Data Factor Empowers Economic Resilience: Evidence from China
1. Introduction and Literature Review
In a world facing growing economic uncertainty and external challenges, strengthening regional economic resilience is crucial for sustainable development. Economic resilience, as defined by Martin (2012), Yang & Shao (2022), and Eichengreen et al. (2024), goes beyond maintaining stability during crises. It involves rapid recovery through self-adaptive adjustments and transforming economic structures towards more advanced forms. Traditionally, capital accumulation, labor input, and technological progress were seen as key drivers. However, the rise of the digital economy emphasizes data as a critical new factor.
Data factor, digital information processed and organized in social production, becomes a core force in reshaping global competitiveness. Its characteristics include non-rivalry, synergy, complementarity, reusability, and spillover effects. China's data production surged from 41.06 ZB in 2023 to 41.06 ZB in 2024, accounting for 26.67% of global data. Data factor's unique attributes break traditional scarcity barriers, significantly impacting regional economic development structures and operational logic (Zhang & Xu, 2024).
Studies show that data-driven digital transformation, talent agglomeration, and industrial structure optimization are key pathways to economic resilience (Pan et al., 2023; Oyadeyi et al., 2024; Sun & Zhu, 2024). High-quality human capital, advanced technologies like AI, and tax reforms incentivizing innovation further boost resilience (Sun et al., 2023; Liu et al., 2024; Zheng et al., 2024). Data factor, as a key carrier, influences economic resilience through its flow, agglomeration, and market-oriented allocation.
However, direct research on data factor and economic resilience is limited. Most studies use policy variables related to data factor to examine its impact pathways and mechanisms, focusing on urban economies, agriculture, and exports (Sitinjak et al., 2018; Demartini et al., 2019; Zheng & Chen, 2023; Zhang & Xu, 2024; Cheng et al., 2024; Luo et al., 2025; Mao et al., 2025; Shi & Yang, 2025; Wang, 2025). These studies collectively show that data factor profoundly influences regional economic resilience through marketization, clustering, and infrastructure development.
This paper systematically examines the impact and underlying mechanisms of data factor on economic resilience using Chinese provincial panel data. It finds that data factor significantly empowers economic resilience, exhibiting distinct spatial gradient characteristics. Regions with stronger data factor foundations but lower economic resilience show a smaller impact effect.
The paper's contributions are threefold:
- It analyzes direct and indirect pathways of data factor empowering economic resilience, expanding the theoretical framework for economic resilience in the digital economy era.
- It reveals heterogeneous characteristics and multi-channel mechanisms of data factor affecting economic resilience, offering policy implications for coordinated regional development and tailored digital strategies.
- It constructs a multidimensional evaluation system for scientifically measuring data factor development.
2. Theoretical Analysis and Research Hypotheses
Data factor empowers economic resilience through direct and indirect pathways:
2.1. Direct Pathways
Data factor directly impacts regional economic resilience through various institutional arrangements and practical pathways:
- Information Transparency and Decision-Making: Abundant data factor enhances information transparency and decision-making scientificity, enabling enterprises to adapt to economic shocks and governments to formulate targeted policies.
- Market-Oriented Allocation: Data factor allocation through data trading platforms strengthens economic resistance, recovery, adaptability, and innovation capabilities, reducing information asymmetry and optimizing production factor flow efficiency.
- Agglomeration Effect: Establishing comprehensive big data pilot zones fosters data factor agglomeration, promoting technological innovation and industrial upgrading, and boosting regional economic resilience.
- Market Construction: Constructing the data factor market provides a fundamental guarantee for data value release, establishing data property rights, pricing, and trading rules, and driving economic resilience enhancement.
2.2. Indirect Pathways
Data factor empowers economic resilience through:
- Innovation-Driven Effect: Agglomeration of data factor stimulates R&D investment, guides technological breakthroughs, and enhances innovation scientificity and efficiency, ultimately strengthening regional resilience.
- Digital Finance: Data factor drives digital finance development, transforming data into tradable assets, improving credit assessment, capital allocation efficiency, and financial inclusiveness, enhancing resilience.
- Industrial Structure Optimization: Data factor freely flows and integrates across industries, promoting cross-industry integration, emerging business forms, and digital transformation, improving production efficiency and resilience.
3. Research Design
3.1. Model Construction
The paper constructs a benchmark regression model (Equation 1) to empirically examine data factor's impact on economic resilience, controlling for variables like industrialization level, urban economic density, financial development, urbanization, and fiscal decentralization.
3.2. Variable Explanation
- Economic Resilience (RES): Utilizes an index constructed by Huang and Zhang (2025) encompassing risk resistance, recovery, adaptive adjustment, and innovation transformation.
- Data Factor (DAT): A multidimensional evaluation system (Table 1) based on foundational support, application level, and transformation efficiency, measured using the entropy-weighted TOPSIS method.
4. Empirical Analysis
The analysis confirms the positive impact of data factor on economic resilience, with significant regional and development stage differences.
4.1. Benchmark Regression Results
Two-way fixed-effects models show a significant positive coefficient for data factor, with robustness tests further validating the core conclusion.
4.2. Endogeneity and Robustness Tests
Various tests ensure reliability, including instrumental variables, lagged terms, bootstrapping, and alternative measurements.
4.3. Regional Heterogeneity Analysis
The impact of data factor on economic resilience varies by region and development stage, with distinct spatial gradients and 'catching up' effects in less developed regions.
4.4. Mechanism Test
Data factor empowers economic resilience through innovation-driven development, digital finance, and industrial structure optimization, forming a multi-dimensional transmission mechanism.
5. Conclusion and Policy Recommendations
The paper concludes that data factor significantly enhances economic resilience, with regional and development stage differences. It proposes policy recommendations:
- Accelerate data factor infrastructure construction for a solid foundation.
- Implement differentiated regional strategies to address spatial heterogeneity.
- Focus on key transmission channels and establish a collaborative promotion system for data factor, mechanism, and resilience.
