· Private foundation
The Eleanor Kagi Foundation A Lynn M Legacy
Its FY2025 filing reports that it accepted unsolicited grant applications.
What you funded, over time
Every grant clustered by its grantee’s IRS cause code (NTEE), by year — across FY2017–2025.
Where the money goes
Your grants by size, and where they go.
By grant size · FY2025
- $10k–50k1 grant · $10k
- $50k–250k3 grants · $342k
- $250k+1 grant · $711k
| Recipient | Amount |
|---|---|
| UNLV Foundation | $711,465 |
| UNLV Foundation | $141,500 |
| Heaven Can Wait Animal Society | $100,000 |
| St Rose Dominican Health Foundation | $100,000 |
| The Animal Foundation | $10,000 |
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 13%, against an area that typically sits at 10%. 88% 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.
5 repeat relationships — 2 still active in FY2025, 3 since wound down; 2 grantees were first funded in FY2025 (too recent to call).
How the two cohorts compare
Organizations
Total granted
Still filing today
New vs renewed · share of each year
In FY2025, 90% of grant dollars renewed an existing relationship; $110k 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
- UOUNIVERSITY OF NEVADA LAS VEGAS FOUNDATION9× · 2017–2025 · $3.6M · revenue +18%
- HCHEAVEN CAN WAIT ANIMAL SOCIETY5× · 2020–2025 · $500k · revenue +147%
- NPNEVADA POLICY RESEARCH INSTITUTE7× · 2017–2024 · $381k · revenue +75%
Funded once
- DEDr Eugene Kirshbaum Founationone grant, 2017 · $100k
- IGIndividual grant recipientone grant, 2017 · $100k
- HCHeaven Can Wait Societyone grant, 2019 · $75k
Repeat = funded in two or more distinct years; growth and survival are read from each grantee’s own subsequent IRS filings.
7 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. 7 of the 13 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.
- Who funds UNIVERSITY OF NEVADA LAS VEGAS FOUNDATION ↗
- Who funds HEAVEN CAN WAIT ANIMAL SOCIETY ↗
- Who funds NEVADA POLICY RESEARCH INSTITUTE ↗
- Who funds PINE POINT SCHOOL INC ↗
- Who funds ST ROSE DOMINICAN HEALTH FOUNDATION ↗
- Who funds EAGLE HILL FOUNDATION OF MASSACHUSETTSINC ↗
- Who funds THE ANIMAL FOUNDATION ↗
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 tightly interlocked camp — most of these funders back each other's grantees too.
Open a dossier: The Bank of America Charitable Foundation Inc · American Online Giving Foundation Inc · Fidelity Investments Charitable Gift Fund · Donor Advised Charitable Giving Inc · 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 The Eleanor Kagi Foundation A Lynn M Legacy 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 The Eleanor Kagi Foundation A Lynn M Legacy?
Find your warmest path to The Eleanor Kagi Foundation A Lynn M Legacy 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.