A Global Analysis on Gender, Employment, and School Enrollment

Project Overview

This data analysis project examines gender disparities in education and employment outcomes across country-level income categories using the World Bank Development Indicators.

Tools

Python | Numpy | Pandas | Matplotlib | Seaborn

Process

1. Developed an actionable, specific research question
2. Fetched and merged World Bank indicator and income classification datasets.
3. Filtered and reshaped data using melting, aggregation, and conditional exclusion based on missingness thresholds.
4. Created multi-year line plots and boxplots to visualize trends across gender and income groups.
5. Developed a comphrensive report describing the resulting relationship between gender, school enrollment, labor-force participation and economic level, applicable for further research and informing policy on education and employment.

Results

The relationship between primary school enrollment and labor force participation varies by income level, with higher enrollment linked to higher unemployment for upper-middle and high-income groups for both males and females. The relationship between enrollment and labor force participation is negative for most female groups and neutral to positive for most male groups. School enrollment generally has a stronger correlation with labor force participation for females than males, regardless of direction. These trends highlight the complex interaction between education, labor markets, and economic development across different income groups.

These trends highlight the complex interaction between education, labor markets, and economic development across different income groups. It is essential to note that these findings do not imply causation; primary school enrollment data pertains to much younger populations than those represented in labor force and unemployment statistics.

Key Learnings

1. Gained proficiency in transforming and visualizing complex, multi-dimensional global datasets.
2. Developed sensitivity to missingness and data consistency issues in international data collection.
3. Learned to operationalize broad research questions into structured exploratory analysis pipelines.

Collaborators

Maia Kennedy | Courtney Chen

UC Berkeley, MIDS

November 2024