The Diffusion Mechanism of China’s Urban Renewal Policy:An Event History Analysis Based on the Pressure-Benefit-Actor Framework

Dataset Description: Diffusion of China's Urban Renewal Policies (2007-2023)
1. Basic Information
1.1 Name
Dataset on the Diffusion of Urban Renewal Policies Across 282 Prefecture-Level Cities in China (2007-2023)
1.2 Spatiotemporal Scope
Time: 2007-2023 (17 years, covering policy exploration to national promotion)
Space: 282 prefecture-level cities in 31 provinces/autonomous regions/municipalities (excluding HK, Macao, Taiwan), covering eastern, central, western, and northeastern regions.
1.3 Scale
4,295 "city-year" observations, with minimal missing values (supplemented by linear interpolation).
2. Core Content
Based on the "Pressure-Benefit-Actor" (PBA) framework, the dataset includes three categories of indicators:
2.1 Dependent Variable
Name: Urban Renewal Policy Adoption Status
Definition: Dummy variable (1 = Issued municipal-level policy/established special institutions; 0 = No policy or only district/county pilots)
Source: PKULAW, government official websites (manually screened for relevance)
2.2 Independent Variables
2.2.1 Pressure Logic
Superior Pressure: Dummy variable (1 = Provincial policy issued in previous year; lagged 1 period)
Peer Pressure: Ratio of policy-adopting cities in the same province (previous year; lagged 1 period)
Social Pressure: Number of "Urban Renewal" news reports (previous year; lagged 1 period)
2.2.2 Benefit Logic
Urbanization Rate: Urban population/permanent residents (lagged 1 period)
Population Density: Logarithm of registered population/administrative area (lagged 1 period)
Fiscal Condition: General public budget revenue/expenditure (lagged 1 period)
2.2.3 Actor Logic
Chief’s Age: Age of Municipal Party Committee Secretary (current-year data)
Chief Turnover: Number of Municipal Party Committee Secretaries during observation
Promotion Incentive: Score (0-3) based on economic indicators vs. group average (lagged 1 period)
Chief’s Work Experience: Dummy variable (1 = Urban construction-related experience; current-year data)
2.3 Control Variables
Per capita GDP and tertiary industry share (both lagged 1 period)
3. Data Sources
Policy Data: PKULAW, government policy platforms
Socio-Economic Data: China City Statistical Yearbook, local statistical bulletins
Media Data: CNKI Newspaper Database
Cadre Data: Local Leading Cadre Database, official leadership profiles
4. Processing Methods
Cleaning: Excluded irrelevant texts, corrected outliers
Variable Handling: Logarithmic transformation for skewed variables; lagged processing for causal validity
Quality Control: VIF < 10 (no multicollinearity); double verification for policy data; cadre data cross-checked with official resumes