Building Workforce Indicators Using Unemployment Insurance Data

• In Event: Data in Action: Real-World Case Studies and Partnerships Driving Policy Change

Abstract

Unemployment Insurance (UI) systems provide essential financial support to workers who have lost their jobs. These systems also collect and maintain a robust repository of data that can be leveraged by researchers and decision-makers. This paper asks the question: how can administrative data from the unemployment insurance system be used to analyze workforce outcomes and support agency operations? Researchers from NORC at the University of Chicago have recently partnered with the Illinois Department of Employment Security (IDES) to explore the answer by developing a series of economic indicators using UI system data. These indicators can be used to better monitor, evaluate, and forecast workforce trends and outcomes and have the potential to be expanded across state lines in the future.

Workforce outcome measures derived from UI data are powerful because they are highly flexible and generalizable. They can be applied to any group of program recipients who have earned UI-covered wages, including those in job training and other safety net programs. Because they can be built on datasets that states are already producing for administrative and statistical purposes, it is possible to streamline data access and integration across states. This versatility makes these measures highly valuable for evaluating a broad range of programs and initiatives.

This paper focuses on building indicators using two UI-system datasets: quarterly wages, which include wages paid by each employer to each employee in a quarter; and UI claims, which include records of filings and weeks claimed. Measures sourced from both datasets already appear in several places within the federal statistical system. However, existing reporting tends to focus on broad geographies over relatively long periods of time and often lacks the granularity that state agencies need to inform day-to-day decision making.

NORC has partnered with IDES in a multiple year effort to develop new economic indicators using UI data and enhance existing uses for these data in agency research, operations, and reporting. This effort has made UI data more accessible to stakeholders and enabled more detailed analyses tailored to specific local labor markets. The process involved storing several years of wage and claim data on a cloud-based data platform, developing and documenting a longitudinal data model, defining and building outcome measures relative to this model, and storing the results in a format that is easily accessible for reporting, research, and ad hoc querying alike. NORC and IDES have used this structure to build several UI and reemployment outcome measures including earnings and reemployment to previous industries for job training recipients and average duration and first payment time-lapse for UI claimants. This paper discusses lessons learned through the process of building these measures and the potential for expanding this work in the future through interstate partnership.

Author

• David G McQuown, NORC at the University of Chicago
  Presenting Author