CnOpenData's Statistics on the Full Lifecycle of Industrial and Commercial Registered Enterprises in China represent the nation's first standardized statistical product systematically delineating the survival dynamics of enterprise cohorts. This database, based on over 350 million detailed industrial and commercial registration records, employs an innovative "fixed time point, cohort-backtracking" statistical model to construct a panel dataset spanning 25 years (2000-2024), covering all 31 provinces, all industry sectors, and all enterprise types. It not only accurately presents the survival and exit counts of enterprises across different "age groups" at any given time point but also intuitively reveals the complete dynamic process from enterprise inception to exit through the cumulative deregistration statistical approach, serving as an indispensable authoritative data infrastructure for studying China's microeconomic vitality, evaluating regional business environments, and assessing industry risks.

Key Features:

• Comprehensive Underlying Data Coverage: The dataset encompasses over 350 million industrial and commercial registration records, achieving full coverage of registered business entities across all provinces in mainland China. The data undergo rigorous alignment with the "National Economic Industry Classification (GB/T 4754-2017)" and the 2023 administrative division codes, ensuring statistical consistency with national standards and high representativeness and authority.
• Unique Statistical Logic, Targeting Core Survival Analysis: Utilizing the "accounting year-backtracking" paradigm, i.e., selecting any accounting year (time point T) as the observation point, tracing back and counting the survival status of all enterprise age cohorts at that point. For instance, it can precisely answer: "At the end of 2020, how many enterprises in the newly established cohort (0 years old), the 2018-2019 cohort (1-2 years old), the 2016-2017 cohort (3-4 years old), etc., were still operating? How many had cumulatively exited?" This design perfectly serves core research topics such as enterprise survival rates and entrepreneurial success rates.
• Rich Dimensional Framework Supporting Multi-level Granular Analysis: The database constructs six analytical dimensions: time (annual) - space (province/city) - entity (enterprise nature) - industry - capital scale - enterprise age. Users can drill down from macro trends to micro insights, e.g., analyzing the survival status of "enterprises in the information technology service industry in the Yangtze River Delta region, with registered capital between 5-10 million RMB and established for 1-3 years," meeting deep needs for academic research and business decision-making.
• Rigorous Data Governance with Deep Cleaning of Key Indicators: Special treatment was applied to the approximately 60% null rate in the "registered capital" field: null and zero values were grouped into the "0" category, preserving sample completeness. Simultaneously, all foreign currency registered capital was uniformly converted to RMB using year-end exchange rates at the time of registration, and extreme outliers beyond the 99.8th percentile were excluded, ensuring rationality and reliability for cross-temporal and spatial capital strength comparisons.

Application Value:

• Enterprise Lifecycle and Entrepreneurial Ecosystem Research: Enables precise quantification of "entrepreneurial mortality rates" and mapping of survival curves for enterprises across regions, industries, and capital backgrounds. For example, it can calculate "the cumulative survival rate of enterprises in Beijing's technology service industry with registered capital of 1-2 million RMB at the 1-3 and 3-5 year age groups," providing robust data support for entrepreneurship research and venture capital.
• Regional Business Environment and Economic Vitality Assessment: By comparing mid-term survival rates of newly established enterprises across cities and provinces, the database objectively quantifies regional business environment quality. It can generate "three-year survival rate rankings of newly established enterprises in key Chinese cities," offering critical performance evaluation and decision-making bases for local governments to optimize business environments and attract investment.
• Industry Trend Insights and Industrial Investment Risk Assessment: Reveals entry barriers and competitive intensity across industries. Industries with rapidly declining survival rates may indicate red ocean competition or policy adjustments, while those with stable survival rates and active new establishments may harbor growth opportunities. For instance, dynamic tracking of survival changes across segments of the "new energy vehicle industrial chain" provides forward-looking insights for industrial policy formulation and capital allocation.
• Quantitative Assessment of Macroeconomic Volatility and Policy Effects: Monitors the impact of macroeconomic shocks on micro-entity survival and conducts "before-after" quantitative evaluations of major policies. For example, analyzing changes in deregistration rates of small and medium-sized manufacturing enterprises entering the 1-3 year cohort in the subsequent two years after the "large-scale tax-and-fee reduction policy in 2019" enables scientific quantification of policy relief effects and micro-transmission mechanisms.

  In summary, this dataset is not merely a comprehensive statistical report but a "microscope" and "telescope" for deeply understanding China's microeconomic dynamics. It transforms massive, complex administrative records into clear, multidimensional, high-value data products directly applicable to academic research and business analysis. Its validated statistical logic, rigorous data governance, and comprehensive dimensional framework provide academia and industry with previously scarce, robust foundational data support for discerning enterprise survival patterns, evaluating business environments, and assessing economic trends.

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Time Period

1990-2024.10 (updatable upon request)

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Field Display

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Sample Data