Language, Law, and the Limits of Global Scalability: Institutional Legacies from Shakespeare to Bad Bunny

Dataset Contents Analysis Files: LATAM+OECD.ipynb - Main analysis notebook comparing regulatory effects across regions OECD.ipynb - OECD-specific analysis cervantes.ipynb - Historical case study analysis Data Files: culture_exports.csv - Cultural exports data from UNESCO explanatory.csv - Regulatory and institutional variables GDPPC.csv - GDP per capita data from World Bank p5v2018.csv - Polity V democracy scores How to Use in Google Colab Step 1: Download Dataset python # Install required packages !pip install pandas numpy statsmodels matplotlib seaborn # Download files directly from Zenodo import urllib.request import pandas as pd # Download main datasets urllib.request.urlretrieve('https://zenodo.org/record/16851638/files/culture_exports.csv', 'culture_exports.csv') urllib.request.urlretrieve('https://zenodo.org/record/16851638/files/explanatory.csv', 'explanatory.csv') urllib.request.urlretrieve('https://zenodo.org/record/16851638/files/GDPPC.csv', 'GDPPC.csv') urllib.request.urlretrieve('https://zenodo.org/record/16851638/files/p5v2018.csv', 'p5v2018.csv') Step 2: Load and Explore Data python # Load main datasets culture_exports = pd.read_csv('culture_exports.csv') explanatory = pd.read_csv('explanatory.csv') gdp_data = pd.read_csv('GDPPC.csv') polity_data = pd.read_csv('p5v2018.csv') # Display data structure print("Culture exports shape:", culture_exports.shape) print("Explanatory variables shape:", explanatory.shape) Step 3: Run Analysis Notebooks python # Clone notebook from Zenodo and run urllib.request.urlretrieve('https://zenodo.org/record/16851638/files/LATAM+OECD.ipynb', 'LATAM_OECD.ipynb') # Open the notebook in Colab from google.colab import files files.view('LATAM_OECD.ipynb') Key Variables Dependent Variable: log_cultural_exports - Log cultural exports per capita Independent Variables: xconst - Executive constraints (legal flexibility proxy) trade_openness - Trade as % of GDP (administrative burden proxy) internet_penetration - Internet users % (market access proxy) log_gdp_pc - Log GDP per capita education - Education spending % of GDP Replication Instructions OECD Analysis: Use OECD.ipynb for developed country sample Latin America Analysis: Filter data for: ARG, BOL, BRA, CHL, COL, ECU, MEX, PER, PRY, VEN Regional Comparison: Use LATAM+OECD.ipynb for full comparative analysis Sample Analysis Code python # Basic regression setup import statsmodels.formula.api as smf # Latin America fixed effects model latam_data = merged_data[merged_data['iso3c'].isin(['ARG', 'BOL', 'BRA', 'CHL', 'COL', 'ECU', 'MEX', 'PER', 'PRY', 'VEN'])] model = smf.ols('log_cultural_exports ~ xconst + log_gdp_pc + trade_openness + internet_penetration + education + C(year) + C(iso3c)', data=latam_data).fit(cov_type='cluster', cov_kwds={'groups': latam_data['iso3c']}) print(model.summary()) Citation When using this dataset, please cite: Brown, S. M. & Hall, D. J. (2025). Language, Law, and the Limits of Global Scalability: Institutional Legacies from Shakespeare to Bad Bunny [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.16851638 Technical Requirements Python 3.7+ pandas, numpy, statsmodels, matplotlib, seaborn Google Colab or Jupyter notebook environment Support For questions about data usage or replication, please refer to the analysis notebooks or contact the authors through the associated journal publication.