The Washington State Merged Longitudinal Administrative Database

• Main Authors: Jennifer Romich, Mark Long, Scott Allard, Anne Althauser
• Format: Article
• Language: English
• Published: Swansea University 2018-10-01
• Series: International Journal of Population Data Science
• Online Access: https://ijpds.org/article/view/1066

This paper describes a uniquely comprehensive database constructed from merged state administrative data.  State Unemployment Insurance (UI) systems provide an important source of data for understanding employment effects of policy interventions but have also lack several key types of information: personal demographics, non-earnings income, and household associations.  With UI data, researchers can show overall earnings or employment trends or policy impacts, but cannot distinguish whether these trends or impacts differ by race or gender, how they affect families and children, or whether total income or other measure of well-being change. This paper describes a uniquely comprehensive new administrative dataset, the Washington Merged Longitudinal Administrative Database (WMLAD), created by University of Washington researchers to examine distributional and household economic effects of the Seattle $15 minimum wage ordinance, an intervention that more than doubled the federal minimum wage.  WMLAD augments UI data with state administrative voter, licensing, social service, income transfer, and vital statistics records. The union set of all individuals who appear in any of these agency datasets will provide a near-census of state residents and will augment UI records with information on age, sex, race/ethnicity, public assistance receipt, and household membership. In this paper, we describe 1.) our relationship with the Washington State Department of Social and Health Services that permits this data access and allows construction of this dataset using restricted personal identifiers; 2.) the merging and construction process, including imputing race and ethnicity and constructing quasi-households from address co-location; and 3.) planned benchmarking and analysis work.