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Data-Driven Support for Labor Equity

March 31, 2022

Elizabeth Shackney L’24, MUSA’24 co-authors a report on improving equity in the federal oversight of employers who hire migrant workers on H-2A visas.

Elizabeth Shackney L’24, MUSA’24 recently co-authored “Can We Use Local Outreach to Improve Equity in Federal Oversight? A Case Study with the H-2A Visa Program,” a report for the Chief Evaluation Office of the U.S. Department of Labor (DOL). The report is based on the findings of a team of researchers co-led by Shackney that had received a grant for the DOL 2021 Summer Data Challenge on Equity and Underserved Communities.

Shackney’s team built on research she conducted on oversight for H-2A agricultural employers as the Director of Analytics and Research at Texas RioGrande Legal Aid (TRLA). Other team members included Dr. Rebecca A. Johnson from Dartmouth College, supervising a team of Dartmouth undergraduate students — Yuchuan Ma, You-Chi Liu, Grant Anapolle, and Cameron Guage — as well as Cassie Davis, also of TRLA.

In March 2021, the Chief Evaluation Office held the Department of Labor’s first Summer Data Equity Challenge competition for scholars to analyze how federal labor policies, protections, and programs reach communities underserved due to race, gender identity, sexual orientation, ethnicity, income, geography, immigrant status, veteran status, and disability status, among others. Scholars were tasked with using public and restricted data to illuminate meaningful gaps in knowledge and to propose practical solutions to fill those gaps.

Teams comprised of both established and emerging researchers received awards of $10,000 to $30,000 each to complete analyses between June and November 2021.

Shackney’s team used various data to study how new computational methods, namely supervised machine learning and natural language processing, can be used to improve equity in the DOL Wage and Hour Division (WHD) enforcement process for employers who hire migrant workers on H-2A visas. The team used both standard characteristics of H-2A job postings (e.g., industry codes) and characteristics created using spatial data and natural language processing of the raw job contracts to predict different enforcement actions, and whether potential violations were discovered by WHD, the local outreach organization, or both entities.

The project builds upon Shackney’s partnership while at TRLA with Dr. Johnson’s data science class in Dartmouth’s Program in Quantitative Social Science, where Shackney served as the lead data science liaison for a Social Impact Practicum.

“I previously worked with TRLA’s farmworker team, wrangling data to use to monitor migrant farmworker employers and conduct outreach to employees,” said Shackney. “Professor Johnson was teaching a data analytics course at Dartmouth and was looking for community partners; we decided to have students analyze the H-2A dataset and merge it with other indicators. The project evolved into a proposal for the Challenge, and our team focused on enforcement procedures within the Wage and Hour division of DOL. It was helpful to see how a federal agency can incorporate data and research into their plans for developing more equitable practices. I also enjoyed having the opportunity to investigate some of the hypotheses TRLA’s on-the-ground advocates had about employer features that may indicate Wage and Hour violations down the line.”

Shackney, who is also pursuing a master’s degree in Urban Spatial Analytics at the Weitzman School of Design, is originally from Pittsburgh and holds an undergraduate degree in government from Wesleyan University. At the University of Pennsylvania Carey Law School, she is involved with the Penn Housing Rights Project, Walk-In Legal Assistance Project, Student Public Interest Network, and Penn Law National Lawyers Guild.

This summer, she will be working with Community Legal Services in Philadelphia, and she plans to work in direct legal services upon graduation, aiming to also weave in her spatial data analytics skillset.

Read more inspiring student pathways to the profession.