Acknowledgements

JUNE 2025

We want to thank the DigiHum 100 team at UC Berkeley for providing us with the DH tools to create and complete this project.

We would also like to extend a special thanks to Scott Caddy for being available throughout our process and providing further insight on how we can merge the world of the humanities and economics .

We are grateful for the open access databases and to the authors of the papers that we utilized for our project.

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OECD

Thanks to the OECD for their databases and providing information about income inequality in the United States.

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Yahoo Finance

Thanks to Yahoo Finance for their open data on S&P 500 index.

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Statista Research Department

Thanks to the Statista Research Department for their data and visalizations on median income for the upper, middle, and lower class.

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United States Census Bureau

Thanks to the U.S. Census Bureau for surveying rates of poverty and being open access.

This project used Python libraries, Pandas, Seaborn, and Matplotlib, for data cleaning and visualization. The members of our project team are Data Science majors or minors. The classes provided at UC Berkeley for Data Science primarily use Pandas and Python in classrooms. Because of our familiarity with these libraries, we felt comfortable using them in this project. This is most evident in the code provided on the Data Visualizations page.

We filtered out all countries besides the United States in these datasets as that was our area of interest. The data we used varied from 1960 to 2023. These years include important economic crises in the United States so we believed this timeline would display any important pattern for observation.

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