· Private foundation
Stephen Edward Scarff Memorial Foundation
Its FY2025 filing reports that it funds preselected organizations and did not take unsolicited requests; check the foundation's own site before ruling it out.
What you funded, over time
By grantee IRS cause code (NTEE).
A cause breakdown isn’t shown here: 71% of STEPHEN EDWARD SCARFF MEMORIAL FOUNDATION’s grantee dollars fall outside its nine largest cause areas, whether because the recipient carries no IRS cause code or because its cause sits in the long tail. A chart would be mostly one residual band and misrepresent the portfolio.
Where the money goes
Your grants by size, and where they go.
| Recipient | Amount |
|---|---|
| CHICAGON LAKE PRESERVATION FUND | $5,000 |
Do your dollars go where the need is?
Each grant placed by the hardship in its grantee’s ZIP, then read against the area’s typical level.
Dollar for dollar, your grants (FY17–25) land where the poverty rate runs at 11%, against an area that typically sits at 6%. 100% of your dollars go to grantees based in above-average-need neighborhoods. Most of your grants land in higher-need ZIPs.
Matched: human-services grants are those whose grantee’s IRS cause code is Human Services. Placed by people below the poverty linein the grantee’s ZIP, averaged from the tracts in that ZIP; grantee ZIP can differ from where services are delivered.
US grants placed by each grantee’s ZIP, which can differ from where services are actually delivered. Need from public data — U.S. Census/ACS, CDC PLACES, Eviction Lab, USDA — shown as context about the area, never attributed to your giving.
Who you back again
Which organizations you funded more than once, and which you funded a single time. Each grantee is matched to its own filings.
And the relationships you keep tend to grow: grantees you re-up on have seen median revenue of +59% since the first grant, against -27% for the ones you funded once.
6 repeat relationships — 1 still active in FY2025, 5 since wound down.
How the two cohorts compare
Organizations
Total granted
Median revenue growth · since first grant
Still filing today
New vs renewed · share of each year
In FY2025, 100% of grant dollars renewed an existing relationship; $0 went to new ones.
Where new relationships form · theme of each grantee’s first grant
First-time = a grantee’s first year in your filing window; renewed = funded in an earlier year too. As a portfolio matures the renewed share naturally climbs — once funded, an org stays “renewable” — so the signal is the years that buck it (a new-grantee intake wave). The earliest year is left-censored: relationships that predate the data read as “new.” Theme is the grantee’s IRS cause.
Backed again, and grew
- CLCHICAGON LAKE PRESERVATION FUND9× · 2017–2025 · $99k
- MFMUSIC FOR MINORS INC3× · 2017–2021 · $10k · revenue +59%
- FOFRIENDS OF CAMP BATAWAGAMA3× · 2017–2022 · $2k · revenue -66%
Funded once
- HSHUMANE SOCIETY SILICON VALLEYgraduatedone grant, 2017 · $25k · revenue +55%
- LGLena Grochowska Foundationone grant, 2023 · $15k
- FFiolione grant, 2018 · $10k
Repeat = funded in two or more distinct years; growth and survival are read from each grantee’s own subsequent IRS filings.
5 grantees tracked through their own filings, 2017–2025.
Each one resolved to its own IRS returns and tracked year by year — your grant beside their revenue from every source. Association, dated; never a causal claim.
Counted here: distinct organizations you funded across 2017–2025, not grant rows in a single year — so this will not match the grant count on the cover. 5 of the 17 grantees resolved in that span have returns of their own we could reconcile; the rest are funded organizations whose filings we could not track year by year.
Where your money sits — by cause, then by grantee
Each org by its size and your share of it — top-left is where you’re load-bearing
Go grantee by grantee — a decade per org, and how each moved after you funded them
A decade per grantee — revenue shaded, your grants as bars, all rows on one timeline.
The grantmakers whose grantees overlap with yours far more than size alone predicts. Each orbits closer the stronger the alignment; the arcs between them show where they also fund each other. Here it reads as a set of overlaps that mostly run through you, not between each other.
Open a dossier: Amazonsmile Foundation
Affinity is a Gamma-Poisson posterior co-funding rate, re-centered on the typical rate, so thin evidence shrinks toward no signal. A research starting point: overlap is association, not proof of shared intent.
Government reliance of your grantees
Every dot is one organization Stephen Edward Scarff Memorial Foundation funds. Left–right is the share of its income from government; up–down is the share from you. Bigger dots raise more. Filter to federal or a single department — and drag the year to watch it move.
Government income is each org’s traced federal awards (USASpending — grants and contracts) plus state payments (open checkbooks) as a share of its total revenue (IRS Form 990); the self-reported government-grant line (990 line 1e) is carried for cross-check: a grantee that reports government grants we could not trace to a source is left off the chart rather than shown as receiving none. An association, not a claim that your grant caused the public funding. Coverage is precision-first — a floor, not a census; state records exist for 9 states, so a grantee outside them shows no state figure (dimmed) rather than a false zero. Federal award amounts are obligations, which can span years, so a single-year share is indicative. 990 filings lag 12–24 months.
Warm introductions · Powered by PlinthPlus
How do I get to Stephen Edward Scarff Memorial Foundation?
Find your warmest path to Stephen Edward Scarff Memorial Foundation through trustees and officers whose names appear on both boards. Search for your organization and Plinth traces the shortest route it can evidence.
Each link is a name appearing on two organizations’ public IRS 990 filings, matched on that name and, where the filings support it, on location. A same-state match has geographic support, which is a second matching feature rather than confirmation that the two are one person; a cross-state one is the likeliest to be two people who share a name. Every hop shows its tier, low-confidence and distant paths are held back rather than guessed, and it is worth confirming the person before you use the introduction.