DOI: 10.5281/zenodo.16236015
title: The Denial of Governance Failure in High-Trust Democracies

description: How to Run the Analysis in Google Colab

This dataset and code package is designed for execution in Google Colab, which provides a free cloud-based Python environment.Follow these steps to reproduce the results.



1. Open Google Colab





Visit https://colab.research.google.com




Sign in with your Google account.





2. Access the Notebooks





This repository contains two analysis notebooks:





Polis.ipynb




cpi.ipynb






Download them from Zenodo, or open them directly in Colab using File → Upload notebook.





3. Mount Google Drive (Optional but Recommended)

Mounting Google Drive allows you to store the data permanently instead of uploading it each time.



python





CopiarEditar



from google.colab import drive
drive.mount('/content/drive')



After mounting, place all dataset files in a folder inside your Drive (e.g., My Drive/CorruptionStudy/).



4. Required Dataset Files

Ensure the following files are available in your Colab session (either uploaded directly or stored in Drive):








File
Description




estat_sdg_16_50_en.csv
Eurostat CPI dataset


V-Dem-CY-Core-v15.csv
V-Dem Core dataset


Controls.xlsx
Control variables


Institutional.xlsx
Institutional variables


Core.xlsx
Additional core variables






 






5. Upload Files (If Not Using Drive)

If you are not using Google Drive, upload all files at the start of your session:



python





CopiarEditar



from google.colab import files
uploaded = files.upload()



Select all required .csv and .xlsx files when prompted.



6. Install Required Python Packages

Run the following command in a Colab cell:



python





CopiarEditar



!pip install pandas numpy statsmodels linearmodels openpyxl





7. Update File Paths in the Notebook

If files are uploaded directly in Colab:



python





CopiarEditar



EUROSTAT_CPI_PATH = "/content/estat_sdg_16_50_en.csv"
VDEM_PATH         = "/content/V-Dem-CY-Core-v15.csv"
CONTROLS_PATH     = "/content/Controls.xlsx"
INSTITUTIONAL_PATH= "/content/Institutional.xlsx"
CORE_PATH         = "/content/Core.xlsx"



If files are stored in Google Drive:



python





CopiarEditar



EUROSTAT_CPI_PATH = "/content/drive/My Drive/CorruptionStudy/estat_sdg_16_50_en.csv"
VDEM_PATH         = "/content/drive/My Drive/CorruptionStudy/V-Dem-CY-Core-v15.csv"





8. Run the Notebook





Execute all cells in order (Runtime → Run all).




The notebook will:





Load CPI and V-Dem data




Merge with control variables




Standardize variables




Estimate two-way fixed effects (Driscoll–Kraay standard errors)




Output model summaries







9. Save Results

To save results to Google Drive:



python





CopiarEditar



df.to_excel("/content/drive/My Drive/CorruptionStudy/results.xlsx")



To download directly:



python





CopiarEditar



from google.colab import files
files.download("results.xlsx")





10. Citation

If using this dataset or code, please cite the Zenodo record as indicated in the Cite As section.

Zenodo Dataset Description:

Title: Epistemic Legitimacy Traps in High-Trust Democracies: Replication Data and Code

Description:

This dataset contains replication materials for "Epistemic Legitimacy Traps: How High-Trust Institutions Silence Inconvenient Truths" - a study examining how friendship-based corruption persists in democratic institutions through systematic exclusion of internal critics.

Contents:



Panel Analysis Data (481 country-year observations, 37 European countries, 2010-2022):



V-Dem Democracy Dataset (v15) corruption measures

Eurostat Corruption Perceptions Index data

Merged and cleaned dataset for two-way fixed effects analysis



Individual-Level Survey Data (66,054 observations):



Controls, Core, and Institutional survey modules

Variables measuring friendship reciprocity norms, institutional trust, and socioeconomic outcomes

Cleaned dataset for OLS regression analysis



Replication Code:



Python scripts for panel data analysis with Driscoll-Kraay standard errors

Stata/Python code for individual-level OLS regressions with robust and clustered standard errors

Data cleaning and variable construction procedures




Key Variables:



Corruption Perceptions Index (Eurostat)

V-Dem corruption measures (executive, public sector, composite)

Institutional quality indicators (judicial independence, civil society participation)

Individual friendship reciprocity and trust measures

Sociodemographic controls


Methodology: Two-way fixed effects panel regression (institutional analysis) and OLS with robust standard errors (individual analysis) testing the relationship between corruption measures, institutional quality, and public perceptions in high-trust democratic contexts.

Research Questions: How do high-trust institutions maintain legitimacy while systematically excluding internal criticism? What role do friendship networks play in enabling "clean corruption" that operates through relationships rather than material exchanges?

Keywords: corruption, epistemic injustice, institutional legitimacy, democracy, trust, whistleblowing, friendship networks, panel data

Citation: [Author], [Year]. "Epistemic Legitimacy Traps: How High-Trust Institutions Silence Inconvenient Truths." Business Ethics Quarterly [forthcoming].

Data Sources: V-Dem Institute, Eurostat, [Original Survey Data Source]

License: Creative Commons Attribution 4.0 International