Now Accepting Applications: Summer 2022 Data Challenge on Equity and Underserved Communities
DOL’s Chief Evaluation Office (CEO) is pleased to welcome applications to DOL’s second annual Summer Data Equity Challenge competition for emerging and established scholars. Up to five winning teams will receive $30,000 each to use data to analyze how federal labor policies, protections and programs reach traditionally underserved communities, starting in June 2022. Applications are due at 12:00 p.m. EDT, April 11, 2022, and decisions are expected in May. CEO has contracted with Manhattan Strategy Group to support the 2022 Summer Data Challenge.
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 use 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 - $30,000 each to complete analyses between June and November 2021.
Please note that these reports were produced outside of CEO's independent evaluation and research process. Please see the individual reports for more information on how these products were developed.
See papers from last year’s winners below:
Awardees and Final Reports and Datasets:
Dr. Alex Bell, Dr. Till Von Wachter, Roozbeh Moghadam, TJ Hedin, and Geoffrey Schnorr, from the California Policy Lab at the University of California, used 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 made 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.
Dr. Eliza Forsythe and Mr. Hesong Yang, from the School of Labor and Employment Relations at the University of Illinois, used 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 identified reasons for disparities in UI benefit recipiency between demographic groups, including race, ethnicity, age, education, sexual orientation, and disability.
Dr. Kelly Jones and Farah Tasneem, from American University, used 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 explored 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 employed 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.
Sarah Garcia, from The University of Minnesota, used 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.
Dr. Rebecca Johnson and Yuchuan Ma from Dartmouth College are partnered 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 used 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.