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 traditionally underserved communities. Populations of interest include those from communities traditionally underserved due to race, gender identity, sexual orientation, ethnicity, income, geography, immigrant status, veteran status and disability status, among others. Analyses use data to illuminate meaningful gaps in knowledge and, ideally, propose practical solutions to fill those gaps. The competition funded awards of $10,000 - $30,000 each to both established and emerging researchers to complete analyses between June and October 2021.


1. Community Level Disparities in Access to Unemployment Insurance During the Covid-19 Pandemic

Dr. Alex Bell, Dr. Till Von Wachter, Roozbeh Moghadam, TJ Hedin, and Geoffrey Schnorr, from the California Policy Lab at the University of California, will use ETA Tables, the CPS and the California Employment Development Department’s (EDD) Unemployment Insurance Claims File to study neighborhood, county, and state-level disparities in the receipt of Unemployment Insurance benefits during the COVID-19 pandemic. The research will make methodological contributions toward understanding how to interpret widely cited measures of Continuing Claims and Initial Claims during the pandemic. Regression analysis across US states will seek to shine light on the socioeconomic and policy determinants of UI access.

2. Understanding Disparities in Unemployment Insurance Recipiency

Dr. Eliza Forsythe and Mr. Hesong Yang, from the School of Labor and Employment Relations at the University of Illinois, will use the Current Population Survey (CPS), the Understanding America Covid-19 Survey and the Department of Labor’s Unemployment Insurance data to measure disparities in Unemployment Insurance eligibility, claiming behavior, and recipiency between demographic groups before and during the Covid-19 pandemic. By doing so, they will identify reasons for disparities in UI benefit recipiency between demographic groups, including race, ethnicity, age, education, sexual orientation, and disability.

3. The Role of Employer Thresholds in FMLA Eligibility and Impacts on Underserved Communities

Dr. Kelly Jones and Farah Tasneem, from American University, will use the CPS and the American Time Use Study (ATUS) to study the extent to which underserved communities are excluded from the Family and Medical Leave Act (FMLA) and the potential benefits to these communities of expanding eligibility. They will explore potentially underserved communities whose eligibility disparities have not been assessed, including LGBTQ populations, and those who are foreign‐born, non‐citizen, rural, or living below the poverty line. They will employ quasi-experimental methods examining state-based expansions of eligibility for family and medical leave entitlement to test the impact of reducing eligibility disparities on labor market outcomes.

4. Labor Market Polarization and Rising Disability

Sarah Garcia, from The University of Minnesota, will use the IPUMS National Health Interview Survey (NHIS), US Census Bureau's County Business Pattern data (CBP) and the University of Kentucky Center for Poverty Research National Welfare Data to study the rising rates of disability among working-age Americans. The first aim of this project is to examine whether rising disability is concentrated in counties experiencing labor market changes resulting from deindustrialization--or the decline of industrial capacity due to social and economic changes. Because states vary in the availability and generosity of social welfare that may ameliorate or exacerbate the relationship between labor market opportunities and disability, the second aim is to examine whether social welfare affects the strength of this relationship.

5. Leveraging Data Science and Machine Learning to Improve Equity in Oversight of H-2 Employers

Dr. Rebecca Johnson and Yuchuan Ma from Dartmouth College are partnering with Elizabeth Shackney and Cassie Davis from Texas RioGrande Legal Aid (TRLA) to use the Wage and Hour Division Compliance Action Data, ETA Quarterly Job Disclosures, OFLC debarment records, Scraped seasonal jobs data, and a private local dataset on H-2A-related intake calls to study how new computational methods – supervised machine learning and natural language processing – can be used to improve equity in the Department of Labor Wage and Hour Division (WHD) enforcement process for H2 guestworkers. The team will use text and spatial features of H-2A job postings to predict different enforcement actions: (1) local intake call but no WHD investigation, (2) local intake call and WHD investigation, (3) WHD investigation but no intake call. The project builds upon a Dartmouth Social Impact Practicum by Dartmouth College students in the Program in Quantitative Social Science.

Research Time Frame: June 1, 2021 - October, 2021

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