\
 & NQPF & Data Mobility & Capital Mobility & Labor Mobility & Tech Innovation & Industrial Upgrade \\
\midrule
$Gov$ & 0.337\textsuperscript{***} & 2.534\textsuperscript{***} & 1.840\textsuperscript{*} & 0.005\textsuperscript{*} & 15.863\textsuperscript{***} & 0.001 \\
 & (0.0181) & (0.3210) & (1.0301) & (0.0025) & (0.9109) & (0.1478) \\
Constant & 0.003 & 0.982\textsuperscript{***} & 2.635\textsuperscript{***} & 0.003\textsuperscript{***} & 4.542\textsuperscript{***} & 0.895\textsuperscript{***} \\
 & (0.0099) & (0.1763) & (0.0370) & (0.0001) & (0.5002) & (0.0117) \\
\midrule
Control Variables  & Yes & Yes & Yes & Yes & Yes & Yes \\
Time Fixed Effects & Yes & Yes & Yes & Yes & Yes & Yes \\
Region Fixed Effects & Yes & Yes & Yes & Yes & Yes & Yes \\
Observations & 3934 & 3934 & 3934 & 3934 & 3934 & 3934 \\
Adjusted R$^2$ & 0.942 & 0.681 & 0.794 & 0.685 & 0.763 & 0.618 \\
F Statistic & 254.72 & 15.33 & 11.47 & 21.69& 106.22 & 1712.18  \\
\bottomrule
\end{tabular}
\end{center}
\end{table}
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\begin{table}[htbp]
\caption{Moderating Effect Test Results}
\label{table:8}
\centering
\footnotesize
\begin{tabular}{l c c c c c c}
\toprule
 & \multicolumn{6}{c}{\textbf{Dependent Variable: New Quality Productive Forces (NQPF)}} \\
\cmidrule(lr){2-7}
\textbf{Variable} & (1) & (2) & (3) & (4) & (5) & (6) \\
\midrule
Local Gov. Competition ($Gov$) & 0.225$^{***}$ & 0.129$^{***}$ & 0.106$^{***}$ & 0.103$^{***}$ & 0.097$^{***}$ & -0.865$^{***}$ \\
 & (0.0177) & (0.0155) & (0.0137) & (0.0139) & (0.0134) & (0.1786) \\
Tech Innovation Level ($Tia$) & 0.007$^{***}$ & 0.005$^{***}$ &  &  &  &  \\
 & (0.0003) & (0.0006) &  &  &  &  \\
$Gov \times Tia$ &  & 0.043$^{***}$ &  &  &  &  \\
 &  & (0.0057) &  &  &  &  \\
Resource Structure ($Resource$) &  &  & -0.003$^{**}$ & -0.006$^{**}$ &  & \\
 &  &  &  (0.0016) & (0.0026) &  & \\
$Gov \times Resource$ &  &  &  & 0.094 &  &\\
 &  &  &  & (0.0747) &  & \\
Business Environment ($BEscore$) &  &  &  &  & 0.003$^{***}$ & 0.002$^{***}$ \\
 &  &  &  &  & (0.0002) & (0.0002) \\
$Gov \times BEscore$ &  &  &  &  &  & 0.021$^{***}$ \\
 &  &  &  &  &  & (0.0038) \\
\midrule
Control Variables & Yes & Yes & Yes & Yes & Yes & Yes \\
Time Fixed Effects & Yes & Yes & Yes & Yes & Yes & Yes \\
Region Fixed Effects & Yes & Yes & Yes & Yes & Yes & Yes \\
Constant & -0.035$^{***}$ & -0.071$^{***}$ & -0.045$^{***}$ & -0.045$^{***}$ & -0.116$^{***}$ & -0.081$^{***}$ \\
 & (0.0094) & (0.0085) & (0.0087) & (0.0087) & (0.0099) & (0.0118) \\
\midrule
Observations & 3934 & 3934 & 3934 & 3934 & 3934 & 3934 \\
Adjusted $R^2$ & 0.950 & 0.685 & 0.656 & 0.656 & 0.673 & 0.675 \\
F Statistic & 315.80 & 883.74 & 871.08 & 774.60 & 935.72 & 841.48 \\
\bottomrule
\end{tabular}
\end{table}


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\begin{landscape}
\begin{table}[htbp]
\caption{Spatial Effect Test Results}
\label{table:9}
\begin{center}

