Eunice Liu ’23 and Helen Ma ’23 say the opportunity to co-author a report for the U.S. Department of Labor has inspired them to think about careers that integrate data science and social science.

They are working with Rebecca Ann Johnson, an assistant professor of Quantitative Social Science, Cameron Guage ’22, and recent graduate Grant Anapolle ’21, compiling and analyzing data for the Department of Labor on how well U.S. employers are protecting the health and safety of temporary guest workers under the H-2 visa program.

“After this, I feel like data science is something that I want to pursue in the future, either going to grad school or working in the field,” Ma says. “Also, the topic of this project, which is about immigration and visas, is personally related to me because I’m an international student studying in the States.”

The team will be expanding on work developed in Johnson’s quantitative social sciences class “Modern Statistical Computing” to produce a report in the fall for the Department of Labor titled “Leveraging Data Science and Machine Learning to Improve Equity in Oversight of H-2 Employers.” Funding was provided by a grant from the federal agency’s Summer Data Challenge on Equity and Underserved Communities.

“Our goal is to use machine learning and modeling to help the Department of Labor figure out a way that they can more efficiently detect employers that are more likely to mistreat employees who they hire as part of the H-2A agricultural guest worker program,” Ma says. “The people who hold H-2A visas are temporary migrants and often minorities, so they tend to be more vulnerable.”

Johnson says the project will work with databases shared by Texas RioGrande Legal Aid (TRLA), which collects intake records that reflect guest worker issues in Texas, Arkansas, Alabama, Mississippi, Louisiana, Kentucky, and Tennessee. The students are also working with data from the Department of Labor on its own enforcement activities and data on the spatial contexts surrounding employers.

Johnson’s “Modern Statistical Computing” class originally made the connection with TRLA with support from Dartmouth’s Social Impact Practicum program. Run by the Center for Social Impact, the practicums connect undergraduate courses with community needs identified by nonprofit organizations.

“There’s starting to be a big national community around data science for social good and using data science to improve equity and legal enforcement,” Johnson says.

Johnson and the students will complete their work and present their findings to a panel of experts in the Department of Labor’s Chief Evaluation Office in Washington, D.C., in October 2021.