Team Member Bios

Acknowledgment

We would like to express our gratitude to the U.S. Department of Labor’s Women’s Bureau for providing the annual data on labor force participation rates for the year 2022. This comprehensive dataset has been instrumental in our analysis, allowing us to explore various aspects of employment trends among different demographic groups. The availability of such high-quality data has enabled us to conduct a thorough and insightful analysis, which we hope will contribute to the ongoing discourse on gender disparities in the labor force.

Data Processing

In our analysis, we made several key technical and data-related decisions to ensure the accuracy and relevance of our findings.

Data Cleaning and Preparation

Initially, our dataset contained some extraneous and duplicated information, particularly projections for 2032, which we decided to remove to maintain focus on the most current and relevant data for 2022. We also separated marital status data from children’s age-related data to prevent confounding variables from affecting our analysis.

Data Analysis

We used Python for our data analysis and visualization tasks. Python was chosen over other programming languages such as R due to its robust libraries for data manipulation (Pandas) and visualization (Matplotlib, Seaborn). These libraries provided the flexibility and power needed to handle large datasets and create detailed, informative visualizations.

Visualizations

To effectively communicate our findings, we employed a variety of visualization techniques:

• Heatmaps: Used to show labor force participation rates by marital status and sex. This technique helped highlight the differences in employment rates among different groups.

• Radar Charts: Utilized to compare labor force participation rates across different levels of educational attainment and racial/ethnic groups. This approach allowed us to present multiple dimensions of data in a single, comprehensible graphic. 

• Bar Graphs: Employed to show the impact of child labor on GDP across different countries, illustrating the economic effects of child labor practices.