Investigating Alternative Sources of Quarterly Wage Data:

An Overview of the NDNH, LEHD, WRIS, and ADARE

Background on Wage Record Data

To assess employment and wage outcomes of workforce interventions, programs utilize employer tax records filed quarterly to match wage information against participant records.

The Unemployment Insurance (UI) quarterly wage records, therefore, have become the most reliable way to assess labor market outcomes of individuals in workforce and welfare programs.

Out-of-state UI wage records have been shared using with an inter-state agreement, the Wage Record Interchange System (WRIS) or its successor, the WRISII (WRIS2).

Sources of Alternative Wage Data

A January 2012 study by the Urban Institute provides a valuable overview of four national data sets that use state unemployment insurance quarterly wage data, some of which augment it with other administrative data.

The four national data sets are the U.S. Department of Health and Human Services' National Directory of New Hires (NDNH), the U.S. Census Bureau’s Longitudinal Employer-Household Dynamics (LEHD), the U.S. Department of Labor’s Wage Record Interchange System (WRIS), and the University of Baltimore’s Administrative Data Research and Evaluation (ADARE) project.

The report explains the origin and purpose of each data set, its strengths and limitations for research (the most important of which is that the data are typically retained for no longer than two years for each individual), examples of research using each data set, and practical advice on how to obtain access to the data. The bibliography includes numerous references that are helpful in assisting workforce professionals.

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Responsible Use of Administrative Data for Performance Accountability

In April 2004, the University of Baltimore prepared for the Employment and Training Administration (ETA) a report on the use of administrative data in the ADARE project piloted in nine states.

The study describes the value of linked administrative and shared welfare databases, and highlights the issues in creating data sharing agreements for new linkages with longitudinal state data systems.