This summer, I had the opportunity to work with the Office of the Under Secretary for Economic Affairs at the U.S. Department of Commerce. I contributed to a variety of projects across the bureau, including a study on drivers of domestic migration, research on distressed communities for the House Regional Leadership Council, and creating software tools for economic analysis.

However, the two main projects I had ownership of were a machine learning-based classification of metropolitan economies and an economic case study of Charlotte, NC. In my machine learning project, I classified major metropolitan areas based on economic and demographic makeup, which demonstrated regional economic similarities as well as delineated regional differences between distressed portions of the country. In particular, it highlighted the depth of the economic gap between a few large, high-income areas and other American cities. My case study project aimed to further investigate that gap through the lens of a metro we found as an outlier: Charlotte. I conducted stakeholder interviews, performed original economic analyses, and compiled relevant historical events to create a holistic picture of Charlotte’s economic development from a national perspective. Through my work, I investigated the relationship between Charlotte’s economy and the nation as well as regional economic inequality within Charlotte itself.

In doing this, I learned a lot about the nation’s economic makeup and the nature of economic distress across the country, technology’s role in improving the efficacy of government action, and how to use the levers of federal economic policy. I’m incredibly grateful to have had the opportunity to contribute to this work at the Department of Commerce and to collaborate with our partners across government, and I hope to continue exploring how to use technology to better our economy for everyone.