Skip to content

· Private foundation

Peter Lau 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.

$605k
Granted FY2024still arriving
2
Grants FY2024still arriving
2
States reached
$585k
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, FY2023 to 2024.

Education$585kScience & Tech$200kHealth$20k
02FY2024 · 2 grants

Where the money goes

Grants by size, and the largest recipients.

By grant size · FY2024

  • $10k–50k1 grant · $20k
  • $250k+1 grant · $585k
$585,000
Median grant
2
States reached
$580k
Total assets
Largest grants
RecipientAmount
DIGITAL HARBOR FOUNDATION INC$585,000
REGENTS OF THE UNIVERSITY OF MICHIGAN$20,000
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:

Dollar for dollar, its grants (FY23–24) land where the poverty rate runs at 18%, against an area that typically sits at 15%. 98% of its dollars go to grantees based in above-average-need neighborhoods. Most of its grants land in higher-need ZIPs.

area typical 15%NYC TECH UNITED INC: $200k → 17%REGENTS OF THE UNIVERSITY OF MICHIGAN: $20k → 14%DIGITAL HARBOR FOUNDATION INC: $585k → 19%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

1 grantees tracked through their own filings, 2017–2024.

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 2024, not grant rows in a single year, so this will not match the grant count on the cover. 1 of the 3 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)
1/1
Grantees still filing
0/1
Grew since first funded

Where the money sits, by cause and then by grantee

DIGITAL HARBOR FOUNDATION: $585,000 · EducationDIGITAL HARBOR FOUNDATIONNYC TECH UNITED INC: $200,166 · OtherNYC TECH UNITED INCREGENTS OF THE UNIVERSITY OF MICHIGAN: $20,000 · OtherREGENTS OF THE UNIVERSITY OF M…
Education$585,000Other$220,166

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 budgetDIGITAL HARBOR FOUNDATION: $585,000 over 1y, 1.0% 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 DIGITAL HARBOR FOUNDATION ↗

How much its grantees rely on government

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

2024
’20’21’22’23’24
no gov · 00%0%1%3%5%funder's share of their income ↑0%25%50%75%100%share of the org’s income from government →median 2%
  • Digital Harbor Foundation: 2% of income from government
no gov moneyreceives it· size = income
0get no government money at all
0rely on government for over half their income
typical government reliance, FY20–24
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 3 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

Play a game based on Peter Lau’s filings →

More from the funding graph