\scriptsize
\begin{tabular}{l c c c c c c c c c c c}
\toprule
 & \multicolumn{11}{c}{\textbf{Dependent Variable}} \\
\cmidrule(lr){2-12}
\textbf{Variable} & (1) & (2) & (3) & (4) & (5) & (6) & (7) & (8) & (9) & (10) & (11) \\
 & $NQPF$ & $Da$ & $NQPF$ & $Ka$ & $NQPF$ & $La$ & $NQPF$ & $Tia$ & $NQPF$ & $Inu$ & $NQPF$ \\
\midrule
$Gov$ & 0.465^{***} & 0.015^{**} & 0.424^{***} & 2.033^{**} & 0.083^{***} & 0.005^{**} & 0.465^{***} & 2.942^{***} & 0.009^{***} & 0.009 & 0.465^{***} \\
 & (0.0188) & (0.0073) & (0.0186) & (0.9370) & (0.0188) & (0.0023) & (0.0188) & (0.2889) & (0.0028) & (0.2844) & (0.0187) \\
$Da$ &  &  & 2.733^{***} &  &  &  &  &  &  &  &  \\
 &  &  & (1.0010) &  &  &  &  &  &  &  &  \\
$Ka$ &  &  &  &  & 0.188^{**} &  &  &  &  &  &  \\
 &  &  &  &  & (0.0903) &  &  &  &  &  &  \\
$La$ &  &  &  &  &  &  & 0.154^{***} &  &  &  &  \\
 &  &  &  &  &  &  & (0.0315) &  &  &  &  \\
$Tia$ &  &  &  &  &  &  &  &  & 0.155^{***} &  &  \\
 &  &  &  &  &  &  &  &  & (0.0315) &  &  \\
$Inu$ &  &  &  &  &  &  &  &  &  &  & 0.004^{***} \\
 &  &  &  &  &  &  &  &  &  &  & (0.0011) \\
$W.Gov$ & 0.040^{*} & 0.005^{*} & 0.038^{*} & -7.840^{***} & 0.033^{*} & -0.005^{*} & 0.039^{*} & 1.383^{***} & 0.034^{*} & 3.095 & 0.038^{*} \\
 & (0.0227) & (0.0026) & (0.0224) & (2.0977) & (0.200) & (0.0027) & (0.0224) & (0.4367) & (0.0202) & (3.3715) & (0.0228) \\
$W.Da$ &  &  & 0.013^{***} &  &  &  &  &  &  &  &  \\
 &  &  & (0.0037) &  &  &  &  &  &  &  &  \\
$W.Ka$ &  &  &  &  & -0.010^{**} &  &  &  &  &  &  \\
 &  &  &  &  & (0.0051) &  &  &  &  &  &  \\
$W.La$ &  &  &  &  &  &  & -1.075 &  &  &  &  \\
 &  &  &  &  &  &  & (1.8823) &  &  &  &  \\
$W.Tia$ &  &  &  &  &  &  &  &  & 1.054^{***} &  &  \\
 &  &  &  &  &  &  &  &  & (0.3825) &  &  \\
$W.Inu$ &  &  &  &  &  &  &  &  &  &  & 0.005 \\
 &  &  &  &  &  &  &  &  &  &  & (0.0039) \\
Spatial $\rho$ & 0.189^{*} & 0.340^{**} & 0.155^{*} & 0.750^{***} & 0.176^{*} & 0.341^{**} & 0.175^{*} & 0.189^{*} & 0.175^{*} & 0.152 & 0.180 \\
 & (0.1069) & (0.1450) & (0.0862) & (0.1605) & (0.1053) & (0.1451) & (0.1054) & (0.1069) & (0.1054) & (0.1354) & (0.1452) \\
\midrule
Control variables & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes \\
Time FE & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes \\
Region FE & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes \\
Observations & 3,934 & 3,934 & 3,934 & 3,934 & 3,934 & 3,934 & 3,934 & 3,934 & 3,934 & 3,934 & 3,934 \\
\bottomrule
\end{tabular}
\end{center}
\end{table}
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\begin{landscape}
\begin{table}[htbp]
\caption{Heterogeneity Analysis by Region and City Tier}
\label{table:10}
\begin{center}
\scriptsize
\begin{tabular}{l c c c c c c c}
\toprule
 & \multicolumn{7}{c}{\textbf{Dependent Variable: NQPF}} \\
\cmidrule(lr){2-8}
\textbf{Variable} & (1) & (2) & (3) & (4) & (5) & (6) & (7) \\
 & Eastern Region & Central Region & Western Region & Northeastern Region & First-tier City & Second-tier City & Third-tier City \\
\midrule
Local Gov. Competition ($Gov$) & 0.700$^{***}$ & 0.320$^{***}$ & 0.163$^{***}$ & 0.206$^{***}$ & 0.173$^{***}$ & 0.735$^{***}$ & 0.295$^{***}$ \\
 & (0.0541) & (0.0369) & (0.0129) & (0.0252) & (0.0772) & (0.0524) & (0.0106) \\
$Ind2$ & 0.064 & 0.007 & 0.010 & -0.013 & 0.647 & -0.008 & 0.006 \\
 & (0.0483) & (0.0128) & (0.0094) & (0.0119) & (0.5018) & (0.0497) & (0.0042) \\
$Ind3$ & 0.010 & 0.007 & 0.005 & -0.011 & 0.351 & -0.021 & 0.006 \\
 & (0.0430) & (0.0119) & (0.0100) & (0.0136) & (0.4763) & (0.0419) & (0.0043) \\
$Urb$ & 0.005 & 0.001 & -0.003 & 0.000 & 0.088 & 0.001 & -0.002 \\
 & (0.0119) & (0.0055) & (0.0043) & (0.0047) & (0.0611) & (0.0101) & (0.0018) \\
$Inv$ & 0.000 & 0.001 & -0.000 & 0.002 & -0.022 & 0.002 & 0.000 \\
 & (0.0034) & (0.0011) & (0.0008) & (0.0016) & (0.0181) & (0.0025) & (0.0004) \\
$Uct$ & 0.003$^{***}$ & 0.002$^{***}$ & 0.003$^{***}$ & 0.002$^{***}$ & 0.006$^{***}$ & 0.001$^{***}$ & 0.002$^{***}$ \\
 & (0.0004) & (0.0002) & (0.0002) & (0.0002) & (0.0011) & (0.0002) & (0.0001) \\
$Ste$ & 1.275$^{***}$ & 0.596$^{***}$ & 0.370$^{***}$ & -0.413$^{***}$ & 1.664$^{***}$ & 0.868$^{***}$ & 0.224$^{***}$ \\
 & (0.0822) & (0.0308) & (0.0430) & (0.0760) & (0.2238) & (0.0750) & (0.0149) \\
Constant & -0.054 & -0.005 & -0.001 & 0.021$^{*}$ & -0.648 & 0.042 & 0.001 \\
 & (0.0415) & (0.0110) & (0.0084) & (0.0110) & (0.4803) & (0.0429) & (0.0036) \\
\midrule
Observations & 1176 & 1120 & 1148 & 462 & 238 & 728 & 2968 \\
Adjusted $R^2$ & 0.921 & 0.968 & 0.976 & 0.984 & 0.813 & 0.952 & 0.945 \\
F Statistic & 94.479 & 137.153 & 142.726 & 34.609 & 34.057 & 65.736 & 225.031 \\
\bottomrule
\end{tabular}
\end{center}
\end{table}
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\begin{table}[htbp]
\caption{Heterogeneity Analysis  by Urban Competition Level}
\label{table:11}
\centering
\footnotesize
\begin{tabular}{l c c c c c c}
\toprule
& \multicolumn{6}{c}{\textbf{Dependent Variable: NQPF}} \\
\cmidrule(lr){2-7}
& \multicolumn{2}{c}{\textbf{Technological innovation}} & \multicolumn{2}{c}{\textbf{Resource development}} & \multicolumn{2}{c}{\textbf{Business environment}} \\
\cmidrule(lr){2-3} \cmidrule(lr){4-5} \cmidrule(lr){6-7}
& (1) & (2) & (3) & (4) & (5) & (6) \\
& High & Low & Developing & Mature & Excellent & Poor \\
\midrule
Local Gov. Competition ($Gov$) & 0.455$^{***}$ & 0.271$^{***}$ & 0.331$^{***}$ & 0.288$^{***}$ & 0.313$^{***}$ & 0.309$^{***}$ \\
& (0.0330) & (0.0220) & (0.0220) & (0.0158) & (0.0265) & (0.0139) \\
Control Variables & Yes & Yes & Yes & Yes & Yes & Yes \\
Time Fixed Effects & Yes & Yes & Yes & Yes & Yes & Yes \\
Region Fixed Effects & Yes & Yes & Yes & Yes & Yes & Yes \\
Constant & 0.001 & -0.006 & -0.008 & 0.007 & -0.019 & 0.008$^{*}$ \\
& (0.0139) & (0.0144) & (0.0135) & (0.0057) & (0.0210) & (0.0046) \\
\midrule
Observations & 1597 & 2039 & 2647 & 1001 & 1858 & 1790 \\
Adjusted $R^2$ & 0.944 & 0.944 & 0.942 & 0.927 & 0.942 & 0.954 \\
F Statistic & 93.63 & 160.43 & 211.61 & 86.09& 185.59 & 135.54 \\
\bottomrule
\end{tabular}
\end{table}




