The Denial of Governance Failure in High-Trust Democracies

Replication Data and Code This repository contains the complete replication materials for the research paper "The Denial of Governance Failure in High-Trust Democracies" by Stefan Holgersson (Linköping University) and Scott Brown (University of Puerto Rico). Abstract This research theorizes friendship-based corruption as a subtle but morally consequential form of institutional failure in high-trust democratic organizations. Using Sweden as a least-likely case, we examine how a whistleblower in the national police force was punished for exposing loyalty-driven misconduct—revealing how informal social bonds can distort governance while remaining shielded by reputational trust. Drawing on original empirical analysis of institutional corruption determinants across 37 countries over 13 years, we show how symbolic denial of corruption enables ethical erosion even in rule-of-law states. Repository Contents Data Files V-Dem-CY-Core-v15.csv - Varieties of Democracy Country-Year Core Dataset v15 (source: varieties-of-democracy.org) estat_sdg_16_50_en.csv - Eurostat SDG Indicator 16.50: Corruption Perceptions Index data (source: ec.europa.eu/eurostat) Controls.xlsx, Core.xlsx, Institutional.xlsx - Supplementary survey datasets for robustness checks Code Files cpi.ipynb - Complete Jupyter notebook containing: Data cleaning and merging procedures Panel regression analysis with two-way fixed effects Driscoll-Kraay standard error implementations Robustness checks and sensitivity analyses All tables and figures from the paper Methodology The quantitative analysis employs panel OLS regression with country and time fixed effects, using Driscoll-Kraay standard errors to address heteroskedasticity, autocorrelation, and cross-sectional dependence. The analysis examines how institutional factors (judicial independence, civil society participation, press freedom, electoral competition) relate to corruption perceptions across European democracies. Key Variables Dependent Variable: Corruption Perceptions Index (CPI) from Eurostat Independent Variables: V-Dem institutional quality measures (standardized) v2juncind - Judicial independence v2x_cspart - Civil society participation v2x_freexp_altinf - Freedom of expression and alternative information v2elmulpar - Multiparty elections v2x_execorr - Executive corruption v2x_pubcorr - Public sector corruption Sample Countries: 37 (primarily European Union + EFTA) Time Period: 13 years (balanced panel) Total Observations: 481 Software Requirements Python 3.7+ pandas numpy linearmodels openpyxl (for Excel file reading) Install requirements: pip install pandas numpy linearmodels openpyxl Replication Instructions Download all files to a local directory Open cpi.ipynb in Jupyter Notebook/Lab Update file paths in the notebook to match your local directory structure Run all cells to reproduce the complete analysis The notebook generates the exact regression results reported in Section 3.5 of the paper, including: Model M0: Composite corruption measure only Model M2a: Executive corruption with institutional controls Model M2b: Public corruption with institutional controls Data Sources Varieties of Democracy (V-Dem) Project. (2024). Country-Year Core Dataset v15. https://varieties-of-democracy.org Eurostat. (2024). SDG Indicator 16.50: Corruption Perceptions Index. https://ec.europa.eu/eurostat License This work is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0). You are free to share and adapt the material for any purpose, with appropriate attribution. Transparency Statement This research employed AI tools for code optimization and formatting assistance. All analytical decisions, theoretical frameworks, and substantive interpretations remain the authors' independent contributions. Full details are provided in the AI Usage Statement within the paper.复现数据与代码 本仓库包含Stefan Holgersson（林雪平大学）与Scott Brown（波多黎各大学）所发表的研究论文《高信任民主政体中治理失效的否认》（The Denial of Governance Failure in High-Trust Democracies）的完整复现材料。 摘要 本研究将基于友谊的腐败界定为高信任民主组织中一种隐蔽但具有道德后果的制度失效形式。本研究以瑞典作为最不可能案例，考察了国家警察部门的一名举报人因揭发忠诚导向的不当行为而遭受惩处的事件，揭示了非正式社会联结如何在声誉信任的庇护下扭曲治理效能。本研究基于对13年间37个国家的制度腐败决定因素的原创实证分析，证明了即便在法治国家，对腐败的象征性否认也会引发伦理侵蚀。 仓库内容 数据文件 V-Dem-CY-Core-v15.csv：民主多样性（Varieties of Democracy, V-Dem）项目国家年度核心数据集v15（来源：varieties-of-democracy.org） estat_sdg_16_50_en.csv：欧盟统计局SDG指标16.50：腐败感知指数数据（来源：ec.europa.eu/eurostat） Controls.xlsx、Core.xlsx、Institutional.xlsx：用于稳健性检验的补充调查数据集 代码文件 cpi.ipynb：完整Jupyter笔记本（Jupyter Notebook），包含以下内容：数据清洗与合并流程、双向固定效应面板回归分析、Driscoll-Kraay标准误实现、稳健性检验与敏感性分析、论文中所有表格与图表 研究方法 本定量分析采用包含个体与时间双向固定效应的面板普通最小二乘（OLS）回归，使用Driscoll-Kraay标准误以处理异方差、自相关与截面相关性问题。本分析聚焦于制度因素（司法独立、公民社会参与、新闻自由、选举竞争）与欧洲民主国家腐败感知之间的关联。 核心变量 因变量：欧盟统计局腐败感知指数（Corruption Perceptions Index, CPI） 自变量：民主多样性（Varieties of Democracy, V-Dem）项目标准化制度质量衡量指标： v2juncind：司法独立 v2x_cspart：公民社会参与 v2x_freexp_altinf：言论自由与替代信息渠道 v2elmulpar：多党选举 v2x_execorr：行政腐败 v2x_pubcorr：公共部门腐败 样本设置 国家：37个（主要为欧盟+欧洲自由贸易联盟成员国） 时间跨度：13年（平衡面板） 总观测值：481 软件需求 Python 3.7及以上版本 pandas numpy linearmodels openpyxl（用于读取Excel文件） 安装依赖命令： pip install pandas numpy linearmodels openpyxl 复现指南 1. 将所有文件下载至本地目录 2. 在Jupyter笔记本（Jupyter Notebook）中打开cpi.ipynb 3. 更新Notebook中的文件路径以匹配本地目录结构 4. 运行所有单元格以复现完整分析 本Notebook将生成论文第3.5节报告的精确回归结果，包括： 模型M0：仅复合腐败测度 模型M2a：加入制度控制变量的行政腐败模型 模型M2b：加入制度控制变量的公共腐败模型 数据来源 民主多样性（Varieties of Democracy, V-Dem）项目. (2024). 国家年度核心数据集v15. https://varieties-of-democracy.org 欧盟统计局. (2024). SDG指标16.50：腐败感知指数. https://ec.europa.eu/eurostat 许可协议 本作品采用知识共享署名4.0国际许可协议（Creative Commons Attribution 4.0 International, CC BY 4.0）进行许可。您可自由共享与改编本材料，需注明适当出处。 透明度声明 本研究使用了人工智能工具进行代码优化与格式辅助。所有分析决策、理论框架与实质性解读均为作者的独立贡献。详细信息可见论文中的AI使用声明。