Legal-Linguistic Path Dependence and the Scalability of Cultural Industries: From Elizabethan Theater to Global IP Regimes

Title:Legal-Linguistic Path Dependence and the Scalability of Cultural Industries: From Elizabethan Theater to Global IP Regimes Creator:Anonymous DOI:10.5281/zenodo.15115958 Version:v1 — Published March 31, 2025 License:Creative Commons Attribution 4.0 International (CC BY 4.0) Description:This dataset accompanies the research study investigating how legal origins and language regimes co-evolve to shape the institutional scalability of cultural industries. Through a comparative historical lens focused on Elizabethan England and Habsburg Spain, the dataset supports the claim that English-based common law systems are more conducive to global IP regime formation than Spanish-based civil law systems. The dataset integrates: The 2024 EF English Proficiency Index (EF EPI), 2023 GDP data by country, 2024 International Property Rights Index (IPRI), and UNESCO statistics on film production language. These sources have been harmonized for cross-country comparative analysis, including the construction of a “Common Law Dummy” and a merged panel file for empirical testing of the legal-linguistic synergy hypothesis. Files Included: EF_EPI_2024_with_Legal_Origin_Common_Law_Dummy.xlsx GDP_2023.xlsx (corrected column header: "Country") IPRI_Country_Tables_Manual.xlsx UNESCO Language of film production - Langue de production des films.xlsx 🛠 Steps to Run in Google Colab Step 1: Correct the Error in GDP_2023.xlsx Open the file in Excel or LibreOffice. Rename the first column from "ountry" to "Country". Save and re-upload. Step 2: Upload Files to Google Colab Open https://colab.research.google.com/ Select File > Upload notebook or create a new one. Upload all four .xlsx files via the file panel or using: python CopiarEditar from google.colab import files uploaded = files.upload() import pandas as pdimport numpy as npimport statsmodels.api as sm # Load dataepi_df = pd.read_excel('EF_EPI_2024_with_Legal_Origin_Common_Law_Dummy.xlsx')gdp_df = pd.read_excel('GDP_2023.xlsx')ipri_df = pd.read_excel('IPRI_Country_Tables_Manual.xlsx') # Standardize and rename country columnsepi_df['Country'] = epi_df['Country'].str.upper()gdp_df = gdp_df.rename(columns={'ountry': 'Country'}) # corrects typo in original column namegdp_df['Country'] = gdp_df['Country'].str.upper()ipri_df['Country'] = ipri_df['COUNTRY'].str.upper() # Subset relevant IPRI columnsipri_df = ipri_df[['Country', 'Intellectual Property Rights (IPR)']] # Merge datasetsmerged_df = epi_df.merge(gdp_df, on='Country', how='inner').merge(ipri_df, on='Country', how='inner')print("Merged rows:", merged_df.shape) # Create new variablesmerged_df['Log_GDP'] = np.log(merged_df['GDP'])merged_df['Interaction'] = merged_df['Common_Law'] * merged_df['English_Lingua_Franca'] # Define dependent variabley = merged_df['Intellectual Property Rights (IPR)'] # Model 1: without interactionX1 = merged_df[['Common_Law', 'English_Lingua_Franca', 'Log_GDP', 'EF EPI Score']]X1 = sm.add_constant(X1) # Model 2: with interactionX2 = merged_df[['Common_Law', 'English_Lingua_Franca', 'Interaction', 'Log_GDP', 'EF EPI Score']]X2 = sm.add_constant(X2) # Fit OLS models with robust standard errors (HC3)model1 = sm.OLS(y, X1).fit(cov_type='HC3')model2 = sm.OLS(y, X2).fit(cov_type='HC3') # Print resultsprint("\n=== Model 1 Results ===")print(model1.summary()) print("\n=== Model 2 Results ===")print(model2.summary())