Introduction to Information Analysis

In the outcome of the project, I discovered varying patterns in the number of representation cases, union success rates, and wages. Specifically, a decreasing number representation cases after the pandemic and an increase in union success rates despite lower voter turnout. Black and Hispanic female workers consistently earned lower nonunion wages, while Hispanic female workers benefited from higher relative union wage premiums. These findings highlight how union activities and wage patterns varied across groups, suggesting differences in how worker protections and benefits were realized during the pandemic. 

As I explored the union data, I learned that flexibility is important when building a data science project. I initially planned to examine wage disparities across public and private sectors as well as demographic groups, but as I delved into the data, I realized that focusing on demographic wage trends provided a more meaningful and deep analysis. I adapted my approach mid-project and was able to come up with recommendations to union leaders and policymakers for each of my research questions, which can be seen in my report. I hope that my findings highlight the need for adjustments in the way that unions support individuals in harsh economic times, specifically within demographic groups that already face systemic discrimination.

View the final project report here.

This project deepened my understanding of how union activity relates to wages and economic trends, especially during crises like the COVID-19 pandemic. It strengthened my analytical skills and showed how data can challenge misconceptions—like the belief that unions harm businesses. The insights gained can inform better labor policy and support a more equitable, resilient economy.

View the final project report here.