This study measures staged achievements of common prosperity and analyzes spatial distribution and driving mechanisms using panel data from 30 provincial regions in China. The authors apply the entropy weight TOPSIS method to quantify levels, use kernel density estimation to examine distributional features, and employ dynamic qualitative comparative analysis (QCA) to identify configuration pathways through which digital new quality productivity elements jointly enable balanced development. Results reported include an overall upward trend in common prosperity, evident east-to-west spatial disparities, three multi-dimensional configuration pathways that promote common prosperity, and a three-stage temporal evolution of enabling mechanisms.
The paper frames common prosperity as a global development objective aligned with narrowing wealth gaps and fostering fairness, social equity, and sustainable development. The authors situate the problem in the context of digital transformation, suggesting that structural challenges—regional imbalances, urban-rural income differences, and constrained growth momentum—undermine progress toward common prosperity. The concept of digital new quality productivity is advanced as a technology-economic paradigm that integrates digital infrastructure, digital talent, and emerging industries to reshape production inputs, processes, and value-creation mechanisms. The study aims to analyze the mechanisms and paths by which these multi-dimensional digital elements influence common prosperity and to derive policy implications to support inclusive growth in the digital era.
The literature review synthesizes three strands of international research related to the topic. First, theoretical foundations: theories such as the technological-economic paradigm and innovation-driven growth demonstrate how technological revolutions and knowledge spillovers can redefine production factors and institutional structures. Second, studies on common prosperity examine conceptual definitions, measurement approaches, and drivers, emphasizing inclusiveness and narrowing regional or social disparities. Third, prior empirical work has investigated the impacts of digital talent, the digital economy, and innovation systems on income distribution and welfare. The authors argue that existing research often treats factors in isolation or uses static methods, limiting understanding of collaborative evolution and synergistic effects; thus they propose a framework integrating multiple digital productivity elements to study their joint effects on common prosperity.
The empirical approach combines quantitative measurement and configurational analysis. To quantify provincial levels of common prosperity over phases, the study uses the entropy weight TOPSIS method. Distributional characteristics and temporal dynamics are explored via kernel density estimation. To uncover joint causal configurations among digital infrastructure, talent, industry, and other digital productivity dimensions, the authors apply dynamic qualitative comparative analysis (QCA), which assesses combinations of conditions that lead to high levels of common prosperity. Data sources, indicator composition, and the precise configuration variables are reported in the manuscript and supporting information.
Using the described measurement approach, the authors report that the level of common prosperity in China has generally trended upward over the study period. Spatial analysis indicates a clear pattern of higher levels in eastern provinces and lower levels in western provinces, with additional disparities between eastern, middle, and western regions. Kernel density estimation is used to document distributional changes over time, supporting the finding of uneven spatial development and persistent regional gaps.
Dynamic QCA uncovers that multi-dimensional elements of digital new quality productivity operate jointly to promote common prosperity rather than acting singly. The analysis identifies three configuration pathways through which digital productivity elements enable balanced development: (1) a “digital intelligence foundation - talent traction” pathway emphasizing foundational digital capacity reinforced by talent; (2) a “digital intelligence industry - talent linkage” pathway stressing industry development in tandem with human capital; and (3) a “digital intelligence development - talent collaboration” pathway focusing on coordinated development processes involving both industry and talent. The authors further describe a temporal progression of dominant pathways from foundational capacity toward industry-focused and then broader development-oriented stages.
The discussion interprets the configuration results in light of theoretical frameworks on technology-driven economic paradigms and innovation-driven growth. The authors highlight that coordinated development across digital infrastructure, talent, and industrial transformation is crucial for enabling inclusive outcomes. They note spatial heterogeneity in pathway adoption and efficacy, underscoring the need for context-sensitive policy design. The study emphasizes that single-factor interventions are unlikely to achieve systemic outcomes; composite strategies that forge linkages among digital elements and human capital are recommended.
Conclusions reiterate the upward trend in common prosperity and the pronounced spatial pattern of eastern advantage. The three identified configuration pathways and the staged evolution—moving from digital intelligence foundation to digital intelligence industry to digital intelligence development—frame policy guidance. The authors suggest differentiated, region-specific development strategies to raise digital new quality productivity and accelerate reduction of regional economic disparities. Detailed policy prescriptions and limitations are presented in the manuscript; specifics on indicator choices and robustness checks are included in the supporting information.
The manuscript reports that all relevant data and supporting materials are provided within the article and its supporting files. Readers are directed to those materials for detailed variable definitions, data sources, and supplementary analyses referenced throughout the study.
Funding sources supporting the research and author contribution roles are declared in the manuscript. The authors state that no competing interests exist.
The article cites relevant literature on technological-economic paradigms, innovation-driven growth, digital capabilities, and measures of common prosperity to situate its contribution. Full bibliographic details appear in the manuscript's reference list.