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\begin{figure}[] \label{Fig1}
    \begin{center}   
    \includegraphics[width=0.8\textwidth]{Fig1.png}
    \caption{Evolution of New Quality Productive Forces and Local Government Competition in China (2009-2022)}
    \end{center}
    \small
    \textbf{Note:} Spatiotemporal analysis reveals three evolutionary phases with characteristic coupling dynamics. During the \textit{Weak Decoupling Period (2009–2014)}, government competition intensity (avg. growth 2.7\%) consistently exceeded NQPF development (1.1\%), widening the gap from 0.06 to 0.10, reflecting institutional competition's limited efficacy in stimulating innovation under factor-driven growth paradigms. The \textit{Recoupling Transition (2015–2019)} witnessed accelerated NQPF growth (4.3\% avg.) alongside moderated competition (1.8\%, index 0.16$\rightarrow$0.17), narrowing the differential to 0.11 and marking the incipient transformation from competition dividends to innovation dividends—evidenced by NQPF's trajectory slope surpassing competition post-2017. In the \textit{Synergistic Intensification Phase (2020–2022)}, NQPF surged (8.4\% avg. growth to 0.069) with steady competition growth (3.5\% to 0.195), while the elasticity coefficient $\varepsilon_{N|G}$ rose from 0.41 to 2.40, confirming the strategic pivot from policy tournaments to innovation ecosystem cultivation.

