· Public charity
River Cities United Way
River Cities United Way works with local agencies to advance the common good by creation opportunities for a good life in Mohave and LaPaz Counties AZ.
What you funded, over time
Every grant placed by its stated purpose and the recipient’s mission, by year — across FY2017–2019.
Where the money goes
Your grants by size, and where they go.
The 10 grants below total $100,745 — the rows itemised in this filing. The $136,912 headline is the total grant expense reported on the return, so the remaining $36,167 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 · FY2018
- Under $10k7 grants · $50k
- $10k–50k3 grants · $51k
| Recipient | Amount |
|---|---|
| Interagency Council | $26,656 |
| Kingman Aid to Abused | $13,326 |
| Saint Mary's Food Bank | $10,829 |
| LHC Meals on wheels | $9,721 |
| Community Legal Svs | $8,632 |
| Boys & Girls Club of Kingman | $8,001 |
| AZ Board of Regents NAU | $7,105 |
| Kingman Area Food Bank | $5,735 |
| Cornerstone Mission | $5,506 |
| Salvation Army-KGM | $5,234 |
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 (FY17–18, $38k) land where the poverty rate runs at 17%, against an area that typically sits at 9%. 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.
10 repeat relationships — 10 still active in FY2018, 0 since wound down.
How the two cohorts compare
Organizations
Total granted
Still filing today
New vs renewed · share of each year
In FY2018, 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
- KAKINGMAN AID TO ABUSED PEOPLE2× · 2017–2018 · $27k · revenue +74%
- CLCOMMUNITY LEGAL SERVICES2× · 2017–2018 · $17k · revenue +57%
- TCTHE CLUB FOR YOUTH2× · 2017–2018 · $16k · revenue +516%
Funded once
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 10 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 tightly interlocked camp — most of these funders back each other's grantees too.
Open a dossier: St Mary's Food Bank Alliance · Kingman Healthcare Inc · American Woodmark Foundation Inc · Arizona Community Foundation · 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 River Cities United Way 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 River Cities United Way?
Find your warmest path to River Cities United Way 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.