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.