· Public charity
Suffolk County Association of Municipal Employees Inc
The organization safeguards its members by negotiating and enforcing collective bargaining agreements that ensure reasonable compensation, job protection, and advocacy for a safe and healthy workplace to best serve suffolk county.
What you funded, over time
Every grant clustered by its grantee’s IRS cause code (NTEE), by year — across FY2021–2024.
Where the money goes
Your grants by size, and where they go.
The 3 grants below total $48,923 — the rows itemised in this filing. The $432,132 headline is the total grant expense reported on the return, so the remaining $383,209 is giving the schedule does not break out: grants under the $5,000 itemisation floor, grants to individuals, and grants reported on other schedules. Every figure below describes the itemised rows only.
By grant size · FY2024
- Under $10k1 grant · $5k
- $10k–50k2 grants · $44k
| Recipient | Amount |
|---|---|
| SUFFOLK AME CARES FOUNDATION INC | $33,777 |
| Individual grant recipient | $10,000 |
| SUFFOLK COMMUNITY COLLEGE FOUNDATION INC | $5,146 |
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.
Your human-services grants (FY21–22, $22k) land where the poverty rate runs at 14%, against an area that typically sits at 5%. 100% of those 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 +40% since the first grant, against +21% for the ones you funded once.
3 repeat relationships — 2 still active in FY2024, 1 since wound down; 1 grantees were first funded in FY2024 (too recent to call).
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 FY2024, 80% of grant dollars renewed an existing relationship; $10k 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
- SASUFFOLK AME CARES FOUNDATION INC2× · 2023–2024 · $141k · revenue +40% · 39% of their budget
- NSNJ STATE PBA SURVIVOR & WELFARE FUND2× · 2021–2022 · $22k · revenue -35%
- SCSUFFOLK COMMUNITY COLLEGE FOUNDATIONINC3× · 2021–2024 · $19k · revenue +111%
Funded once
- FAFIREMEN'S ASSOCIATION OF THE STATE OF NEW YORKone grant, 2022 · $8k · revenue +21%
- LILONG ISLAND FEDERATION OF LABOR AFL-CIOone grant, 2022 · $5k · revenue +16%
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 6 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.
Government reliance of your grantees
Every dot is one organization Suffolk County Association of Municipal Employees Inc 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 Suffolk County Association of Municipal Employees Inc?
Find your warmest path to Suffolk County Association of Municipal Employees Inc 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.