Research › Findings
The systems that fund restoration in Washington — federal awards, state grants, tribal programs — each keep their own records. None sees the full picture. These findings describe what emerges when those records are assembled: structural patterns that are invisible from inside any single system but visible from the point where they converge — the desk of the organization carrying them all.
Each verified against the dataset.
94% of restoration organizations have funded commitments spanning two or more data systems
Nearly all organizations in the benchmark (94%) have funded commitments from two or more distinct data sources — USASpending.gov, RCO PRISM, and RCO ArcGIS. Their full portfolio is not visible in any single system.
The structural picture that matters — where timelines collide, where match commitments compound, where capacity gets stretched — assembles only when you hold all the pieces simultaneously. Each source is accurate on its own. None of them can see the interactions.
Based on 50 organizations. Sources: USASpending.gov, RCO PRISM, RCO ArcGIS.
58% of restoration organizations have deadlines clustering in the same seasonal windows
29 of 50 organizations (58%) show end-date clustering as a structural conflict type — the most prevalent conflict pattern in the dataset. This is driven by species-specific in-water work windows, typically July through September in western Washington.
Organizations do not choose this compression. The seasonal calendar imposes it. What the data adds is how that clustering interacts with other funded commitments — match commitments due in the same window, federal reports on a different cycle, state deliverables stacking underneath.
Based on 50 organizations. End-date clustering requires 3+ active funded commitments with converging deadlines.
Grant count explains less than 20% of the variation in portfolio complexity
Active grant count explains just 9% of the variation in portfolio complexity across 50 organizations. More grants does not automatically mean more structural friction.
Complexity comes from how grants interact — timeline overlaps, match burden compounding, cross-source convergence — not from how many there are. An organization with 5 grants on conflicting timelines carries more structural pressure than one managing 20 that do not touch each other.
Based on 50 organizations. R² = 0.089 between active grant count and portfolio complexity score.
Match burden runs at roughly 15 percent of active funding at the population median
Across the benchmark of 50 Pacific Northwest restoration organizations, the population-level match-burden ratio sits at approximately 15 percent under the canonical PCS-factor definition. For every $1 of active primary award funding at the median entity, organizations carry roughly $0.15 of additional cost-share commitment. The distribution is wide: at the 25th percentile the ratio is roughly 1.5 percent; at the 75th percentile roughly 47 percent. This heterogeneity reflects the diversity of cost-share structures across entity types and funding programs rather than a single sector-wide pattern.
Match burden is a structural feature of the funding model rather than a measure of organizational quality. State and federal programs deliberately attach match requirements to expand the public-investment leverage on each grant dollar; the burden of meeting those requirements lands on the recipient organization’s books and time.
Match-burden ratios concentrate by entity type. Organizations whose program shape requires acquisition or capital projects — land trusts particularly — tend to carry higher active match commitments than organizations focused on operations or technical assistance. The cohort-by-cohort distribution shows up in the Sector Diagnostic; the population median reported here is the cross-sector reference point.
Methodology: Match-burden ratio is the canonical engine definition: sum of match dollars divided by sum of primary award dollars, both computed over active obligations only (the canonical factor match_burden_ratio, shown in diagnostic reports as “Cost-Sharing Ratio”). Computed from RCO PRISM and federal-grant data within the 50-organization benchmark; one entity has no active primary funding and is excluded from the ratio (n = 49 for the population median). Subgroup medians (e.g., land trusts) are reported in the Sector Diagnostic where cohort context is preserved. Figures verified against zone3 benchmark on April 28, 2026.
Curious what your portfolio looks like?
Freehold assembles the structural picture that no single funding source can see.
Also: Sector Watch · Funding Exposure