Weekly Wonk: Spring '26 Wonk Intelligence Quarterly
Strategic guidance, plus FLOTUS on the Hill for foster care
From the Founder’s Desk
Our field is navigating volatile terrain with incomplete maps.
That’s the problem our premium Wonk Intelligence Quarterly is built to solve.
This is forward guidance and what to do about it for leaders making real decisions under live conditions of constraint, not analysis for its own sake.
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Our latest WonkCast was our first AMA — your questions on navigating policy and political reality, decisions under constraints, and what child welfare is actually for.
Let’s get into it.
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Weekly Wonk Deep Dive
Weekly Wonk Deep Dive
We Don’t Know Ourselves:
Child Welfare’s Knowledge Crisis
How solvable data, analysis, and measurement constraints limit policy decision quality.
By: Laura Radel, Senior Contributor
Every year, hundreds of thousands of children enter the child welfare system.
Caseworkers make decisions that will shape the rest of their lives: whether to remove a child, where to place them, when to reunite a family.
Those decisions depend on what the field knows.Yet the field does not know enough.
Decades of research, federal investment, and policy reform have not resolved the field’s most basic questions: what works, for whom, and under what conditions.
Many of the same debates that dominated child welfare thirty years ago still dominate it today.
At the heart of the field’s paralysis is a three-part knowledge problem:
A data infrastructure problem. Our core data sets do not tell us what we need to know and are in many aspects unreliable.
An interpretation problem. We do not make good use of the data we have and frequently misinterpret its signals.
An invisibility problem. Important subpopulations are excluded from our data, and inadequately reflected in our program and policy decisions as a result.
The Data Infrastructure Problem
The child welfare system’s national data derives primarily from three administrative systems:
The National Child Abuse and Neglect Data System (NCANDS) collects child-level data on every maltreatment report.
The Adoption and Foster Care Analysis and Reporting System (AFCARS) covers every child in foster care or placed for adoption by a public child welfare agency.
The National Youth in Transition Database (NYTD) surveys foster youth at 17 and follows a subset at 19 and 21.
NCANDS and AFCARS have been collecting data since the 1990s; NYTD since 2010.
These systems were built primarily to record measurable events: maltreatment investigations, foster care placements, and certain attributes of the events and the children and families involved.
They are most reliable for documenting what happened.
They are considerably less reliable for explaining why – the circumstances, decisions, and family factors that drove those events.
In addition, despite the Administration for Children and Families’ standardization efforts, reporting differences across states persist, and data are unavailable until more than a year after the period they describe.
AFCARS was updated in 2020, but many new or revised fields have not yet been reported publicly, and promised longitudinal functionality has not yet been fully realized.
These consequences show up in specific ways.
Neglect Definitions
Most children come to the attention of the child welfare system because of neglect. Yet state definitions of neglect vary widely, and circumstances deemed neglect may be very different from one another, calling for different responses.
Maltreatment Deaths
NCANDS captures only a portion of child deaths related to maltreatment, limiting our ability to analyze patterns and prevent future harm to other children.
Re-Entry
Tracking re-entry to foster care, particularly from adoption and guardianship placements, has been a perennial problem, hampering efforts to support children and families through post-permanency challenges.
Family Circumstances
Across both NCANDS and AFCARS, fields relating to the problems that led to the maltreatment report or foster care placement are used inconsistently by states.
For instance, with respect to substance use as a reason a child is removed and enters foster care, AFCARS estimates range from under 10 percent to over 60 percent.
Knowledgeable professionals do not believe this variation primarily represents actual differences in prevalence of that magnitude, but rather differences in reporting practices.
The Interpretation Problem
Unreliable data is only part of the problem. The field also misreads what it has.
Three patterns stand out.
National Averages Mask State-Level Variation
National-level analysis frequently obscures trends that differ substantially among states.
A line that may look flat nationally may obscure big gains in some places and losses in others. Or a statistic that may look small nationally may be large in some places.
For instance, TANF spending represents 8 percent of states’ federal child welfare funding nationally, but in three states it was more than half of such funding, while in other states it was zero.
Similarly, knowing that about half of terminations of parental rights occur within the Adoption and Safe Families Act timeline masks the fact that states ranged from 16 percent to nearly 90 percent.
Recognizing the range of variation can point to insights about the policy and practice drivers of differences and the differential impacts of policy changes for states.
For advocates, funders, and other national organizations who care about federal policy, these insights also point to explanatory factors driving the small-p politics policy lives by.
Published Reports Make Trend Analysis Difficult
Published analyses such as the annual AFCARS, Child Maltreatment and Child Welfare Outcomes Reports make it difficult to track progress on measures over time at the state level.
For instance, in the new AFCARS Dashboard, one can view a state’s figures for only a single year at a time and only a few national trends (like the total number of children in foster care) can be viewed over a several year period.
Looking at just a few years at a time we also overlook long-term trends, such as the transformation of title IV-E from what began primarily as a foster care program but now consists largely of children receiving adoption subsidies.
A lot of work goes into these reports, they sit atop a gold mine of data, and yet paradoxically their format makes inevitable a simplification that obscures signal.
