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· Private foundation

Posey Fdn Tuw

Its FY2025 filing reports that it accepted unsolicited grant applications.

$19k
Granted FY2025still arriving
4
Grants FY2025still arriving
3
States reached
$5k
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, FY2019 to 2025.

Education$64kArts & Culture$40k
02FY2025 · 4 grants

Where the money goes

Grants by size, and the largest recipients.

$5,000
Median grant
3
States reached
$302k
Total assets
Largest grants
RecipientAmount
SCHOOL OF THE ART INST OF CHICAGO$5,000
MARYLAND INST COLL OF ART$5,000
NEW YORK ACADEMY OF ART FBO$5,000
NEW YORK ACADEMY OF ART$4,000
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.

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

2 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. 2 of the 17 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)
0
Early backer (in before they grew)
2/2
Grantees still filing
1/2
Grew since first funded

Where the money sits, by cause and then by grantee

NEW YORK ACADEMY OF ART: $26,000 · OtherNEW YORK ACADEMY OF ARTDIVYANGI SHUKLA CO ART INSTITUTE OF: $6,000 · OtherDIVYANGI SHUKLA CO ART INSTITUTE OFSOFIYA KUZMINA CO NEW YORK ACADEMY OF: $6,000 · OtherSOFIYA KUZMINA CO NEW YORK ACADEMY OFFIDELIS JOSEPH CO CRANBROOK ACADEMY OF: $6,000 · OtherFIDELIS JOSEPH CO CRANBROOK ACADEMY OFNEW YORK ACADEMY OF ART FBO COLIN DAWSON: $5,000 · OtherSHUO CAI CO SCHOOL OF THE ART INSTITUTE: $5,000 · OtherMARYLAND INST COLL OF ART: $5,000 · OtherADRIANA VIGNOLI CO SCHOOL OF THE ART: $5,000 · OtherSUNI VIZCARRA WOOD CO SCHOOL OF THE ART: $5,000 · OtherJED SMITH CO NEW YORK ACADEMY OF ART: $4,500 · OtherSANTIAGO GALEAS CO NEW YORK ACADEMY OF: $4,500 · OtherHEATHER MCLEOD CO NEW YORK ACADEMY: $4,500 · OtherPRATT INSTITUTE FBO CLAIRE HEIDINGER: $4,000 · OtherLAUREN FAULKNER CO NEW YORK ACADEMY: $4,000 · OtherART INST OF CHICAGO FBO SUNI VIZCARRA: $4,000 · OtherMOLLY LAY CO UNIVERSITY OF ALABAMA: $4,000 · OtherTHE ART INSTITUTE OF CHICAGO: $5,000 · Education
Other$98,500Education$5,000

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

25%50%75%100%$10M$100Mgrantee revenue →↑ funder's share of their budgetNEW YORK ACADEMY OF ART: $26,000 over 2y, 0.2% of budgetTHE ART INSTITUTE OF CHICAGO: $5,000 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.

  • Who funds NEW YORK ACADEMY OF ART ↗
  • Who funds THE ART INSTITUTE OF CHICAGO ↗

How much its grantees rely on government

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

2025
’20’21’22’23’24’25
no gov · 00%0%1%3%5%funder's share of their income ↑0%3%5%8%10%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 · 0–10%
    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 17 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 2025, released 2025. Filings run roughly 12 to 24 months behind; figures are dated accordingly.

    Source object · view filing

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