Socio-Economic Factors in Global Migration

The Complex Inquiry

The objective was to move beyond simple statistics and use a multi-variable dataset to statistically confirm or reject common hypotheses about migration (e.g., is unemployment a stronger driver than political stability?). This required a disciplined approach to handle complex, often highly correlated, features.

The Statistical Solution

I employed Python-based statistical tools to analyze and communicate complex relationships in the data.

• Correlation Matrix: Generated a matrix and heatmap to identify multicollinearity between potential driving factors (e.g., GDP vs. Education Spend).
• Regional Subsetting: Focused the analysis on specific regions (like Sub-Saharan Africa) to provide targeted, meaningful insights instead of generic global observations.
• Hypothesis Refinement: Used visualization (scatter plots, box plots) to test and refine initial hypotheses, ensuring conclusions were backed by statistical evidence.

Key Findings & Data Story

The analysis provided a clear narrative: **Environmental factors, while often secondary, showed the sharpest correlation increase** with migration intent over the last decade. This conclusion provided a new, actionable dimension for humanitarian and economic policy discussion.