The Cultural Resource Curse: How Trade Dependence Undermines Creative Industries

This dataset accompanies the study The Cultural Resource Curse: How Trade Dependence Undermines Creative Industries. It contains country-year panel data for 2000–2023 covering both OECD economies and the ten largest Latin American countries by land area. Variables include GDP per capita (constant PPP, USD), trade openness, internet penetration, education indicators, cultural exports per capita, and executive constraints from the Polity V dataset. The dataset supports a comparative analysis of how economic structure, institutional quality, and infrastructure shape cultural export performance across development contexts. Within-country fixed effects models show that trade openness constrains cultural exports in OECD economies but has no measurable effect in resource-dependent Latin America. In contrast, strong executive constraints benefit cultural industries in advanced economies while constraining them in extraction-oriented systems. The results provide empirical evidence for a two-stage development framework in which colonial extraction legacies create distinct constraints on creative industry growth. All variables are harmonized to ISO3 country codes and aligned on a common panel structure. The dataset is fully reproducible using the included Jupyter notebooks (OECD.ipynb, LATAM+OECD.ipynb, cervantes.ipynb). Contents: GDPPC.csv — GDP per capita series from the World Bank. explanatory.csv — Trade openness, internet penetration, and education indicators. culture_exports.csv — UNESCO cultural export data. p5v2018.csv — Polity V institutional indicators. Jupyter notebooks for data processing and replication. Potential uses: Comparative political economy, cultural economics, institutional development, and resource curse research. How to Run This Dataset and Code in Google Colab These steps reproduce the OECD vs. Latin America analyses from the paper using the provided CSVs and notebooks. 1) Open Colab and set up Go to https://colab.research.google.com Click File → New notebook. (Optional) If your files are in Google Drive, mount it: python CopiarEditar from google.colab import drive drive.mount('/content/drive') 2) Get the data files into Colab You have two easy options: A. Upload the 4 CSVs + notebooks directly In the left sidebar, click the folder icon → Upload. Upload: GDPPC.csv, explanatory.csv, culture_exports.csv, p5v2018.csv, and any .ipynb you want to run. B. Use Google Drive Put those files in a Drive folder. After mounting Drive, refer to them with paths like /content/drive/MyDrive/your_folder/GDPPC.csv.