\end{figure}



%figure 2 机制路线图



\begin{landscape}
\begin{figure}[] \label{figure2}
   \centering
    \includegraphics[width=1.2\textwidth]{Fig2.png}
   \caption{The mechanism of the role of innovation-driven local government competition in influencing the development of NQPF.}
\end{figure}

\end{landscape}

\begin{figure}[] \label{Fig3}
    \begin{center}   
    \includegraphics[width=0.8\textwidth]{Fig3.png}
    \caption{Spatial Distribution of New Quality Productive Forces ($NQPF$) across Chinese Provinces, 2022}
    \label{Fig3}
    \end{center}
    \small
    %\textbf{Note:} China's provincial NQPF exhibits marked spatial heterogeneity. Distribution follows a core \textbf{``east-high, west-low"} pattern demarcated by the \textit{Hu Huanyong Line} (Heihe-Tengchong Line): (1) Eastern coastal provinces form high-value clusters (\textgreater0.096 in Jiangsu, Guangdong, Zhejiang; 0.052-0.096 in Shanghai, Shandong); (2) Central provinces (e.g., Henan, Hunan) comprise the median transition zone (0.036-0.052); (3) Western provinces (Qinghai, Gansu) and border regions fall within the lowest quantile (\textless0.036). Notably, the southeastern coastal belt south of the \textit{Qinling-Huaihe Line} demonstrates intensified innovation momentum, revealing a superimposed north-south gradient atop the east-west disparity.
\end{figure}


\begin{figure}[] \label{Fig4}
    \begin{center}
    \includegraphics[width=0.8\textwidth]{Fig4.png}
    \caption{Spatial Configuration of Local Government Competition Intensity in China}
    \label{Fig4}
    \end{center}
    \small
    %\textbf{Note:} Local government competition exhibits a decaying core-periphery structure, characterized by three distinct spatial tiers. High-competition polarization zones (indices $\geq$0.12) concentrate primarily in the Yangtze River Delta (Shanghai-Jiangsu-Zhejiang) and Pearl River Delta (Guangdong), with peak values of 0.12–0.20 observed in Jiangsu, Shanghai, and northern Zhejiang. Secondary competition zones (0.08–0.12) encompass the Bohai Rim (Shandong-Hebei), West Coast Economic Zone (Fujian), and central inland hubs (Hubei-Chongqing), where Chongqing emerges as the sole high-value inland node. Low-competition depressions ($<$0.08) dominate northwestern (Qinghai-Gansu-Ningxia), southwestern (Yunnan-Guizhou), and northeastern regions (Heilongjiang-Jilin), demonstrating pronounced fracture along the Hu Huanyong Line—approximately 83\% of national competition intensity concentrates southeast of this demarcation (color-grade area-weighted estimate).
\end{figure}


\begin{figure}[] \label{Fig5}
    \begin{center}
    \includegraphics[width=0.8\textwidth]{parallel trend1.png}
    \caption{Parallel trend}
    \label{Fig5}
    \end{center}
    \small
\end{figure}





		

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