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· Private foundation

4j 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.

$19k
Granted FY2024still arriving
3
Grants FY2024still arriving
1
States reached
$16k
Largest
01What it funds
01100% classified

What it funded, over time

Every grant placed by its stated purpose and the recipient’s mission, by year, FY2020 to 2024.

Human Services$22kEducation$2kPhilanthropy$1kHealth$919Crime & Legal$108
02FY2024 · 3 grants

Where the money goes

Grants by size, and the largest recipients.

By grant size · FY2024

  • Under $10k2 grants · $3k
  • $10k–50k1 grant · $16k
$2,000
Median grant
1
States reached
$986
Total assets
Largest grants
RecipientAmount
RONALD MCDONALD HOUSE CHARITIES OF SOUTH DAKOTA INC$16,000
GROTON SCHOOL DISTRICT$2,000
SAFE HARBOR FOUNDATION$1,000
02The need
03

Does the money reach high-need areas?

Each grant placed by the hardship in its grantee’s ZIP, against the area’s typical level.

show:

Its human-services grants (FY24–24, $16k) land where the poverty rate runs at 9%, against an area that typically sits at 9%. 0% of those dollars go to grantees based in above-average-need neighborhoods. Its grants skew toward lower-need ZIPs.

area typical 9%RONALD MCDONALD HOUSE CHARITIES OF SOUTH DAKOTA INC: $16k → 9%0%20%40%50%more need →
grant to an above-average-need area below average· circle size = grant amount
How this is measured
Matched: human-services grants are those whose grantee’s IRS cause code is Human Services. Placed by people below the poverty line in the grantee’s ZIP, averaged from the tracts in that ZIP.
How this is measured
US grants placed by each grantee’s ZIP, which can differ from where services are delivered. Need from public data (U.S. Census/ACS, CDC PLACES, Eviction Lab, USDA), shown as context about the area, never attributed to the funder’s giving.
03Its field
04the grantee network

5 grantees tracked through their own filings, 2017–2025.

Each grant beside the grantee’s revenue from every source, year by year.

How this is measured

Counted here: distinct organizations funded across 2017 to 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.

Each grantee is resolved to its own IRS returns. Association, dated; never a causal claim.

0
Load-bearing (≥25% of a budget)
1
Early backer (in before they grew)
5/5
Grantees still filing
3/5
Grew since first funded

Where the money sits, by cause and then by grantee

RONALD MCDONALD HOUSE CHARITIES OF SOUTH DAKOTA INC: $16,000 · Human ServicesRONALD MCDONALD HOUSE CHARITIES OF SOUTH DAKOTA INCSLEEP IN HEAVENLY PEACE INC: $5,604 · Housing & ShelterSLEEP IN HEAVENLY PEACE …GROTON SCHOOL DISTRICT: $2,000 · OtherGROTON SCHOOL…MAKE-A-WISH FOUNDATION OF SOUTH DAKOTA & MONTANA: $919 · OtherMAKE-A-WISH F…Safe Harbor: $108 · OtherSafe Harbor Foundation: $1,000 · Philanthropy
Human Services$16,000Housing & Shelter$5,604Other$3,027Philanthropy$1,000

Each grantee by its size and the funder’s share of it; top left is load-bearing

25%50%75%100%$1.0M$10Mgrantee revenue →↑ funder's share of their budgetRONALD MCDONALD HOUSE CHARITIES OF SOUTH DAKOTA INC: $16,000 over 1y, 0.2% of budgetSLEEP IN HEAVENLY PEACE INC: $5,604 over 2y, 0.1% of budgetSafe Harbor Foundation: $1,000 over 1y, 1.0% of budgetMAKE-A-WISH FOUNDATION OF SOUTH DAKOTA & MONTANA: $919 over 1y, 0.1% of budgetSafe Harbor: $108 over 1y, 0.0% of budget
Grantee by grantee: a decade per organization, before and after funding

A decade per grantee: revenue shaded, grants as bars, all rows on one timeline.

Co-funders

Grantmakers whose grantees overlap with this funder’s far more than size alone predicts. Closer means stronger alignment; arcs show where they also fund each other. Here they form a tightly interlocked group: most of these funders back each other's grantees too.

South Dakota Community FoundationSD13.5× affinity4 shared granteesties to 2 of 2Hover any node to trace its alignments.Compare side by side →

Open a dossier: South Dakota Community Foundation · First Interstate BancSystem Foundation Inc · Amazonsmile Foundation

How this is measured
Affinity is a Gamma-Poisson posterior co-funding rate, re-centered on the typical rate, so thin evidence shrinks toward no signal. Overlap is association, not proof of shared intent.

How much its grantees rely on government

Each dot is one organization 4j Foundation funds: left to right is the share of its income from government, bottom to top the share from 4j Foundation. Bigger dots raise more.

2024
’20’21’22’23’24’25
no gov · 00%0%1%3%5%funder's share of their income ↑0%1%3%4%5%share of the org’s income from government →
    no gov moneyreceives it· size = income
    0get no government money at all
    1report government grants on their 990 we could not trace to a source (not plotted)
    0rely on government for over half their income
    ⤢ axis zoomed · 0–5%
    typical government reliance, FY20–25
    How this is measured
    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 a grant caused the public funding. Coverage is precision-first, a floor rather than 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 to 24 months.

    On method. Every financial figure here is read from IRS e-file XML, the funder’s own 990/990-PF, and from the multi-year returns of the 6 grantees we resolved, each linked to its source. Grantee outcomes are each organization’s own program-service reporting (Form 990, Part III), read as association with funding, never as caused by it. Low-confidence name matches are suppressed rather than guessed. How we build these →

    How this is measured
    Each figure carries one of six labels. Filed is what the funder reported on its return. Official is another government record about an organization, such as a federal award or a charity register, joined by name where no shared identifier exists. Resolved is an identity we worked out where the filing named a recipient without an EIN, kept only above a measured confidence threshold. Computed is arithmetic over those, like themes, portfolio clusters and co-funder strength. Context is a statistic about a place rather than about an organization, which is what the need overlay is: it describes the area a grantee’s address sits in, not where its work lands. Inferred is drawn by a model from text, like the partnerships read out of public news and organization websites. Each is labeled where it appears.

    Generated from the IRS Form 990-PF e-file return for fiscal year 2024, released 2024. Filings run roughly 12 to 24 months behind; figures are dated accordingly.

    Possibly out of date. A more recent filing (FY2025) is on record and reports no grant expense, so the figures above are from FY2024, the most recent year this funder made grants.

    Source object · view filing

    Play a game based on 4j’s filings →

    More from the funding graph