· Public charity
Ronald McDonald House Charities of the Bay Area
Make contributions and grants to organizations which engage in educating children, providing for the essential needs of children or carrying on research as to diseases and other disorders of children, or the diagnosis, prevention, and treatment thereof.
Where the money goes
Your grants by size, and where they go.
The 4 grants below total $375,197 — the rows itemised in this filing. The $762,956 headline is the total grant expense reported on the return, so the remaining $387,759 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 · FY2017
- $10k–50k2 grants · $82k
- $50k–250k2 grants · $294k
| Recipient | Amount |
|---|---|
| RONALD MCDONALD HOUSE OF SAN FRANCISCO | $146,788 |
| RONALD MCDONALD HOUSE AT STANFORD | $146,787 |
| JOHN MUIR FOUNDATION CARE MOBILE | $41,772 |
| VMC FOUNDATION | $39,850 |
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 (FY17–17) land where the poverty rate runs at 9%, against an area that typically sits at 7%. 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.
4 grantees tracked through their own filings, 2017–2024.
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 2017–2024, not grant rows in a single year — so this will not match the grant count on the cover.
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 Ronald McDonald House Charities of the Bay Area 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.
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How do I get to Ronald McDonald House Charities of the Bay Area?
Find your warmest path to Ronald McDonald House Charities of the Bay Area 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.