Cross-Sectional Analysis Has Dominated Where Longitudinal Analysis is Needed
What matters for policy is a stable picture of trends over time. Longitudinal analysis provides that, but we have very little of it.
It’s much harder to make sustainable policy decisions with point-in-time data. That’s what we’re drowning in, since most of our collections are cross-sectional analyses.
Until the updated AFCARS 2020 (which began reporting data for 2023), tracking children longitudinally in federal foster care data was difficult.
Cross-sectional analysis has significant limitations, and as far back as 2004 the Pew Commission on Children in Foster Care recommended outcomes be tracked using longitudinal rather than cross-sectional data.
Effective financing and policy decisions rely on an understanding of the most common experience, with guardrails to manage the edge cases.
Longitudinal analysis looks at a system’s flow, better capturing the trendline experience of the typical child, which is key for shaping policy.
Cross-sectional analysis looks at a system’s stock, what’s present right now.
That means cross-sectional analysis over-represents children in foster care for long periods, distorting our picture of the typical experience.
Conversely, it also obscures what we understand about exits from care; exit cohorts in such data are weighted to children who have short lengths of stay.
The field is only now beginning to have the data infrastructure to do it. It will also need interpretive infrastructure to make meaning from that and shape policy and financing.
The Invisibility Problem
Unreliable data and misinterpretation are problems with what the field collects.
The invisibility problem is different.
Entire populations and system actors are simply absent from the data, so policy decisions impacting them are made without them, leading to policy drift.
Four blind spots stand out.
Children Screened Out Before Investigation
NCANDS does not collect information on children whose maltreatment reports are screened out prior to investigation. This is not a small population, or an irrelevant one.
These 2 million children annually represent about half of all children referred to child protective services agencies each year.
Research shows that many families who are screened out are very much like those classified as higher risk, and many will be reported again.
Children in “Hidden” Foster Care
Many children – estimated at between 100,000 and 300,000 children per year – are diverted from formal foster care, typically to relative caregivers who may not receive financial or social support.
In addition, reunification services are generally not provided to parents in these circumstances.
These children are very like others who are formally placed in relative foster care but are invisible in the data of states that use this practice.
Foster care rates are declining. Does a shift to diverted “hidden” foster care explain part of that story? We can’t answer that question because we don’t have data on it.
Courts’ Roles
Child welfare outcomes depend not only on agencies’ actions, but also on the timeliness and quality of court decisions.
Yet little information on courts’ activities is available in federal child welfare data, despite their central role to decisions on placements and much more.
That means we’re lacking insight into a critical dependence that shapes accountability and oversight in child welfare financing and policy.
Foster Parents
While some information is collected about foster parents, this information is insufficient to understand the movement of children in and out of foster homes, or to calculate measures such as the Home for Every Child ratio that is currently an ACF priority.
At minimum, the collection of an identifier for the foster home would be necessary to track how many foster homes are active during the reporting period, how many children spent time in the home, and the patterns of children’s movement in and out of the home.
Consequences and Resolution
The lack of reliable data, the misinterpretation of the data we do collect, and the omission of some populations combine to prevent an adequate understanding of the activities and outcomes of the child welfare system.
The data are not well-suited for ensuring accountability or continuous improvement.
The data infrastructure problem is a federal responsibility.
ACF controls the data systems, sets the definitions, and determines what gets reported.
Leaders with federal relationships — whether through advocacy, oversight, or technical assistance — could emphasize accelerated implementation of AFCARS 2020’s longitudinal capacity and for expanding what the systems are designed to capture.
The architecture of federal data shapes what every state can know.
The interpretation problem is solvable now, with existing data.
State leaders and their philanthropic partners do not need to wait for better federal systems to begin analyzing trends longitudinally, disaggregating national findings, and interrogating what their own data actually shows.
The gap here is analytical capacity and will, not data availability. That’s a large reason for Child Welfare Wonk’s creation and approach.
The invisibility problem requires deliberate investment.
Screened-out families, hidden foster care, court behavior – none of these will become visible through administrative data reform alone.
Federal and state leaders will need to expand what the systems are designed to capture.
Philanthropic funders can support the research needed to document these populations in the interim, but the long-term solution is structural, not supplemental.
None of these are academic problems. They’re solvable constraints on the quality of policy decisions and how we track and refine them over time.
Leaders who understand that have a clearer sense of where to push.
Wonkatizer
Bipartisan Older Youth Roundtable Features FLOTUS
What Happened
First Lady Melania Trump made a rare Capitol Hill appearance Wednesday for a bipartisan roundtable with House Ways and Means Committee members and young people who have experienced foster care.
Discussions anchored to six legislative proposals that would modify the Chafee program for older youth, emphasizing housing, education, and workforce preparation.
While cost neutral, the proposals would expand what the program can cover and how it supports young people.
Why it Matters
A FLOTUS appearance like this is anything but pro forma. It generates momentum in an otherwise complex political environment.
Even without new funding or significant structural policy reforms, that momentum offers an opening to cultivate new champions and create conditions for future change.
What to Watch
Whether a consolidated legislative package comes together that can garner momentum. Watch for signals of negotiation and potential markup timing.
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That’s it for this week, Wonks!
Z
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