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

数据集说明：中国城市更新政策扩散（2007-2023年）
1. 基本信息
1.1 数据集名称
《中国282个地级市城市更新政策扩散数据集（2007-2023年）》
1.2 时空范围
时间：2007-2023年，共计17年，覆盖政策探索至全国推广的完整阶段；
空间：覆盖31个省、自治区、直辖市（不含香港、澳门、台湾地区）的282个地级市，涵盖东部、中部、西部及东北地区。
1.3 数据规模
共计4295条“城市-年份”观测值，缺失值极少，采用线性插值法进行补充。
2. 核心内容
本数据集基于“压力-收益-行动者（Pressure-Benefit-Actor, PBA）”分析框架，包含三类指标：
2.1 因变量
名称：城市更新政策采纳状态
定义：虚拟变量（1=已出台市级层面政策或设立专门工作机构；0=未出台相关政策或仅存在区县级试点）
来源：北大法宝（PKULAW）、政府官方网站（经人工筛选确认相关性）
2.2 自变量
2.2.1 压力逻辑
上级压力：虚拟变量（1=上一年度省级层面出台相关政策；滞后1期）
同伴压力：同省份内上一年度已采纳政策的城市占比（滞后1期）
社会压力：上一年度“城市更新”相关新闻报道的数量（滞后1期）
2.2.2 收益逻辑
城镇化率：城镇常住人口/常住总人口（滞后1期）
人口密度：注册人口与行政面积比值的对数值（滞后1期）
财政状况：一般公共预算收入与支出的比值（滞后1期）
2.2.3 行动者逻辑
市委书记年龄：市委书记的年龄（当期数据）
市委书记更替：观测期内市委书记的更换次数
晋升激励：基于经济指标与群体均值对比所得的得分（0-3分，滞后1期）
市委书记工作经历：虚拟变量（1=具备城市建设相关工作经历；当期数据）
2.3 控制变量
人均GDP与第三产业占比（两类指标均滞后1期）
3. 数据来源
政策数据：北大法宝（PKULAW）、政府政策平台
社会经济数据：《中国城市统计年鉴》、地方统计公报
媒体数据：中国知网（CNKI）报纸数据库
干部数据：地方领导干部数据库、官方领导履历
4. 数据处理方法
数据清洗：剔除无关文本，修正异常值
变量处理：对偏态分布变量实施对数变换；为保障因果有效性，对变量进行滞后处理
质量控制：方差膨胀因子（VIF）<10，无多重共线性问题；政策数据经双重核验；干部数据与官方履历交叉复核