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

Dataset Description This dataset supports the research article "Nou Pa Bèt: Civic Substitution and Expressive Freedoms in Post-State Governance" published in Third World Quarterly. The study examines how civic participation functions as institutional substitution when state capacity collapses, using Haiti and 20 other fragile states as case studies. Research Question: Can civic engagement compensate for governance failures in post-state contexts? Key Finding: Civil society participation significantly predicts better governance outcomes even when state institutions are weak, with strongest effects in contexts of high expressive freedoms. Files Included CivicEngagement_SelectedCountries_Last10Years.xlsx (25 KB) V-Dem civic engagement indicators for 21 fragile states (2015-2024) Variables: participatory governance, civil society participation, freedom of expression/assembly, anti-system movements, direct democracy wgidataset.xlsx (2.6 MB) World Bank Worldwide Governance Indicators (WGI) Dependent variables: Control of Corruption, Government Effectiveness Nou.ipynb (10 KB) Complete Python analysis code Replicates all regressions and tables in the published article Quick Start: Google Colab Step 1: Open in Colab # Click this link to open a new Colab notebook: # https://colab.research.google.com/ Step 2: Upload Data Files from google.colab import files uploaded = files.upload() # Select and upload both .xlsx files from this Zenodo record Step 3: Install Required Packages !pip install openpyxl statsmodels scipy Step 4: Run the Analysis import pandas as pd import statsmodels.api as sm # Load data civic = pd.read_excel("CivicEngagement_SelectedCountries_Last10Years.xlsx") wgi = pd.read_excel("wgidataset.xlsx") # Merge datasets # [See Nou.ipynb for complete code] # Run regression (Table 2 in article) X = df[['v2x_partip', 'v2x_cspart', 'v2cademmob', 'v2cafres', 'v2csantimv', 'v2xdd_dd']] X = sm.add_constant(X) y = df['Control_of_Corruption'] model = sm.OLS(y, X).fit(cov_type='HC3') print(model.summary()) OR: Download Nou.ipynb and run all cells to replicate complete analysis. Variables Dependent Variables (World Bank WGI) Control_of_Corruption - Extent to which public power is NOT exercised for private gain Government_Effectiveness - Quality of public services and policy implementation Independent Variables (V-Dem Project) v2x_partip - Participatory Component Index (0-1) v2x_cspart - Civil Society Participation Index (0-1) v2cademmob - Freedom of Peaceful Assembly (0-4) v2cafres - Freedom of Expression (0-4) v2csantimv - Anti-System Movements (0-4, lower = more movements) v2xdd_dd - Direct Popular Vote Index (0-1) Sample Countries (N=21 Fragile States) Haiti, Sierra Leone, Liberia, DRC, Central African Republic, Guinea-Bissau, Chad, Niger, Burundi, Yemen, South Sudan, Mozambique, Sudan, Eritrea, Somalia, Mali, Afghanistan, Papua New Guinea, Togo, Cambodia, Timor-Leste Time Period: 2015-2024 (last 10 years of available data) Key Results (Replicate Table 2) Control of Corruption Model: R² = 0.700 Civil Society Participation (v2x_cspart): Non-significant substitution effect Freedom of Expression (v2cafres): Positive, significant (p = 0.028) Anti-System Movements (v2csantimv): Negative, significant (p < 0.001) Government Effectiveness Model: R² = 0.672 Similar patterns with expressive freedoms as key enablers Interpretation: Civic substitution works best when expressive freedoms allow civil society to organize and communicate freely. Citation Dataset: Brown, S., Fils-Aime, J., & La Tortue, P. (2025). Nou Pa Bèt: Civic Substitution and Expressive Freedoms in Post-State Governance [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.17588730 Article: Brown, S. M. (2025). Nou Pa Bèt: Civic Substitution and Expressive Freedoms in Post-State Governance. Third World Quarterly, [forthcoming]. DOI: [will be added upon publication] Data Sources V-Dem Data: Varieties of Democracy Project, Version 14 (2024). https://www.v-dem.net/ WGI Data: World Bank Worldwide Governance Indicators (2024). https://www.worldbank.org/governance/wgi/ Replication Notes Robust Standard Errors: All regressions use HC3 heteroscedasticity-consistent standard errors Missing Data: Observations with missing values on any variable are dropped (listwise deletion) No Standardization: Variables are used in original scales (raw, not z-scored) Sample Size: N=288 country-year observations after merging and cleaning Technical Requirements Python 3.7+ Required packages: pandas, numpy, statsmodels, openpyxl, scipy Recommended environment: Google Colab (free, no installation required) License Creative Commons Attribution 4.0 International (CC BY 4.0) You are free to: Share — copy and redistribute the material Adapt — remix, transform, and build upon the material Under the following terms: Attribution — You must give appropriate credit and link to the license Contact For questions about data, methodology, or replication: Scott M. Brown - Professor of Finance, University of Puerto Rico–Río Piedras Email: [e-mail] For questions about Haitian context or fieldwork: Jempsy Fils-Aime - Data collection and local context Paul La Tortue - Editorial and translation support Acknowledgments This research was conducted with support from the University of Puerto Rico–Río Piedras and Universidad de Granada. We thank the V-Dem Institute and World Bank for making their data publicly available. Last Updated: November 12, 2025Version: v4 (corrected regression specifications)