DOI: 10.5281/zenodo.15107113
title: Professional Colonialism: Notarial Monopolies as Instruments of Institutional Capture in Postcolonial Territories

description: 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.