The Washington State Merged Longitudinal Administrative Database

Abstract

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.

In response to demands on public systems to do more, do better, and cost less, the value of integrated administrative data systems (IDS) for social policy is increasing (Fantuzzo & Culhane, 2016). This is particularly relevant in programming for young children where services are historically fragmented, disconnected from systems serving school-aged children, and siloed among health, human services, and education agencies. Guided by the vision that Iowa’s early childhood system will be effectively and efficiently coordinated to support healthy families, we are developing an early childhood IDS to address this disconnection and facilitate relevant and actionable social policy research.

Iowa’s IDS is a state-university partnership that acknowledges the need for agencies to retain control of their data while enabling it to be integrated across systems for social policy research. The innovative governance model deliberately incorporates procedures for stakeholder engagement at critical tension points between executive leaders, program managers, researchers, and practitioners. Standing committees (Governance Board, Data Stewardship, and Core team) authorize and implement the work of the IDS, while ad-hoc committees are solicited for specific projects to advise and translate research into practice.

This paper will articulate the Iowa IDS governance model that was informed by means tested principles articulated by the Actionable Intelligence for Social Policy Network. It will include our collaborative development process; articulated mission and principles that guided discussions about legal authorization, governance, and use cases; and the establishment of governance committees to implement our vision for ethical and efficient use of administrative data for social policy.