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· Private foundation

Peng Education and Welfare Foundation

Its FY2024 filing reports that it funds preselected organizations and did not take unsolicited requests; check the foundation's own site before ruling it out.

$1.4M
Granted FY2024still arriving
5
Grants FY2024still arriving
1
States reached
$1.2M
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, FY2022 to 2024.

Education$2.0MHealth$100kHuman Services$46kPhilanthropy$35k
02FY2024 · 5 grants

Where the money goes

Grants by size, and the largest recipients.

By grant size · FY2024

  • $10k–50k3 grants · $81k
  • $50k–250k1 grant · $100k
  • $250k+1 grant · $1.2M
$36,000
Median grant
1
States reached
$18M
Total assets
Largest grants
RecipientAmount
Tsinghua University$1,235,000
Theo for Nepal eye Clinic$100,000
Boyo Social Welfare Foundation$36,000
Andrew Charity$25,000
St Mary's Junior College$20,000
02The need
03

Where the grant dollars land

Across the US and around the world.

US giving: 2% of grant dollars ($46k). The 98% that went abroad ($2.1M) is mapped below.

Beyond the US: 98% of all-years giving ($2.1M)

2 countries, by the recipient’s country on the filing.

grant dollars sentlessmore
Taiwan$2.0M · 6 grants
Austria$100k · 1 grant
How this is measured
International giving is mapped by the recipient’s country from the same 990 filings; comparable need data isn’t available at that grain.
03Its field
04the grantee network

0 grantees tracked through their own filings, 2022–2024.

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

How this is measured

Counted here: distinct organizations funded across 2022 to 2024, not grant rows in a single year, so this will not match the grant count on the cover. 0 of the 5 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)
0/0
Grantees still filing
0/0
Grew since first funded

Where the money sits, by cause and then by grantee

Tsinghua University: $1,964,000 · OtherTsinghua UniversityTheo for Nepal eye Clinic: $100,000 · Other+3 more: $111,000 · Other
Other$2,175,000

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

25%50%75%100%grantee revenue →↑ funder's share of their 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.

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 5 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.

Source object · view filing

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