Professional Colonialism: Notarial Monopolies as Instruments of Institutional Capture in Postcolonial Territories

Contents Core Data Files: Civil_Codes_Cadastre_GDP.xlsx - Cross-national data on legal regime types, civil code adoption dates, cadastral system establishment, and economic indicators for 36 countries Corruption_Perception_Index.csv - Transparency International CPI scores by country and year IPRI_Country_Tables_Manual.xlsx - International Property Rights Index data with manual corrections and regime classifications vdem_variables_filtered_1996_onward.xlsx - Varieties of Democracy indicators for executive corruption and rule of law (1996-present) wgidataset.xlsx - World Bank Worldwide Governance Indicators including Control of Corruption and Regulatory Quality Analysis Code: Corruption.ipynb - Complete Jupyter notebook containing all statistical analyses, data cleaning, non-parametric tests, and visualization code How to Use This Dataset 1. Replication of Main Results Run Corruption.ipynb to reproduce all statistical analyses: Kruskal-Wallis tests comparing governance across legal regime types Mann-Whitney U tests for pairwise regime comparisons Descriptive statistics and effect size calculations Data visualization and diagnostic plots 2. Working with Individual Data Files Legal Regime Classification (Civil_Codes_Cadastre_GDP.xlsx): Column Legal_Regime: Three categories (notarial, administrative, hybrid) Use for country classification and regime-based analysis Includes GDP per capita for control variables Governance Indicators: CPI_score: Higher = less corrupt (0-100 scale) WGI_cc: World Bank Control of Corruption (-2.5 to +2.5 scale) vdem_exec_corr: V-Dem Executive Corruption (0-1 scale, higher = more corrupt) IPRI_score: Property Rights Protection (0-10 scale, higher = better) 3. Statistical Approach The dataset uses non-parametric methods due to non-normal distributions: Primary tests: Kruskal-Wallis H and Mann-Whitney U Effect sizes: Rank-biserial correlations Sample: 36 countries (29 notarial, 6 administrative, 1 hybrid) 4. Key Variables for Analysis # Main outcome variables outcomes = ['CPI_score', 'WGI_cc', 'vdem_exec_corr', 'IPRI_score'] # Grouping variable regime = 'Legal_Regime' # Categories: notarial, administrative, hybrid # Control variables controls = ['GDP_per_Capita', 'Civil_Code_Year', 'Cadastral_System_Year'] 5. Expected Results The analysis should demonstrate: Notarial regimes score significantly lower on all governance indicators Large effect sizes (rank-biserial r > 0.8) indicating practical significance Consistent directional patterns across multiple governance measures Technical Requirements Python 3.7+ with pandas, numpy, scipy, matplotlib, seaborn Jupyter notebook environment Excel reader capability (openpyxl or xlrd) Citation When using this dataset, please cite both the associated research paper and this dataset with DOI: 10.5281/zenodo.16869757 Research Applications This dataset is suitable for: Comparative institutional analysis Legal origins and governance research Postcolonial studies of institutional persistence Property rights and economic development research Methodological studies in cross-national comparison Data Quality Notes Missing data handled through listwise deletion Manual verification of legal regime classifications Temporal alignment across indicators (most recent available year) Robustness checks included in analysis code For questions about data collection methodology or analysis techniques, please refer to the accompanying research paper or contact the authors.