240 Public Administration Review • March | April 2018
Public Administration Review,
Vol. 78, Iss. 2, pp. 240–250. © 2017 by
The American Society for Public Administration.
State Agencies’ Use of Administrative
Data for Improved Practice:
Needs, Challenges, and Opportunities
Colleen Schlecht is researcher at Chapin Hall
at the University of Chicago. Her work includes
qualitative research and quantitative analysis of
programs and supports for vulnerable youth, from
early childhood to emerging adults, and their
families. She holds a master’s degree in public policy
from the University of Chicago and a bachelor’s
degree in public policy studies from Duke University.
Emily R. Wiegand is researcher at Chapin
Hall at the University of Chicago, specializing in
data manipulation and management, database
development, quantitative analysis, and the use of
administrative data to guide strategic planning and
decision-making in the nonprofit and public sectors.
She has a master’s degree in public policy and a
bachelor’s degree in history, both from University
Scott W. Allard is professor in the Evans School
of Public Policy and Governance at the University of
Washington. He is a nonresident senior fellow in the
Brookings Institution’s Metropolitan Policy Program
and codirector of the Family Self-Sufficiency Data
Center at the University of Chicago.
Abstract : Growing interest in the use of administrative data to answer questions around program implementation
and effectiveness has led to greater discussion of how government agencies can develop the necessary internal data
infrastructure, analytic capacity, and office culture. However, there is a need for more systematic research into how
states find different pathways and strategies to build administrative data capacity. Drawing on interviews with almost
100 human service agency staff and their data partners, the authors examine the realities of administrative data
use. They summarize the experiences of data users in order to address two main challenges : limited analytic capacity
and challenges to linking or sharing data resources. The article concludes by examining a range of approaches that
government agencies take to improve data quality and capacity to analyze that data .
Evidence for Practice
• Improving data quality tends to involve intentional but incremental efforts to enhance the accuracy of data
entry, reduce the prevalence of missing data, and create detailed data documentation.
• High-capacity administrative data users allocate staff time to reflect on questions or metrics that extend
beyond mandatory reports and will have immediate benefits to the agency and its clients.
• Agencies making consistent commitments to analytic activity outside of mandatory reporting do so by
prioritizing and protecting staff time for such activities and developing data infrastructures that make data
flexibly available for analytic work.
• Integrating analytics into program planning and design often requires intentional partnerships with external
researchers, agencies in other states, charitable philanthropy, and federal agencies.
• States seeking to become field-leading learners build and share comprehensive data resources, participate in
creative discussions about data analytics and program design, and support the learning of data users.
A lthough researchers and government
agencies have been using administrative
data to understand program dynamics for
several decades, there is growing interest in the use
of administrative data to answer questions around
program implementation and effectiveness across
a range of social policies. A number of factors have
increased the role that administrative data play in
policy-driven research today: more accessible and
powerful data analysis tools, a growing emphasis
on evidence-based policy, the use of data-driven
decision-making tools in program management,
and data science or “big data” initiatives intended to
improve data resources and capacity (see Isett, Head,
and VanLandingham 2016 ; Lavertu 2016 ; Mergel,
Rethemeyer, and Isett 2016 ). Momentum behind
greater administrative data use also builds on recent
work to better understand social policy dynamics
(Einav and Levin 2013 ; Johnson, Massey, and O’Hara
2014 ), the consequences of welfare reform (Cancian
et al. 2002 ; Lee, Mackey-Bilaver, and Goerge 2003 ;
Ribar, Edelhoch, and Liu 2016 ), the impact of
subsidized child care (Weber, Grobe, and Davis
2014 ), and the relationship between neighborhood
characteristics and employment (Chetty, Friedman,
and Saez 2013 ).
Nevertheless, common limitations of administrative
data can hinder their contribution to evidence-
based policy decision-making processes (Hotz
et al. 1998 ). These limitations generally fall into
two main groups: limits on valid inference and
data use obstacles. The validity of inference from
administrative data can be compromised for a variety
of reasons (Greer and Bullock 2017 ; Lavertu 2016 ;
Mergel, Rethemeyer, and Isett 2016 ). Data systems
not initially designed for performance measurement
may not yield administrative data containing accurate
information about key performance or outcome goals.
Information located in administrative data files may
not be checked regularly for accuracy or consistency.
Sample restrictions also exist because administrative
Scott W. Allard
University of Washington
Emily R. Wiegand
Chapin Hall at the University of Chicago
A. Rupa Datta
NORC at the University of Chicago
Robert M. Goerge
Chapin Hall at the University of Chicago
Mathematica Policy Research
A. Rupa Datta is vice president and senior fellow
at NORC at the University of Chicago, where she leads
major studies that combine surveys with administrative
and commercial data sources to support policy analysis
in human services such as employment supports, early
care and education, and poverty alleviation. She liaises
with state administrators as part of the Family Self-
Sufficiency Data Center at the University of Chicago.
Robert M. Goerge is senior research fellow at
Chapin Hall at the University of Chicago. He is also a
senior fellow at the Harris School of Public Policy and
the University of Chicago Computation Institute and
codirector of the Family Self-Sufficiency Data Center
at the University of Chicago.
Elizabeth Weigensberg is senior researcher
at Mathematica Policy Research. She has more than
15years of experience conducting evaluations and
research in the fields of workforce development and child
welfare for federal, state, and local government agencies
and foundations. Her expertise includes linking and
analyzing complex administrative data from state and
local public agencies and providing technical assistance
to develop performance measures and to facilitate the
use of data to inform policy and practice.