Nou Pa Bèt: Civic Substitution and Expressive Freedoms in Post-State Governance

Here's a clear Zenodo description for your dataset: Dataset Description This dataset supports the research paper "Nou Pa Bèt: Civic Substitution and Expressive Freedoms in Post-State Governance" which examines how civic participation functions as institutional substitution in fragile states, with Haiti as the primary case study. The dataset combines governance indicators from the World Bank's Worldwide Governance Indicators (WGI) with civic engagement measures from the Varieties of Democracy (V-Dem) project. Files Included: wgidataset.xlsx (2.57 MB) - Complete World Bank Worldwide Governance Indicators dataset covering multiple governance dimensions across countries and years CivicEngagement_SelectedCountries_Last10Years.xlsx (25.03 KB) - Processed V-Dem civic engagement indicators for fragile states sample (2015-2024) including variables for participatory governance, civil society participation, freedom of expression, freedom of assembly, anti-system movements, and direct democracy civic.ipynb (10.35 KB) - Complete Python analysis notebook containing all data processing, regression analysis, and visualization code used in the study How to Use in Google Colab: Step 1: Upload Files python from google.colab import files import pandas as pd import numpy as np # Upload the files to your Colab environment uploaded = files.upload() # Select and upload: CivicEngagement_SelectedCountries_Last10Years.xlsx and wgidataset.xlsx Step 2: Load the Datasets python # Load the civic engagement data (main analysis dataset) civic_data = pd.read_excel('CivicEngagement_SelectedCountries_Last10Years.xlsx') # Load the WGI data (if needed for extended analysis) wgi_data = pd.read_excel('wgidataset.xlsx') # Display basic information print("Civic Engagement Dataset Shape:", civic_data.shape) print("\nColumns:", civic_data.columns.tolist()) print("\nFirst few rows:") civic_data.head() Step 3: Run the Analysis Notebook python # Download and run the complete analysis notebook !wget https://zenodo.org/record/[RECORD_ID]/files/civic.ipynb # Then open civic.ipynb in Colab or copy/paste the code cells Key Variables: Dependent Variables (WGI): Control_of_Corruption - Extent to which public power is exercised for private gain Government_Effectiveness - Quality of public services and policy implementation Independent Variables (V-Dem): v2x_partip - Participatory Component Index v2x_cspart - Civil Society Participation Index v2cademmob - Freedom of Peaceful Assembly v2cafres - Freedom of Expression v2csantimv - Anti-System Movements v2xdd_dd - Direct Popular Vote Index Sample Countries: 21 fragile states including Haiti, Sierra Leone, Liberia, DRC, CAR, Guinea-Bissau, Chad, Niger, Burundi, Yemen, South Sudan, Mozambique, Sudan, Eritrea, Somalia, Mali, Afghanistan, Papua New Guinea, Togo, Cambodia, and Timor-Leste. Quick Start Analysis: python # Install required packages !pip install statsmodels scipy # Basic regression replication import statsmodels.api as sm from statsmodels.stats.outliers_influence import variance_inflation_factor # Prepare variables for regression X = civic_data[['v2x_partip', 'v2x_cspart', 'v2cademmob', 'v2cafres', 'v2csantimv', 'v2xdd_dd']].dropna() y_corruption = civic_data['Control_of_Corruption'].dropna() y_effectiveness = civic_data['Government_Effectiveness'].dropna() # Run regression (example for Control of Corruption) X_const = sm.add_constant(X) model = sm.OLS(y_corruption, X_const).fit(cov_type='HC3') print(model.summary()) Citation: Brown, Scott M., Fils-Aime, Jempsy, & LaTortue, Paul. (2025). Nou Pa Bèt: Civic Substitution and Expressive Freedoms in Post-State Governance [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.15058161 License: Creative Commons Attribution 4.0 International (CC BY 4.0) Contact: For questions about data usage or methodology, please contact the corresponding author through the institutional affiliations provided in the paper. This description provides clear, step-by-step instructions for researchers to immediately begin working with your data in Google Colab while explaining the theoretical and methodological context.