Cross-Country Dataset on Women’s Education, Gender Development, and Economic Sustainability

Description

This dataset supports the study examining the relationship between women’s education and economic sustainability and the moderating role of the Gender Development Index (GDI) across countries. The repository provides the complete materials used for data preparation, analysis, validation, and visualization to enhance transparency, reproducibility, and future research use. The repository includes the original data files obtained from publicly available international databases, including the World Bank Gender Statistics Database, the International Monetary Fund Gender Equality Database, and the Sustainable Development Report 2025. It also contains the cleaned and integrated cross-country dataset developed by merging the relevant indicators and addressing data completeness and consistency. Women’s education is represented through a composite Education Index derived using Principal Component Analysis (PCA), while economic sustainability is operationalized using indicators related to SDG 8 (Decent Work and Economic Growth), SDG 9 (Industry, Innovation and Infrastructure), and SDG 10 (Reduced Inequalities). The dataset additionally includes the Gender Development Index, GDP per capita, urbanization, and alternative measures of sustainable development used for robustness analysis. The repository also provides the complete Python analysis code used for data processing, descriptive statistics, correlation analysis, multicollinearity assessment, hierarchical regression analysis, moderation testing, robustness analysis, and marginal effects estimation. The corresponding statistical outputs, including descriptive results, regression estimates, model comparisons, and robustness results, are included to facilitate verification and replication. All tables and publication-quality figures generated during the analysis, including the correlation heatmap, standardized coefficient plot, interaction visualization, and marginal effects plot, are also provided. Collectively, these materials enable researchers to reproduce the analytical procedures, validate the reported findings, conduct additional analyses, and extend research on women’s education, gender development, economic sustainability, and the Sustainable Development Goals.

Steps to reproduce

The study findings can be reproduced using the original data, cleaned dataset, Python code, outputs, tables, and figures provided in this repository. Download the files: Download all repository files and retain the existing folder structure. Set up Python: Install Python 3.10 or a compatible later version. The analysis can be executed in Jupyter Notebook, JupyterLab, or another Python environment. Install the required packages: pip install pandas numpy openpyxl scipy scikit-learn statsmodels matplotlib seaborn Review the original data: The source files were obtained from the World Bank Gender Statistics Database, IMF Gender Equality Database, and Sustainable Development Report 2025. They are included to support transparency and data verification. Reproduce the cleaned dataset: Run the data-cleaning and integration code to select indicators, harmonize country identifiers, manage missing values, merge the datasets, and construct the final cross-country dataset. The women’s Education Index is generated using Principal Component Analysis (PCA), while sustainability measures are calculated using the relevant SDG indicators. Update the file paths: Modify the input and output paths according to the file locations on your computer: FILE = r"C:\Your_Folder\CrossCountry_Sustainability_Dataset.xlsx" OUTPUT = r"C:\Your_Folder\Analysis_Results.xlsx" Run the analysis: Execute the Python code sequentially. The analysis includes descriptive statistics, correlations, Variance Inflation Factors (VIF), hierarchical ordinary least squares regression with HC3 robust standard errors, moderation analysis, and robustness tests. Estimate the models: Model 1 includes log-transformed GDP per capita and urbanization as controls. Model 2 adds women’s education and the Gender Development Index (GDI). Model 3 adds the interaction between the mean-centered Education Index and GDI to test moderation. Run the robustness analysis: Use Economic Sustainability Plus and the Overall SDG Index as alternative dependent variables and compare the findings with the primary model. Generate and verify outputs: Run the visualization code to reproduce the correlation heatmap, coefficient plot, interaction plot, and marginal effects plot. Compare the generated statistics, regression results, tables, and figures with the reference outputs. Minor numerical differences may occur because of variations in Python or package versions. Export results: Statistical outputs are exported to Excel files, while figures are saved as high-resolution image files in the specified output folder.

Institutions

• Alliance University

  Karnataka, Bengaluru

Categories

Licence