How States Are Graded
Our methodology for assigning letter grades to state foster care systems — data sources, metric definitions, and grading scale.
Primary Grade — Reunification Rate
Each state receives an A–F grade based primarily on its reunification rate: the percentage of children who exit foster care by returning to their birth family or primary caregiver.
| Grade | Reunification Rate | Interpretation |
|---|---|---|
| A | 55% or higher | Excellent — well above national average (~50%) |
| B | 45% to 54.9% | Good — near national average |
| C | 35% to 44.9% | Below average — room for improvement |
| D | 25% to 34.9% | Significantly below average |
| F | Below 25% | Well below average — significant concern |
All 8 Tracked Metrics
In addition to the primary grade, we track 8 performance dimensions for each state:
Reunification Rate
% of exits to reunification with family
Adoption Rate
% of exits to adoption
Aging Out Rate
% of exits aging out at 18+ without permanency
Median Length of Stay
Median months a child spends in foster care
Re-entry Rate
% re-entering foster care within 12 months of exit
Placement Stability
% of children with 2 or fewer placement moves
Maltreatment Recurrence
% with repeat maltreatment within 6 months of first report
Timely Permanency
% achieving permanency within 12 months of entry
Data Sources
All state performance data comes from federal sources:
- ACF AFCARS — Adoption and Foster Care Analysis and Reporting System, administered by the Administration for Children and Families (ACF). States submit data annually. We use the most recent AFCARS report (FY2022).
- HHS NCANDS — National Child Abuse and Neglect Data System for maltreatment data
- U.S. Census ACS — For child population estimates to calculate per-capita rates
Grade-band thresholds at a glance
The PlainFoster grading system uses a transparent letter-grade rubric based on a state's reunification rate relative to the national distribution. Knowing the underlying thresholds is critical for interpreting why two states with seemingly similar rates can land in different grade bands — the spread between B and C is tighter than the spread between D and F, reflecting how outcomes cluster around the national median.
| Grade | Reunification rate band | Approx. % of states |
|---|---|---|
| A | ≥ 50% | ~15% |
| B | 42% to 49.9% | ~30% |
| C | 35% to 41.9% | ~30% |
| D | 28% to 34.9% | ~15% |
| F | below 28% | ~10% |
Reunification rate bands defined by PlainFoster from the AFCARS national distribution. State percentages approximated from the most recent AFCARS report; recompiled each release cycle.
Limitations
This grading system has important limitations to understand:
- A single metric (reunification rate) cannot fully capture the complexity of a state's child welfare system
- States have different definitions, data systems, and reporting practices, which can affect comparability
- Higher maltreatment report rates may reflect better detection systems, not worse safety outcomes
- Geographic and demographic differences between states affect all outcomes
- This is a simplified educational tool, not an official federal assessment
Frequently Asked Questions
Why is reunification rate the primary grading factor?
Reunification with birth family is the preferred permanency outcome under federal law, and it's the most universal metric available across all states. A high reunification rate generally indicates effective family support services, reasonable caseloads, and a system focused on keeping families together when safe.
Does a lower grade mean a state is negligent?
No. Grades reflect outcomes measured by available federal metrics, not the effort or dedication of child welfare workers. States with lower grades may face structural challenges: underfunding, high caseloads, rural service gaps, or higher rates of substance use disorders in their communities.
Are there other federal grading systems?
Yes. The federal Child and Family Services Reviews (CFSRs) assess states on safety, permanency, and well-being outcomes. States that don't meet federal standards must complete Program Improvement Plans. Our grading system draws on the same AFCARS data but uses a simplified approach for public clarity.
Why doesn't the grade include maltreatment data?
Maltreatment data from NCANDS is presented separately because it measures a different dimension — child safety — and its reporting varies significantly by state definitions and practices. A state with more reports may actually have better detection systems, not worse child safety.
Understanding the Data
The information presented throughout this guide is informed by publicly available public records published by federal and state government agencies. Our database aggregates and standardizes these records to make them more accessible and easier to interpret for general audiences. When we reference specific statistics or trends, they are drawn directly from these authoritative sources unless explicitly noted otherwise.
It is important to understand the limitations of any large-scale data dataset. Records may contain errors from the original data collection process, some fields may be incomplete for older entries, and classification systems may have changed over time. Our analysis accounts for these factors by clearly labeling data vintage, flagging records with missing critical fields, and noting when temporal comparisons span methodology changes in the source data.
For readers who want to conduct their own research, we recommend going directly to the source whenever possible. federal and state government agencies provides detailed documentation on collection methodology, sampling frames, and known data quality issues. Our goal is not to replace primary sources but to make them more approachable and to highlight patterns that may not be immediately obvious when browsing raw records.
How We Analyze Data Records
Our analytical approach involves several steps designed to surface meaningful insights from large datasets. First, we clean and standardize the raw data, handling variations in naming conventions, date formats, and categorical labels. Then we compute summary statistics, distributions, and comparative benchmarks across relevant dimensions such as geography, time period, and category type.
Key metrics we examine include statistical records, geographic distributions, temporal trends. These indicators provide a multi-dimensional view of each entity in our database, allowing users to understand not just individual records but how they compare to peers, regional averages, and national benchmarks. We believe this contextual approach is far more valuable than presenting raw numbers in isolation.